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Chapter 3 Chapter 4 Measu rements Principles and Practiceof Image Analysis . -- Automatic 8--'--" i" Microstructure and Materials . . Historical Development Automatic Image Analysis Overview Common Applications Microstructure and Materials In the renaissance period of western history, philosophers and alchemists began to speculate that differences in materials were related to their internal structure. These speculations were encouraged by such evidence as facets on the face of a coarse grained metal fracture or dendrites (tree-like structures) found in the sink head of a casting. It wasn't until the mid-1800s that Henry Sorby developed a successful method of revealing the microstructure of rocks and metals. The abilityto visually analyze the microstructure of materials was a powerful tool that enabled materials scientists and manufacturers to understand the behavior of the materials. All crystalline materials have a characteristic microstructure. The processes used to alter their properties usually affect the microstructure in some visible way. Therefore. the ability to analyze these changes is helpful in controlling manufacturing processes and failure analysis. Historical Development Image analysis is a technique for extracting quantitative data from images. usually with the objective to analyze some property of the specimen. When Henry Sorby visually examined his first successfully polished metal specimen, he analyzed it and described one of the microstructures as a "pearly structure". The name pearlite has since been applied to the eutectoid structure of iron-carbon alloys. Since photography was in its infancy and the microscope illumination was not adequate for the weak sensitivity of photographic materials. early microscopists drew sketches of the microstructures. In addition. visual estimates were made of how much pearlite. ferrite, or carbide was present. One of the earliest improvements in microstructural analysis was the comparison chart (fig 1-1). This is a simple chart that displays various percentages of black on white and white on black graphic. It is estimated that this reduces error from +/-20% to +/-10% accuracy. Chapter1 .. ~ ~"MUWUCIIT~ ." :.41 t . .. !-!J ~. n st ,.,. 10. '.'..'. '".' ,~"..%,~., ..-. "".-;,.,.-. "":.'.1.-." ,." . ... .. ..'~. _.;~~ ~."..\ ... . . -'..':-~',.,-, ... ...,.. - .w . .. . .",,'."'. .~.~,..':c," I ffi .",...:...", ~~!;~i~~~i! II»jl: ~ ~ MS Figure 1-1 Area percent comparison chart The development of an areal method of image analysis offered prospects of an improvement in accuracy. With this method, a 10.000 square transparent grid was secured to the face of an enlarged photomicrograph and each square that contained any part of the constituent to be analyzed was counted. Another attempt at improving image analysis was the linear measuring device. The commercial version was known as the Hurlbut Counter. This semi-automatic instrument consisted of a large console containing a motor that was connected to the microscope stage with a flexible shaft. The console had six analog counters; each controlled by its own push button. As the specimen was driven linearly across the microscope stage, the various constituents passed the cross hair of the ocular lens (Figure 1-2). To measure the percentage of any of up to six constituents, the operator pressed the appropriate button for as long as that constituent was under the cross hair of the ocular lens and the counter registered the linear distance of that constituent. At the end of the series of linear scans, the numbers for each constituent were recorded. This method was more rapid than the areal technique and offered improved accuracy. However there was a considerable demand for manual dexterity, and eye fatigue was a serious problem. The linear method was particularly unsuited to microstructure consisting of many constituents, particularly fine distributions. Although the measurement time was less than half of that required to perform areal analysis, the accuracy was the same or slightly less. ~Buehler Ltd. 1999 2 The Institute for Microstructural Analysis The greatest improvement in both speed and accuracy was achieved using the point count method as illustrated in Figure 1-3. It employed a 9, 16. or 25-point reticule grid mounted in one of the microscope oculars. In this method, the fraction of points that fell on the constituent being measured is equal to the volume fraction. The microscope is equipped with a special point counting microscope stage that has interchangeable x-y movement knobs that allow the specimen to be advanced by a selected uniform increment. When the knob is turned to each successive clock stop, the technician counts the number of times the constituent of interest falls under any grid intercept. This method may be applied to microscope images and photomicrographs. A hundred or more areas may be counted in an hour. leading to a far greater accuracy than the other manual methods. A more detailed description of quantitative techniques is in Chapter 2. Figure 1-3 The point count method and various grid configurations. During the 1960's, the development of video technology made possible the first video-based image analysis system. Although the early models were complex and expensive, they were the beginning of a technology that has developed rapidly to point that some form of automatic image analysis is available to virtually any user who has this need. In the 1980's, the development of PCs and their DOS environments enabled an increase in the speed and accuracy of image analyzers. During the past years, WindowS@environments have created a more user-friendly interface for the PC user. Today's image analysis systems are utilizing the latest technology of computer processors and Windows environments. Typically, specialized imaging boards are no longer needed to process images. The integration of other Windows based programs is made easy (MS Office). Therefore, modern image analysis equipment gives an opportunity for electronic image acquisition, storage and archiving as well as report generation. The image analysis user can place images and measurement data in professional looking reports. The ever increasing demand for higher quality products at lower competitive costs increased the need for accurate and timely microstructural analysis. The Buehler Omnimet Image Analysis System was developed to solve this dilemma. Since the introduction of the first generation of the Omnimet Image Analysis Systems (Omnimet) in 1980, several generations have been developed, including Omnimet II, Omnimet 3 and 4. Next, the Omnimet Advantage was developed. This was a milestone insofar that the software and hardware development was brought in-house. Recently, the latest generation of image analysis system, the Omnimet Enterprise, was introduced. The major changes were predominantly in the software and hardware architecture resulting in a much faster and more user-friendly image analysis system. In addition, the software has been broken into modules allowing the user to purchase only those features necessary and then upgrade at a later date. Automatic Image Analysis Overview Such renowned scientists as Lord Kelvin and Henry Clifton Sorby expressed the critical need for quantitative analysis more than 150 years ago. They understood the importance of numerical data rather than simple qualitative observations. History reveals the evolution of quantitative analysis from the manual methods such as volume fraction estimate charts to the modern computer based image analyzers. Automatic image analysis deals primarily with the analysis of features in a twodimensional image obtained through image forming devices such as a microscope. The features of interest are binarized or highlighted with a bitplane (color). The binarization or thresholding process is performed based on the gray values or colors of the pixels in the given image. Following the thresholding process, binary image ~Buehler Ltd. 1999 4 The Institute for Microstructural Analysis modifications are often neededto isolate the desiredfeatures. A variety of common stereological measurements can then be employed to characterize the microstructure of the material. The following processes are included in the overall terminology of "Automatic Image Analysis" and will be covered in this course: .. .. . . . Image Capture (Acquisition) Image Clarification Thresholding (Binarization) Binary Image Modification Measurements Report Generation Image Storage & Retrieval (Database) This image analysis course covers the basic principles of imaging algorithms and measurements as they occur in the field of quantitative metallography. It stresses the importance of good specimen preparation practices, microscope optimization, and proper image analysis and database operations to obtain maximum accuracy. Through lectures, demonstrations, and nands-on training, basic image analysis operations are taught. Additional subjects such as shading correction, frame operations and innovative analysis techniques will be included. The course will also cover the integration of images and results to MS Word and Excel. Image Analysis LImitations Although image analysis offers a whole world of applications. it is also important to understand some of the factors that may compromise the accuracy of the data obtained. The following are some of the potential sources for inaccuracy: The results may be skewed by poor specimen surface preparation that produces relief, i.e.; hard constituents are higher than the matrix material. Edge rounding is a polishing artifact that causes pores to appear enlarged or edge features to be distorted. (Chapter 3) If the specimen is etched to produce contrast, care must be taken to avoid overetching that could remove precipitates that would then appear to be pits or porosity. (Chapter 3) The microscope used to generate the image must be correctly adjusted to obtain optimum resolution and even illumination that will define features (Chapter 4). The detection operation must be performed carefully (requiring materials knowledge) to clearly separate image elements so that they can be represented properly. The data produced by image analysis must always be considered in the larger context of the chemistry, thermal and mechanical history of the specimen. In the final analysis, we must remember that the images analyzed are twodimensional slices of a three-dimensional structure. Common Applications Throughout the past 20 years of Buehler's involvement in image analysis, a number of applications stand out and may be of interest to most image analysis users. Below is a listing of these applications: ASTMGrainSize:Manual determination of grain size according to ASTM E112 is tedious or error prone. Image analysis provides a rapid and accurate method of this measurement. Even if etching is unable to produce complete grain boundaries or if there are twins that could skew the data, modification can be employed to make corrections. If a specification cites maximum grain size limitations, the excessively large grains may be transferred to a different bitplane color to provide visual and numeric feedback. Histograms, which graphically show the grain size distribution, may also be produced. Porosity: Porosity is detrimental the physical properties of most engineering materials. Image analysis is able to characterize the pores according to the number of pores, maximum size, average size and the size distribution in the form of a histogram. Linear Measurements: While the simple filar measuring accessory is still widely used for making occasional measurements, in cases where a high quantity of measurements and more statistics are required, image analysis is time saving. After the necessary delineation, detection, and binary isolation of the coating, several grid lines are superimposed. These are then combined with the solid coatings using Boolean logic. The result is many strings (ferets) representing the coating thickness at a given point of the coating. In a given field, up to 100 coating thickness data points can be generated allowing for a statistically sound coating thickness distribution. Feature ShaDe and Size: The shape of the graphite constituent in gray and ductile irons is critical. The flake shape of the graphite in gray irons severely limited this alloys usefulness due to its low ductility, impact and tensile strength. Ductile iron was developed 50 that the graphite would occur in the form of spherical nodules with the result of dramatically improved physical properties. However, variations in chemistry and other factors can cause the nodules to be irregular, leading to some degradation of the properties. The ability to monitor the graphite shape or determine "nodularity" is another ability of image analysis. These same techniques are applicable to any constituent that can be detected. Phase Percentaae: The area percent of various phases of in a microstructure have a great influence on their properties. The tensile strength of gray iron, for example, is directly related to the % pearlite in its microstructure. Significant area percent carbide in many alloys is a measure of machinability and in many specifications. Retained austenite is another phase that is viewed as detrimental in certain alloys under specified service conditions. In addition, multiple phases can be detected in a single routine. @BuehlerLtd. 1999 .. The Institute for Microstructural Analysis ~ and PractIceof AutomaticImageAnalysis . ,~.! Manual Quantitative Analysis Stereology Manual Quantitative Analysis Initial approaches toward quantification included chart ratings and visual estimates. These were followed by general linear measurements. Metallographers commonty perform metrology type measurements; e.g., when measuring case depths, decarburization or plating/coating thickness. A scale is placed over the structure, and the depth or thickness perpendicular to the surface is measured. For example, ASTM E 1077describes the measurement of decarburization of steel specimens using such measurements. Stereology ~ Nomenclature Application of stereology requires the use of mathematical symbols for the parameters. The International Society for Stereology has promoted a standard nomenclature which is constantly evolving as new approaches are developed. The most basic symbols are: P = Point L = Line A = Area S = Surface V = Volume N = Number These symbols can be combined in a number of ways to generate different symbols. For example, Pp represents the point fraction; that is, the fraction of grid points lying in a phase of interest. While A and S seem to be the same, A is for a flat surface while S is for a curved surface. Thus, Sy represents the grain boundary surface area per unit volume. NAis the number of particles per unit area while Ny is the number per unit volume. PhasePropo~ons One of the most common measurements, determination of the amount of phases present, can be done using three different methods. Areal analysis, developed by Delesse in 1848, says that the area percent of a phase on a 2D plane is equal to its volumetric percent, that is, AA= Vy' However, measuring the area of second phases is very tedious unless they are quite coarse. Lineal analysis, developed by Rosiwal in 1898, says that the lineal fraction of test lines in a phase on the 2-D plane is equal to its volumetric percentage, that is, ~ = Vy' This is easier to determine but still rather tedious. Starting around 1930, several workers in different fields and countries showed that the percentage of points on a test grid lying in the phase of interest was equal to the volumetric percentage, that is, Pp = Vy' Of the three methods, this is the most efficient technique; that is, it produces the best precision for the least effort when done manually. The point counting technique is described fully in ASTM E 562 (also ISO 9042). Image analyzers use essentially the same procedure; that is, the amount of a phase (usually called the area fraction or volume fraction even if it actually is a point fraction) is determined by the number of picture elements or "pixels" in the phase of interest divided by the total number of pixels; i.e., Pp' expressed usually as a percentage. PointCountinaExamole ASTM E 562 describes the point counting procedure for determining the amount of second-phase constituents. A grid with systematically spaced points (e.g., 10 rows of 10 equally spaced points) is superimposed over the structure, either on an eyepiece reticle or a plastic sheet placed over or behind a ground glass projection screen or on a monitor. The points are usually drawn as fine perpendicular crossing lines and the "poinf' is the intersection of the two lines. This is done because actual points would be very difficult to see. The optimum point density for manual point counting is @Buehler Ltd. 1999 2 The Institute for Microstructural Analysis Chapter 2 Principles and Practice of Automatic Image Analysis usually determined from 3M v where the volume fraction is a fraction (not a percent). If the volume fraction is 0.50 (50%), then the optimum grid point density is 6. On the other hand, if the volume fraction is 0.01 (1%), the optimum point density is 300. The point fraction is the ratio of the points in the phase of interest to the number of grid points. Oftentimes a 100 point grid is used for all work since the division is unnecessary. Points falling on the interface are counted as Y2a hit. For best manual results, sample more fields and do as little work as possible on each field measurement (the adage, "do more, less well"). The field-to-field variability has a greater influence on measurement precision than the counting precision on a given field. ~, +: + ~ i~! B ~ ~i ~ ~ .: + ~ ~ .,ifr;; '-T ;~ ..:f !;F' ~ I'" ~ ~ ~ r;,~"i: :0= + ~ ~ ~ "~ ~ ~~ l T ~ ! ",. ~ .:t . '+ ~ Figure 9-1 Point count grid overlayed on Muntz metal, Klemm's I reagant The microstructure above shows the beta phase in Muntz metal (Cu-40% Zn) preferentially colored by Klemm's I reagent while the alpha matrix is unaffected - ideal conditions for point counting. Since there is less (3than a, count the number of times the points fall in the colored (darker grey) (3 grains. The amount of a is simply 100 - %(3.As you can see, we have superimposed a 54-point test grid (8 rows of 8 points) over the structure and we have 12 hits and 11 tangent hits. The point fraction (volume fraction) is 17.5/54 = 0.273 or 27.3%. The point counting grid would be placed randomly over the structure a number of times so that the point fraction is determined for a number of fields. The necessary number of fields to yield a 10% relative accuracy varies inversely with the volume fraction (the lower the volume fraction, the greater the number of fields, i.e., the greater the total number of applied grid points). @Buehler Ltd. 199! "he Institute for Microstructural Analysis Principles andPracticeof Automatic ImageAnalysis [.. i Groin SIze Grain size is perhaps the most commonly performed microstructural measurement, although chart ratings are more commonly done than actual measurements (this is changing). The ASTM grain size number, G ,is defined as: n = 2 G-1 where n is the number of grains per square inch at 100X. To convert n to NA(the number of grains per square mm at IX), multiply n by 15.5. The four ASTM grain size charts show graded series of grain structures of different types. Grain size can be measured by the planimetric method Jeffries in 1916) or by the intercept method (developed 1904). In the planimetric method, a count (developed by Zay by Emil Heyn in is made of the number of grains completely within a circle of known area and half of the number of grains intersected b y the circle to obtain N . Then, N is related to G. This method A A slow when done manually because the grains must be marked when counted to obtain an accurate is count. In the intercept method, either straight lines, curved lines, or circles are placed over the structure and a count is made of either the number of grain boundary intersections, P, or the number of grains intercepted, N, by the line. P or N is divided by the true line length, LT,to determine PLor NL,the number of intersections or interceptions per unit length (for a single phased structure). The reciprocal of PLor NLgives the mean lineal intercept length, 1, 1= 1/N, = 1/P, a measure of grain size that can be converted to a G value. The intercept method is more efficient than the planimetric method yielding acceptable measurement precision «10% relative accuracy) in much less time. ASTM E112 contains a complete description of these methods. A major revision of E112 was approved in 1995. Grain Size ExamDle ASTM E 112 describes the manual measurement of grain size for structures with a single grain size distribution while ASTM E 1382 covers image analysis measurements. Note that E 112 was heavily revised in 1995 (additional minor changes in 1996), so it is best to read the latest version. Grain size can be measured using either the planimetric or the intercept methods. In the examples, the approach has been simplified slightly to illustrate the methods. Additional field sampling should be done to obtain good statistical data. In the planimetric method, ASTM recommends using a test circle with a diameter of 79.8 mm (5000 sq. mm area) placed randomly over the grain structure. To obtain an accurate count of the number of grains inside the circle and the number intercepted by the circle, we must mark the grains on the template as we count which makes this method slow (although this is not a problem by image analysis). We must know the magnification of the image as well. The figure 9-2 shows the grain structure at 200X of a low-carbon sheet steel after color etching. A circle of known size (64.4mm diameter) has been @Buehlerltd. 1999 4 The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImageAnalysis.., Chapter2 placedover the image to illustrate the method. There are 44 grains within the circle (n...) and 34 grains intercepted by the circle (n~). The number of grains per sq. mm, NA, is calculated from:NA= f {n- + Y2(n---J} The multiplier f is calculated from (M2/circle area), where M is the linear magnification of the image. For this example, NA= 12.28 {44 + Y2(34)}= 749.1 grains/sq. mm From NA,we can calculate the ASTM grain size number, G, using the following formula from E 112-96:G = {3.322 (log10 NA)- 2.954} = 6.6 The ASTM grain size can also be determined using the intercept method counting either the number of grains intercepted, N, or the number of grain boundaries intersected, P, with a test line. ASTM recommends using a grid with three concentric circles with a 500mm total line length. To illustrate the principle of the method, we will use the same image with a single circle (shown on previous page). The count of the number of grains intercepted by the circle is N. To calculate the number of interceptions per mm, NL,we divide N by the true length (circumference) of the circle. Since the diameter of the circle is 64.4mm, its circumference is nD, that is, 202.3mm. The true length is 202.3mm divided by the magnification, M, that is, 1.01mm. Hence, NL = N~ = 34/1.01 = 33.6 interceptions per mm. To calculate the grain size, we first determine the mean lineal intercept length, I, which is the reciprocal of NL(or of PL' the number of grain boundary intersections per unit length). Thus, 1= 1/33.6 = 0.0298mm. G is calculated from an equation from E 112-96:G = {-6.644 (log10 1)- 3.288} where 1is in mm. In this example, G = 6.85. Since the two methods are sensing different geometric aspects of the three-dimensional grain structure, they will not give exactly the same value, but they will be close, generally within the experimental limitations of the measurements. In practice, we would repeat these measurements on a number of fields in order to obtain a good estimate of the grain size. Figure 9-2. Low carbon steel sheet with a circle grid superimposed Spacings The spacing between second-phase particles, such as carbides or inclusions in steels or between intermetallic particles in aluminum alloys, can affect mechanical properties and formability. A special case is the interlamellar spacing of pearlite in high carbon steels (like rail steel) where refinement of the spacing improves both strength and toughness. Spacings are easily assessed using a simple Nl (number of particles intercepted per unit length of test line) measurement. The mean center-tocenter spacing, sometimes called 0, is simply: 0 = 1/Nl This is not a nearest-neighbor spacing, but the mean spacing between particles in the test line direction (either placed randomly or in some preferred direction, such as the through-thickness direction). If the amount of the second phase is determined, for example, by point counting, the mean edge-to-edge spacing, called), (or the mean free path, MFP), can be calculated by: ). = (1-Pp)/Nl where Pp is a fraction rather than a percentage. This is a very good structure-sensitive parameter. By a simple subtraction of (0"- A), we can obtain the mean intercept length of the second phase particles - without measuring any particles! Furthermore, if we count the number of particles within a known area to obtain NA (including only half of the particles intersected by the field edges), we can determine the average cross sectional area of the particles, A, by: A = PP/NAwhere Pp is the point fraction (as a fraction, not a %) of the second phase. Thus, the average size of particles can be determined manually without actually measuring the particles. With modern image analyzers, individual measurements of particles are fast and simple. Besides generating average particle dimensions, the distribution of particle sizes can be obtained by feature-specific image analysis. To determine the interlamellar spacing of pearlite (or of any eutectic or eutectoid), it is common practice to count the number of carbide interceptions with a straight test line perpendicular to the lamellae. However, because the lamellae intersect the surface at different angles, it is better to determine a mean random spacing, ar, than a mean directed spacing, ad,A mean random spacing is obtained by determining Nl using randomly oriented test lines (or curved or circular lines). The mean random spacing is easily used to calculate the mean true spacing, at, by:at = ar /2. In the past, the mean directed spacing, ad, was determined for the pearlite colony with the finest observed spacing, and this was assumed to be the true spacing. This is a better technique for isothermally-formed pearlite than for pearlite formed during continuous cooling. However, the longer you search for the finest colony, the finer the measured colony size! That is, the ad value obtained depends upon the amount of time spent looking for the finest colony, even in isothermally-formed pearlite. Any effort spent looking for a "best" or "worst" field condition, of any type, is strongly influenced by the amount of search time, and the results are neither reproducible nor precise. Interlamellar Soacina Examole Traditionally, the metallographer has searched for the finest appearing interlamellar colony and made a measurement of its spacing using a test line perpendicular to the lamellae. This spacing is claimed to be the true interlamellar spacing. However, this method is not reproducible as the longer you search, the finer the measured spacing. A better approach is to measure a mean random spacing and divide that by two to get the mean true spacing. This method was verified and proven to be correct (see Metallography, Vol. 17, No.1, February 1984, pp. 1-17). The micrograph in Fig. 9-3 is that of an as-rolled carbon steel of about 0.45% C, etched with 4% picral and photographed with the SEM (specimen perpendicular to the beam) using secondary electrons at 17,800x magnification. A circle with a diameter of 49.7mm was placed over the lamellae and the number of carbide lamellae intercepted by the test line, N, was counted. NLwas again determined as N (23) divided by the true line length, nD/M, where the circle diameter, 0, is 49.7mm and M is 17,800x. Thus NL is 2622 interceptions per mm. The mean random spacing, crr, is given by: cr, = 1/NL = 0.381 ~ = 381 nm. The mean true spacing, crt, is cr,/2 = 190.7nm. Figure 9-3: As-rolled carbon steel SEM photo (4% plcral) Statisflcs Other measurements are possible, but the ones described above represent some of the simplest and most useful. Each can be repeated on a number of fields on the plane-of-polish so that a mean and standard deviation can be obtained. The number of fields measured influences the precision of the measurement. Manual measurements are tedious and time-consuming so sampling statistics may be less than desired. Image analysis removes most of the barriers to inadequate sampling. @Buehler Ltd. 1999 7 The Institute for Microstructural Analysis A good measure of statistical precision is the 95% confidence interval (or confidence limit). This defines a range around the mean value where, 95 times out of 100, a subsequently determined mean will fall. For example, a mean volume fraction of 10% :t;2% implies that for 95 of 100 measurements, the mean value will be between 8 and 12%. The 95% confidence interval is determined by: 95% CI = ts/nY2 where t is the Student's t factor (t is a function of the confidence level desired and the number of measurements, n, and can be found in standard textbooks and in some ASTM standards, e.g., E 562 and E 1382) and s is the standard deviation. The relative accuracy, RA, of a measurement is determined by: %RA = 100 . (95% CI)/X, where X is the mean value. In general, a relative accuracy of 10% or less is considered to be satisfactory for most work. Sampling So far, we have discussed measurements on a single plane-of-polish on one specimen. Because we are usually dealing with large quantities of material (such as an entire "hear' of metal or alloy, a large heat treatment lot of bars, forgings, etc., or a large forging or casting), a single specimen may not be representative of the whole quantity. Ideally, random sampling of a large batch would be best, but practical considerations usually rule this out. In most cases, sampling is done at predetermined convenient locations, such as the extreme ends of a coil, bar, plate, etc., or at locations which will be subjected to extensive machining. In some cases, excess metal is added to a forging or casting to provide test material as similar as possible to that of the component. Sampling is often a compromise and is rarely excessive due to cost considerations. However, inadequate sampling or sampling of nonrepresentative material or locations may degrade the value of the measurements. Stereological measurements are best employed on sectioning planes that sample the microstructure randomly. This means that any oriented plane will produce the same data within the limits of statistical precision. However, for certain materials, the microstructure varies with the test plane. A classic example is that of inclusions in wrought steels which are preferentially elongated in the deformation direction. Sampled perpendicularly to the deformation axis (transverse plane), the inclusions look like spherical particles while, when sampled on a plane parallel to the deformation axis (longitudinal plane), they appear as long, thin rods or as broken "stringers". If measurements are made on these planes, we obtain different values for NA, their length (or diameter), their spacing, and even the volume fraction. Thus, if we want to characterize their 3-D characteristics, measurements must be made on the three principal planes and averaged (or an alternate technique such as the trisector with vertical sectioning must be employed). In practice, the true 3-D characteristics may not be needed, and measurements are made using one standard test plane orientation which yields datasuitable for quality control and material comparisons. This is the procedure employed in ASTM E 1245. ~ @Buehler ltd. 1999 9 The Institute for Microstructural Analysis Cha ter 3 The Role of S ecimen Pre aration The Importance of Specimen Preparation Sectioning Mounting Grinding & Polishing Microstructural Etching Techniques Contrast Enhancement The Importance of Specimen Preparation Image analysis is most often performed on surfaces that have been prepared to reveal the "true microstructure." This statement correctly implies that it is possible to produce a surface that does not represent the true condition of the material being analyzed. When manual visual methods of image analysis are used, the human eye's ability to compensate for abnormal surface conditions, combined with the mind's ability to make rational judgments, reduces the effects of poor specimen preparation. However, an image analysis system is not able to make such adjustments and therefore analyzes exactly what is observed. Any surface condition other than the true microstructure that is significant enough to be detected and influence the measurement data must be avoided. These conditions are listed in the table below, together with the probable effect on the analysis. @Buehler Ltd. 1999 Defect Effect on Analysis Relief Hinders edge discrimination Increases volume fraction Pull-outs Hinders identification Detects as additional phase Reduces volume fraction Scratches (if large enough) Detect as a feature Complicate threshold setting Increases feature counts Increases volume fraction Comet tails Detect as a feature Complicate threshold setting Smearing Makes detection more difficult Stains Increases feature counts Increases volume fraction The Institute for Microstructural Analysis Principlesand Practiceof AutomatedImage Analysis",;, 'i Sectioning Sectioning is performed to remove a suitably sized specimen for subsequent mounting and polishing. Since the intended plane of polishing is usually determined by the sectioning operation, caution must be exercised to avoid excessive damage to this surface. Abrasive cutting, the most often recommended method of metallographic sectioning, produces minimal surface deformation and is also the most economical, simple, and rapid method available. If the removal of specimens involves destructive methods such as torches or hacksaws, the cuts should be made at a safe distance from the area of interest. Subsequent cutting to remove the damaged areas should be performed in the laboratory with an abrasive cutter. Wheel selection should be based on the chemical and physical properties of the material to be cut. While aluminum oxide abrasive wheels are suggested for cutting ferrous alloys, non-ferrous alloys and non-metals should be cut with silicon carbide wheels. Abrasive wheels are rated according to their hardness. The softer wheels are used to cut harder materials; the harder are preferred for softer materials. Special resin or metal bonded diamond abrasive blades may be required for extremely hard metals, carbides and ceramics. Adeguate. uniform coolant is important to prevent heat buildup during the cutting process. Submerged cooling is very efficient, but cutters employing an abundant stream of coolant directed at the cutting area may be equally effective. If a cutter employs adjustable coolant nozzles, the distance from both nozzles to the workpiece must be equal. This prevents irregular wear of the abrasive wheel, which may result in curved cuts and possible wheel failure. Technigue is another important aspect of metallographic cutting. Parts must be clamped securely to prevent movement during cutting. Firm, but not extreme pressure, should be applied with the blade to maintain a reasonable cutting action. Excessive pressure could cause burning of the workpiece and possible wheel breakage. Resistance to cutting could indicate a wrong choice of abrasive wheel for the specimen or insufficient cooling. Drastic slowing down or stalling of the cutter while in operation may indicate that the particular cutter is not suited for the job. Mounting Mounting provides a safe, convenient means of holding metallographic samples during preparation and protects the sample edge from the destructive attack of abrasive materials. Encapsulants for metallography fall into two major categories: . . Compressing Mounting Castable Mounting Compression molding resins are dry powders or PREMOLDS @ which cure at 3,000 to 4,200 psi (3-4.2 ksi) pressure and 140 - 16~ C temperature. They are ideally suited for mounting solid specimens that are not damaged by the required heat and pressure. While compression mounting is more economical and usually requires less time and effort, castable curing resins are preferred for specimens that are sensitive to damage from heat and pressure. Selection of a mounting technique must also take into consideration the possible need for edge protection. Vital information such as case hardness depth, plated layer thickness and surface defects may be preserved by the application of @Buehler Ltd. 1999 2 The Institute for Microstructural Analysis effectiveedge protection. A poorly protected specimen edge becomes a radius rather than a flat plane when attacked by abrasives. This might cause distortion and loss of important features, which may. due to the divergent reflection of light, lead to inaccurate analysis or measurements. If an edge is rounded, a surface layer may appear shallower than the actual dimension. Poor edge rounding may be controlled by choosing a low shrinkage mounting material containing hard filler such as EPOMET@or adding a hard filler to a low shrinkage resin such as Epoxide. Another effective edge preservation technique utilizes an electroless nickel coating. EDGEMET@,which forms an intimate hard protective layer on certain specimens. - Edg.. not protected c=~ LIght T~ T T Specimen Mount Rounded Retained Figure 3-1. Comparison of images produced by specimens based on flatness Grinding & Polishing Each stage of abrasive preparation is vital to the end result. Incorrect preparation could lead to an erroneous interpretation. Regardless of the sectioning techniques used, some degree of surface damage remains and must be removed during the grinding and polishing processes. The depth of the deformed layer will vary widely, depending on the physical properties of the specimen material and the severity of the abrasive operations used during the initial stages. Failure to successfully remove all the abrasive damage could lead to poor microstructural definition, and, in extreme cases, artifacts or false microstructure. The deformation produced by each preceding step must be completely removed by the succeeding one. Shortcuts should be avoided and the entire recommended sequence followed, so that the finished polished sample may be analyzed with confidence. Today's workplace demands more and more automation. This is true in reference to planar grinding as well. Conventional methods are effective, but have one major downfall: the effective use of SiC papers is limited to 30 - 60 seconds per paper. In recent years, different surfaces and abrasives were developed as alternatives to SiC papers. These products include grinding disks, platens, durable synthetic cloths, alumina papers, etc. Sufficient lubricant must be applied to avoid heat build-up and flush away the removal products. Too much lubricant will result in a hydroplaning action where the specimen rides on a film of water, thereby reducing the effectiveness of the abrasive. When the grinding is performed correctly, the following stages and final polishing will proceed more efficiently and with greater assurance of acceptable results. It is possible to use alumina and other abrasives for the intermediate stages, but for the most part diamond compounds are the most effective. Diamond is extremely hard and tough while removing specimen material faster and cleaner than most other abrasives. Although the initial cost of diamond may seem higher, real savings are gained through reduced polishing times, greater flatness and superior finishes. Hard specimens may be prepared with less pitting, rounding or plucking when using diamond rather than other abrasives. Final polishing is intended to produce a scratch-free surface for metallographic analysis. Since the material removal rate for this stage is extremely low, it is nearly impossible to correct errors committed at earlier preparation steps. Over polishing at this point may produce microstructural relief, pits, rounded edges and irregular surfaces. If the previous steps have been correctly and thoroughly performed, the duration of final polishing may be minimized. Final polishing is usually performed on a rotating wheel covered with a cloth having slight to moderate nap. Alumina or colloidal silica abrasives in the 0.3 to 0.05 micron range are commonly used. To charge the wheel, the cloth is first moistened with distilled or de-ionized water. The alumina or colloidal silica suspensions are then applied to the cloth. For best results, the cloth should not be too wet because this increases the possibility of pitting or inclusion pullout. Other abrasives may be helpful in preparing specific specimen materials. Magnesium oxide is very effective for final polishing the softer wrought aluminum alloys. Chromic oxide is sometimes used to prepare steels for inclusion studies. Cerium oxide may be used for preparing copper, aluminum and other soft materials. ~Buehler Ltd. 1999 " The Institutefor Microstructural Analysis Etching Techniques Microstructural etching is a chemical process that develops fine microstructural detail by selectively attacking and/or coloring susceptible areas, thereby producing visual contrast. Two major types of etching are chemical etching and electrolytic etching. Chemical Etching A polished specimen is swabbed with or immersed in a suitable etchant. The selective attack results from the different dissolution rates based on grain orientations or chemistry. Electrolytic Etchina A polished specimen is placed in an etching solution and made the anode in an electrolytic cell. A cathode made of compatible material is also placed into the cell. Low voltage is applied by a controlled source for a period determined experimentally or from previous experience. In the process, the anode is selectively dissolved. ~o A B c D Figure 3-2 Application of a chemical etchant by swabbing Chemical Etching Facility A suitable etching facility must provide a safe means of mixing stock chemicals and storing stock chemicals and mixedetchants for ready use. The ideal facility is a fume hood to vent any potentially hazardous fumes and a sink with running water for rinsing specimens after the etchant has been applied. An ultrasonic cleaner can also be included to remove tightly adhering abrasive particles prior to etching and reaction products after etching. Some additional supplies required for a chemical etching facility would include: cotton balls and cotton swabs, beakers or dishes for working solutions, alcohol in a dispenser bottle to remove water, tongs for holding specimens, safety glasses or shield, and plastic gloves. In the as-polished condition, most metals and their alloys display limited microstructural detail such as: . Voids such as porosity . Non-metallic inclusions . . Corrosion products . Reinforcing fibers Graphite in cast irons When a polished specimen is etched with a suitable etchant, the following additional microstructural details are revealed: . . . . . Grain boundaries . . Depletion zones . Segregation . Precipitates Layer interfaces Phases Coring Reaction zones Dendrite patterns Contrast Enhancement Ceramics Ceramic materials are increasingly used in applications requiring high hardness, toughness, heat, wear, and corrosion resistance. These materials, which include alumina, silicon nitride and others, are not only difficult to polish but also resist efforts to reveal their microstructure. Even when porosity, grain boundaries and cracks are visible through the microscope, the contrast is marginal at best for image analysis. This is particularly true for magnifications higher than 200x. In such cases, sputter coating maybe used to increase the contrast significantly. Sputter coating is the vacuum vapor deposition of a thin film such as gold or goldpalladium. Concrete Analysis Concrete is an aggregate material whose properties depend upon a correct ratio of particles and air porosity in the cement matrix. Therefore, it is extremely helpful to perform particle counts and area % porosity in a timely manner using automatic image analysis. Like ceramics, concrete samples have limited optical contrast, so the following method was developed to increase the contrast. The specimens are first ground through 800 grit SiC abrasive papers to produce a smooth, flat surface. They are then washed thoroughly. This is followed by a 1202F (49-54QC) bake in an oven for 3-4 hours to remove moisture. Next the specimens are pressed onto a stamp pad containing a moderate amount of black ink, taking care to avoid filling the pores. The ink-coated specimen is placed into the oven for 10-12 hours or until dry. While the sample is still warm, apply zinc oxide paste to the surface, and then place in a refrigerator until the paste hardens. Carefully scrape the surface with a plastic scraper or putty knife. Dust the surface with aluminum oxide polishing abrasive or plaster of Paris and rescrape the surface until the voids appear white against a dark background. The specimen is now ready for analysis. ~Buehler Ltd. 1999 6 The Institute for Microstructural Analysis Principles arKI Practice of Automated Image Analysis Chapter 3 I Common Etchants for Copper Alloys Name Use Composition Application Mode Macroetch (brilliant) General 50 ml H2O 50 ml HNO, 0.5 gm AgNO, Dip General Most alloys (rapid) 50 ml NH.OH 50 ml H2O2(3%) Swab Dichromate Most alloys 500 ml H2O 10 gm ~Cr 207 40 gm H2SO. (Add 1 drop HCI/25 ml before using) Swab Grard #1 Most alloys 100 ml H2O 5 ml HCL 20 gm FeCls Swab Chromic AI Bronze 99 ml H2O 1 gm crO3 Electrolytic CommonEtchants for Aluminum Name Use Composition Application Mode Flicks Alloy AI (Macroetch) 90 ml Water 15 ml HCL 10 ml HF Dip (to remove smudges. dip in HNO3) General Unalloyed and 1000 Series II 199 ml Water 1 ml Hf Swab Keller's Alloy AI 190 ml Water 3mlHCI 5 ml HNO3 2ml Hf Dip CommonEtchant for Nickel Name Use Composition Application Mode Flat Nickel 50 ml Acetic Acid Dip @Buehler Ltd. 1999 The Institute for Microstructural Analysis ~ CommonEtchants for Ferrous Alloys Name AJ-7 Use Composition Application Mode Iron and steel 50 ml Water 50 ml Hydrochloric acid Dip for 10-15 minutes 160-175DF 2 ml HNO3 98 ml Ethyl Alcohol Swab for 5-15 seconds 100 ml EthylAlcohol 10g PicricAcid Swab for 10-15 seconds 100 ml Ethyl Alcohol 5ml HCI 19 Picric Acid Swab for 10-15 seconds 90 ml Water 10 9 Potassium Meta Bisulfide Dip for 10-20 seconds 100 ml Water Dip for 1-10 minutes (Macroetch) Nital Carbon steels (Ferrite boundaries) Picral Carbonsteels, Ferrite and Pearlite Vilellas High carbonsteel (transformation products) Potassium Meta Bisulfite Alloy steels (4% untempered martensite) ASTM Acetic Electrolyte Prior austenic grain boundaries in martensite and bainite structures 300 series stainless 2 9 Picric Acid 1 9 Sodium Tridecylbenzene Sulfonate 10 ml HNO3 10 ml Acetic Acid 15 ml HCI 5 ml Glycerine Electrolytic 3-5 volts for 10-20 seconds Chromic Electrolytic 300 series stainless 90 ml Water 10 9 Chromic Trioxide Electrolytic 3-5 vohs for 5-10 seconds Kallings 400 series stainless 33 ml EthylAlcohol 33 ml Water 33 ml HCI 1.5 9 CupricChloride Swab for 5-10 seconds Special Alloy Etchants Principles . and Practk:e of Automated Image - Analysis . i, i! Cha ter 4 r! Li ' Chapter 4 tical Microsco Introduction to Light Optical Microscopy Light Optical Microscopes Illumination Modes Resolution Kohler Illumination Cameras and Photomicrography Video I Digital Imaging Optical Terminology Introduction to Light Optical Microscopy Once a specimen is prepared and the true microstructure is revealed (see previous chapters), the metallographer needs to study the microstructure of the material. This means the determination of phases or constituents, including their relative amounts, sizing, spacing and arrangement. The examination of a specimen surface using optical microscopes depends upon the contrast that exists between the features in the microstructure. These features arise because of differences in the geometrical or the absorption (reflectivity) characteristics of the specimen surface. Ceramics have a tendency to absorb light and therefore their reflectivity is reduced however there are techniques available to enhance the reflectivity of the specimen surface. Geometrical effects occur primarily because of differences in the level (topography) of the surface caused by the etching process. These differences, for example, are seen at grain boundaries after etching, where the grain boundaries produce different degrees of light scattering and hence different degrees of contrast and brightness. The reflected light microscope is the most commonly used tool in metallography for the study of materials. Light optical microscopes are required to magnify images to observe the fine details not seen with the human eye. The unaided human eye can only resolve details separated by about 0.1mm or 100 microns. A microscope magnifies an image with the application of suitable lenses that bend and focus light or other types of radiation. The useful magnification that can be achieved reaches a limit, which is dependent upon the wavelength of the radiation employed. ~Buehler ltd. 1999 The Institute for Microstructural Analysis Table 6-1: Common Magnifications for Light Optical Microscopy Applications Light microscopes are the least expensive scope and the simplest to use with a considerable amount of flexibility. Standard descriptions typically include the way the image is created such as either being a reflective, transmitted, or stereo microscope. For the majority of materials prepared by standard practices (grinding and polishing), the reflected light microscope is utilized. Observations of thin sections, however, require a transmitted light microscope. While this lesson will focus on the light microscope, other types of instruments are often used to characterize microstructures, such as: Scanning electron microscope (SEM) Transmission electron microscope (TEM) Light Optical Microscopes Stereo Microscopes The stereo microscope offers the user several advantageous features not found in a compound microscope. The stereo microscope has an extended working distance (as much as 2000 mm), and depth of field. These features make the stereo microscope an indispensable tool in many areas of investigation. The magnification range is in the area of 1x to 250x. They produce a threedimensional visual image and use coaxial, ring or oblique illumination. They are excellent for the examination of rough surfaces such as fractures. On polished surfaces, they produce a darkfield image. Transmitted Light Microscopes Transmitted light microscopes are used to examine thin transparent materials such as human tissue, bones, thin rock sections and minerals. Light rays from the illuminator are primarily collimated via a lens system located in the lamphouse or microscope base. These rays are directed to a reflective mirror situatedin such a manner to direct these rays parallel to the optical axis. Ascending rays pass through the field diaphragm located in the base of the microscope. Rays, after being further collimated at the field diaphragm, leave and enter the aperture diaphragm of the condenser. Transmitted light rays leaving the condenser are highly organized and intensified. These rays strike the transparent specimen and proceed into the numerical aperture of the objective, the eyepieces and to the observer. Transmitted brightfield yields a highly magnified and resolved image. Little color is discerned. Shadows, outlines and edges of clear and opaque substrates are generally observed. Reflected Light Microscopes They are often called metallurgical microscopes because they are necessary for the examination of opaque polished samples. Vertical illumination produces the needed Brightfield effect. Other forms of illuminations such as Darkfield, Polarized Light and Differential Interference Contrast (DIG) may be utilized as discussed in this chapter. ~inllr. R.i. RAei~ r.fI."t.tt linht mi"rnern~ "nnfinllrAtinne Inverted Microsco~es Metallographs areinvertedstage microscopes that are designed to meet the exacting needs of the metallographer and material scientist. As shown in fig 6-2, the metallograph has numerous features that make it a versatile tool for microstructural analysis. Unlike the earlier La Chattier design that was a long optical table, modern metallographs are compact tabletop designs. Depending on the needs of the user, a metallograph may have one or two integral illumination sources as well as one or two camera formats, plus a CCTV port for image analysis or monitor viewing. In addition, special illumination modes such as darkfield, polarized light and differential interference contrast are easy to access. Although the inverted stage feature provides self-leveling of the specimen, it also restricts specimen visibility, making it more difficult to locate a specific location. There is also a @Buehler Ltd. 1999 3 Figure 6-2: Versamet Metallograph The Institute for Microstructural Analysis greater possibility of damaging the polished surface because the specimen is placed, polished face down, on an aperture plate. Metallographs have a more complex light path that produces some light loss but the more powerful quartz halogen and xenon illumination sources more than compensate for any losses. UDriahtMicrosco~s The upright microscope shown is a direct descendent of the earliest microscope designs but benefits from great advancements in optics that have occurred over the years. One key advantage of upright microscopes is their simple light paths that do not compromise the resolution produced by the objective lens. Another advantage is that the polished specimen lies upright on the stage, allowing the operator to see exactly where the light is hitting the surface. Upright microscopes are usually significantly less expensive than an inverted stage microscope of equal quality. One disadvantage is the need to level each specimen on a microscope slide using a leveling press and clay. Another disadvantage is that the camera is usually not built-in, making it necessary to install an accessory camera to the top of the microscope. "1m Plane T goula. I.n. 0 o~ Field Aperture Dlop~rog", Dlophrog.. I R.fl.~I;ng/ Transmllling S"rfa~. - -- O~.~tlv. r . ).-y ~+-~" ill -eI ,f-: 0 I ili I ".Iay ,.".. I. B \. ~ ' Con_- ~~~Figure 6-4 Light Path of an Upright Microscope Reflective Illumination Modes Metallurgical microscopes are usually purchased with brightfield illumination as the standard condition. Additional types of illumination may also be added as accessories and are illustrated on the following pages. Brightfield (BF) is the standard lighting condition for reflected light microscopy. Light strikes the sample surface at a high angle and is reflected back on the same path to the viewer or camera. The resulting image is viewed as dark lines on a bright background. This is the best condition for cursory examination. Darkfield (DF) is produced by special objective lenses that cause light from the illuminator to be channeled down the side of the lens and strike the sample surface at an incident angle. The light then returns to the viewer via the lens elements, producing bright lines on a dark background. Darkfield illumination causes fine features to stand out, even if they were not visible with Brightfield illumination. See figure 6-5 for an example. Differential Interference Contrast (DIC) is produced when light from the illuminator is split by a Nomarski prism that causes the two beams to be out of phase with each other. Then they are recombined; they produce an effect that accentuates the topography of the sample (i.e., features that are in relief). This is valuable to enhance the contrast of microstructures that are difficult to etch. DIG illumination is very useful in critically evaluating the flatness of advanced materials having a tendency to have excessive relief or edge rounding. See figure 6-6 for an example. Polarized Light: Lightconsistsof electromagnetic wavesvibratingin all directionsperpendicularto the directionof propagation. If light is passedthrough a polarizingfilter, the transmittedlightwill vibrate in a single plane.Such light is referredto as plane-polarizedlight. If a plane-polarizedlight beamstrikes normalto the surfaceof an isotropicmetalsurfaceand the reflectedlight is passedthrougha secondpolarizingfilter (analyzer)placed90 degreesto the polarizer,the lightwill be extinguished.This is referredto as cross-polarized light.Whenworkingwith an anisotropicmaterial,an imageof the microstructure will be observed. Therefore,the microstructureof an anisotropicmaterialcan be observedwithouthavingto etch the specimen. In addition,certainetched conditionsare enhancedwith this technique. See figure 6-7 for an example. Contrast Enhancement of Ceramics Accurate analysis of microstructures requires sufficient contrast to clearly delineate individual constituents from the matrix. This is particularly true with materials such as ceramics that absorb light and therefore have poor reflectance. Even in the etched condition, these materials display very weak contrast, making it difficult if not impossible to analyze them. However, the contrast may be increased dramatically by depositing a 10-15 micron layer of gold-palladium using a commercial sputter coater. This technique was originally employed to reduce electron charging on samples that are placed into the scanning electron microscope. Principles . and Practice of Automated Image - Analysis . II! ' Chapter 4 Figure 6-5: Comparison of a cast aluminum alloy observed in brightfield (left) and darkfield conditions (right). Figure 6-6: Comparison of a white cast Iron (~/o nital etch) observed in brightfield (left) and differential Inteference contrast conditions (right). Figure 6-7: Comparison of a cast aluminum alloy, which has been anodized, observed in brightfield (left) and polarized light conditions (right). ~Buehler Ltd. 1999 8 The Institute for Microstructural Analysis ~88 arxt Practiceof AutomatedImageAn8IY8I8 I C~ Resolution Resolution Limits The most influential component in an optical microscope is the numerical aperture (denoted N.A.) of the objective. This is a measure of its light gathering power. Numerical aperture is defined as: N.A.= n sin a n=refractive index of the medium in front of the objective (n=1 for air, 1.51 for oil), a=the half-angle of the most oblique rays entering the front lens of the objective. The numerical aperture is also related to the resolving power of the objective. Resolving power is defined as the ability to reveal closely adjacent structural details. A more commonly used term is the Limit of Resolution: this is the maximum distance allowing details to be resolved. Limit of Resolution = 1 Resolving Power = A 2 x N.A. A = wavelength of light used A = O.55~ for green light The maximum usable magnification of the light optical microscopic is limited by the inherent resolution of dry objective lenses to be about 1000x. However, if an oil immersion lens is used, a magnification approaching 1500x is practical. This is possible because the index of refraction, n, of certain oils is much higher than air. In actual practice, the N.A. of an air I dry lens never exceeds 0.95. However, if a high refractive index oil is used to transmit light between the front element of the objective lens and the sample, the NA can be as great as 1 to 1.5. The result is better resolution at higher magnifications. However, be certain to use oil only on lenses clearly marked for that purpose. Magnification The total magnification observed at the eyepieces is calculated by the following formula: x eyepiece Total magnification = objective magnification magnification x tube factor magnification Note that the tube factor is usually equal to one Total magnification,however,can exceedthe resolvingpowerof the objective lens and it shouldbe understoodthat the usefulmagnificationis dependentupon the numericalaperture.Notethat once the limit of resolutionhas been reached, there is no point in increasingthe magnification(e.g., enlarginga photograph that does not revealany extra details). Question: Which lens combination produces the best image assuming A = 0.55 pm? A. Combination of a 20x objective lens (N.A. = 0.40) with a 10x eyepiece?, or B. Combination of a 10x objective lens (N.A. = 0.25) with a 20x eyepiece? Answer A. Total magnification = 20 x 10 = 200x Limit of Resolution = A I (2 x N.A.) = 0.55pm/ B. Total Magnification = 10 x 20 Limit of Resolution (2 x 0.40) = 0.69pm = 200x =A I (2 x N.A.)=0.55pm/ (2 x 0.25) =1.10pm The correct answer is A because of the greater resolving power or smaller limit of resolution. At low magnifications, this limit will not be as critical as for higher magnifications where a loss in resolution will cause the image to appear out of focus. Another important consideration of the objective lens is its depth of field. Depth of field is the range within which the details are in focus. For low magnifications, this is not usually critical, but for higher magnifications, care must be taken to insure that the specimen surface is flat and perpendicular to the objective lens. Objective Lens Selection Objective lenses determine both the useful magnification and resolution limits. On the body of each objective lens will be a listing of the magnification, N.A, flat field and color corrections. For practical purposes, a convenient rule to assist in determining the maximum useful magnification is to multiply the N.A. of an objective with 1000 achromats. Magnification in excess of the maximum useful magnification will result in what is termed empty magnification, i.e., the image is enlarged without resolving details. Example: 40 x objective with an N.A. of 0.45 0.45 (N.A.) x 1000 = 450 The 40x objectiveis capableof resolvingdetails up to a total magnificationof 450x. Although emptymagnification is not a major problem at 200x and below, it is very critical when measuring thin layers or resolving fine detail at high magnifications (500x and higher). The other properties of microscopic objectives that must be considered are corrections for optical aberrations. The two principal aberrations are spherical and chromatic. Achromats are corrected spherically for one color. usually yellow-green, and chromatically for two colors - generally green and red. Principlesand Practiceof AutomatedImageAnalysis Chapter4 Kohler Illumination The adjustment technique necessary to obtain the greatest performance from a microscope or metallograph is referred to as "KOhler" illumination. This illumination and adjustment method was devised by a German named KOhlerin 1893 and has been generally used since that date. Kohler illumination Techniques If instructions are not provided with the microscope, follow the steps outlined below. Use the manufacturer's instructions, if available, to locate the following assemblies and controls: Lamphouse or light source Lamp adjustment controls (intensity and position) Condenser mirror or lens (if equipped) . Aperturediaphragm adjustment Field diaphragm adjustment Eyepieces Procedure 1 2. 3. Remove the eyepieces and adjust each for midpoint focus; there is usually a line scribed on the side of the eyepiece barrel to indicate this position. Remove any filters from the light source and light path. These may be the slide-in type or they may be part of a rotating wheel or turret. Place a specimen onto the stage; a typical polished and leveled sample is best. 4. Turn on the illumination source. 5. Adjust both the field diaphragm and the aperture diaphragm to maximum diameter. 6. Adjust the coarse and fine focus controls to being the specimen into sharp focus through the eyepiece. 1 Remove one eyepiece from the microscope. 8. Reduce the illumination to a low level comfortable for viewing. 9. @Buehler Ltd. 1999 Move the lamphouse or the condenser control on the lamphouse until the image of the light bulb filament is in sharp focus when viewed through the tube without the eyepiece. 0 The Institute for Microstructural Analysis Principlesand Practk:eof AutomatedImageAnalysis Chapter4 10. Center the bulb filament in the field of view. Controls for moving the light up and down and side to side are usually part of the light source or its attachment on the lamp house. 11. Reinsert the eyepiece. 12. When viewing through the eyepieces at a focused image, adjust the field diaphragm so that it lies just outside the field of view. 13. Remove one eyepiece. 14. When viewing down the tube without the eyepiece. adjust the aperture diaphragm so that its minimum radius is about 15% less than the radius of the field of view. 15. Replace the eyepiece. Re-install any filters, which were removed. 16. Re-adjust the illumination to a comfortable viewing level. Note: Each microscope objective has an entry aperture through which the light from the lamphouse enters the objective. The diameter of the entry aperture varies with the magnification power of the objective. The higher the objective magnification power, the smaller the diameter of the entry aperture. If the objective lens is change to a higher or lower magnification, the entry aperture at the rear of the objective is also changed Thus, the aperture diaphragm size must also be changed to maintain the 85% relationship between the objective entry aperture and the aperture diaphragm. This is why the aperture diaphragm adjustment must be repeated whenever a different objective magnification is selected. Cameras & Photomicrography For many years, metallographers have documented images. Typically, they are acquired using light optical microscopes, stereomicroscopes, macro lenses and scanning electron microscopes (SEM). Photography was, and still is, the most common way to obtain images in the materials laboratory. At first, glass plates, then sheet film and 35mm film formats were used to accomplish this task. Since the 1960's, Polaroid instant films have largely replaced wet-processed films. Wet processed films produce the highest quality images, with best permanence, easily duplicated but the process is labor intensive and negative storage (and subsequent retrieval) is a problem. Instant films, which do not require a darkroom technician, offer speed and convenience. The savings in processing time and labor is offset somewhat by high film costs, waste, and the greater expense when multiple prints are required. Further, image quality of some instant films is noticeably inferior. Color instant films are plagued with reciprocity failures, i.e., inability to generate true colors unless the exposure time is carefully controlled. The newer 64T film has solved this problem. @BuehlerLtd. 1999 10 The Institute for Microstructural Analysis Principles and Practiceof Aut00\8tedImage . - Analysis -~ I Chapter4 I Different features of microscope cameras include: Size Format The most popular sizes are 35mm rolls, 3x4 packs and 4x5 sheet film of individual instant sheets. Shutters Mechanical or electronic Exposure Control Manual, semi automatic or automatic. Exposure Meters Photographic film is expensive and every effort must be made to obtain the best results with the least possible waste. Automatic exposure meters pay for themselves in film costs and reduced frustration. Fihers Although they are the least expensive equipment item, the availability and correct choice of filters often makes the difference between success or failure. The main types of filters are: Neutral densityfilters which reduce the illumination intensity without affecting the color temperature. Monochromatic filters which produce a single wavelength of light to insure a sharp focus on black and white films. . Color correction filters allow the operator to use daylight film with tungsten illumination and vice versa. Color compensating filters are used to compensate for minor color temperature differences between the film and the illumination source. Photomicrographs are used to document the microstructure resulting from various processes. They are also used to determine the cause of service failures by comparing the microstructure of failed and unfailed parts. Successful photomicrography requires attention to several factors: Correct microscope adjustments Sharpfocus Clean optical surfaces Apertures correctly set Correct and even illumination (right color temperature match with no hot spots) @Buehlerltd. 1999 1t The Institute for Microstructural Analysis Chapter 4 Principles and Practice of Automated Image Analysis Video I Digital Imaging The revolutionary progress in computer and video technology has created a definitive trend toward electronic image acquisition. These images can be used in software applications such as word processors or desktop publishing programs allowing for fast report generation and electronic distribution. Image Capture This term describesimageacquisitionby meansof a camera and frame grabber or a digital camera. Because of the many choices of camera types, a video microscopysystem must be flexible. Analog CCD (Charged-CoupledDevice) cameras,both black and white and color, are most frequentlyused. Component video (Y/C or S-Video)and compositevideo signals and a numberof color video standards such as NTSC, PAL and SECAM are typically supported. Images acquired in the materials laboratory are optimized in real time by adjusting brightness,contrast, and color saturation.The analog output camera signal is then digitized utilizing an analog frame grabber board. Various PC-compatible image file formats and compressionalgorithms are commonly used, such as TIFF, BMP,TGA, DBA,PCX,JPEG and manyothers. Fundamental Parameters of an Imaging System In general all of the same principles apply to electronic imaging as they did for standard microscopy. However, the introduction of new components will slightly alter traditional definitions as well as present new concepts. For example, the placement of a transfer lens between the microscope and camera, may result in the field of view and/or screen magnification being a factor of 2 or more different than the ocular view. Table 7-1 lists the recommended transfer lens based on the chip size / sensor size of the camera. The list below redefines some commonly used imaging terms. Field of View: The viewable area of the specimen under inspection. words, this is the portion of the object that fills the camera's sensor. In other Resolution: The minimum feature size of the object that can be distinguished by the imaging system. Sensor size: The size of a camera sensor's active area, typically specified in the horizontal dimension. This parameter is important in determining the proper lens magnification required to obtain a desired field of view. Also referred to as chip size. Primary magnification: ~Buehler Ltd. 1999 sensor size / field of view 12 The Institute for Microstructural Analysis Determining the field of view I magnification In order to determine the field of view, or portion of the specimen that is visible on the monitor, it is necessary to know the sensor size. the objective magnification and the transfer lens magnification. The field of view does not vary with the size of the monitor. Monitor Field of View (diagonal) = sensor size (diagonal) . objective mag. X transfer lens mag Exam~le: Determine the field of view, if you are using a 40X objective, 0.5 X transfer lens and a Y2inch camera. Field of view diagonal = 8 mm / (40 x 0.5) = 0.4 mm It is also useful to determine the on-screen magnification achieved on the monitor. In order to calculate this, it is necessary to know the objective magnification, transfer lens magnification, sensor size (diagonal), and monitor size (diagonal). On- screen Magnification = optical magnification X electronic magnification, where the optical magnification = objective mag. X transfer lens mag. And electronic magnification = monitor diagonal/sensor diagonal. Example: Determine the on-screen magnification when using a 10 X objective, 0.38 transfer lens, 1/3" sensor size and a 19" monitor. On-screen magnification = 10 x 0.38 X ( 19 X 25.4 /6) = 305.5 X Table 7-1 Transfer lens recommendation Sensor size Camera format (inches) @Buehler Ltd. 1999 Transfer lens Diagonal (mm) 1/3 6 .38X 1/2 8 sox 2/3 11 .67 X 1 16 1.0X 13 The Institute for Microstructural Analysis ~~ ~ndPracticeof Automated ImageAnalysis Chapter4 Image quality An imaging system should create sufficient image quality to enable extraction of desired information about the specimen from the image. Note that what may be adequate for one application may prove inadequate for another. There are a variety of factors that contribute to the overall image quality, including resolution, image contrast, depth of field, perspective errors and geometric errors. Resolution Resolution is a measurement of the imaging system's ability to reproduce object detail. Previously the resolution was examined in terms of the objective lens limitations. Now an additional factor is being added, camera resolution. Start with a simplified image of squares where each square fills one camera pixel. If the squares are the same color and imaged on to neighboring pixels the two are indistinguishable from one another. Since there is no space between the squares they appear as one solid rectangle. In order to distinguish them, a certain amount of white space is needed. The sequence of a colored square next to a white square is said to represent a line pair (Ip). As a result, resolution can be expressed in terms of line-pairs per millimeter (Ip/mm), also known as the frequency. The inverse of the frequency yields the spacing in millimeters between two resolved squares. Figure 6-8. Demonstration of line pairs The object resolution can be calculated from the image resolution of the camera using the primary magnification of the imaging lens. 8~o~uJ.as: Line-Pair (Ip/mm) = 1 / Spacing (mm), Line-Pair (Ip) = 2x Pixel CCD resolution: Cameraresolution(~) = 2 x Pixelsize (~) Object vs Cameraresolution: Object res. (~) @Buehler ltd. 1999 = Camera resolution (~) 14 / primary magnification The Institute for Microstructural Analysis Contrast Clearly. resolution is important for describing detail. An equally important factor is how effectively the differences between the feature and background shade of grey are reproduced in the image. A black line on a white background is an example of 100% contrast. % Contrast = (I max -I min) I (I max + 1min) x 100 : Intensity, greyscale values range from 0 (black) to 255 (white) Depth of Field The depth of field of a lens is its ability to maintain a desired amount of image quality as the specimen is positioned closer to and further from best focus. As the specimen is placed closer or farther than the working distance, it goes out of focus and both the resolution and the contrast suffer. Components The sensor, as well as other electronic components, plays a significant role in the performance of an imaging system. Proper integration of all components (including camera, capture board, software and cables) will result in optimum system performance. Charge-Coupled Devices (CCD) Charge-Coupled Devices (CCDs) are the most common camera sensors used in machine vision. The CCD camera contains a silicon chip that consists of a matrix of light sensitive photosites called pixels. CCD Pixels When light falls on a CCD chip, it is collected by a matrix of small potential wells called pixels. The image is divided into these small discrete pixels. The information from these photosites is collected, organized, and transferred to a monitor to be displayed. Analog CCD cameras have rectangular pixels (larger in the vertical dimension). This is a result of a limited number of scanning lines in the signal standards (525 lines for NTSC, 625 lines for PAL). Asymmetric pixels yield higher horizontal resolution than vertical. Analog CCD cameras (with the same signal standard) usually have the same vertical resolution, for this reason, the industry standard is to specify resolution in terms of horizontal resolution. Digital cameras are not limited by the vertical bandwidth, and therefore, can have either rectangular or square pixels. CCD Sensor Size The size of the sensor's active area is important in determining the systems field of view. Given a primary magnification determined by the lens, larger sensors yield a larger field of view. There are several standard CCD sensor sizes: 1/4", 1/3", ¥2', 2/3", and 1". All of these standards maintain a 4:3 (Horizontal :Vertical) dimensional aspect ratio. @Buehler Ltd. 1999 15 The Institute for Microstructural Analysis Principlesand Practiceof AutomatedImageAn~si~ Chapter4 Another issue is the ability of the lens to support certain CCD chip sizes. If the chip is too large for the lens design, the resulting image may appear to fade away and degrade towards the edges because of vignetting (extinction of rays which pass through the outer edges of the lens). This is commonly referred to as the "tunnel" effect, since the edges of the field become dark. Smaller chip sizes do not yield such problems. Transfer lens recommendations based on sensor dimensions were shown in Table 7-1. Analog vs. Digital CCD Cameras The CCD silicon chip is an analog component, meaning that the pixel values are collected by means of sampling (interlaced or progressive.) The signal processor and encoder converts this information into an analog signal, which can be transferred to a monitor. In digital cameras, the digitizing occurs as the signal is collected from the chip. Once digitized, processing and image enhancements can be done with little loss to the signal. Table 7-2 is a summary of the camera characteristics. Table 7-2 Diqital and Analoa Camera ComDarison Digital @Buehler Ltd. 1999 Analog Typically large cameras Size is typically smaller Vertical resolution is not limited, so digitalcameras can offer higher resolution. Vertical resolution is limited by the With no bandwidth limit, these can offer higher number of pixels and larger CCD sensors, resulting in greater resolution. Sensors usually are standard size formats. Computer and capture board required to display signal. Computers/ capture boards can be used for digitizing but are not necessary for display. Signal can be compressed so user can transmit in lower bandwidth with no loss. Analog printing and recording can easily be incorporated into the system. Resolution specified by pixel count Resolution The output signal is digital, therefore little signal loss occurs during processing. Analogsignals are susceptible to 16 bandwidth of the analog signal. specified by TV lines noise and interference which cause signal loss. The Institute for Microstructural Analysis c~ Principles and Practice of Automated Image Analysis Optical Terminology Aberrations Optical defects which are inherent in the lens design. Achromat An objective lens corrected for chromatic aberrations in two colors, for spherical aberration to one color. Apochromatic An objective lens that is corrected chromatically for three colors and is better corrected for spherical aberrations than an achromat. Brlghtfleld An image condition in which depressions appear as dark features in a bright background usually produced by high angle illuminations. Darkfield An image condition in which depressions appear as bright features in a dark background usually produced by high angle illumination. Depth of Field The distance along the optical axis through which the object is in sharp focus. Empty Magnification High magnification which increases the image size .. . . withoutan increase in detail due to the inherent limits of the Objective lens resolution. Flat Field An image condition in which the entire field of view appears to be in sharp focus. Measuring Eyepiece An optical device used to measure the length of image features. Numerical Aperture (N.A.) The measure of the light gathering capability of a lens, as determined by its design. Parfocal The image is in focus at the eyepiece and is also in focus at the film place of the camera or the accessory port. Resolution The measure of the ability of a lens to image closely spaced features so that they are seen as individual objects. Stage Micrometer A plate which is placed on the specimen stage of a microscope so that it scribed units may be visually compared to the arbitrary units of the measuring eyepiece. By determining how many units of the micrometer are equal to one unit of the measuring eyepiece. a calibration is determined for each objective lens. Tube Factor The distance from the focal point of the objective lens to the focal point of the ocular (viewing or projection lens) of a microscope. If greater than 1.0x it will increase the total magnification of the microscope. If the magnification is less than 1.0x,it will decrease the total magnification of a particular objectiveocular lens combination. Working Distance The distance between the front surface of the objective lens and the sample surface when the image is focused. @Buehler Ltd. 1999 1~ The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImageAnalysis; Cha ter ( Chapter5 5 Gettin Started Software Components lIumination Settings (B&W camera) Shading Correction Calibration Software Components Four icons are present on the Windows desktop. ~ . . Enterprise - mainprogram . Database configuration rename the labels for the database fields ~ Camera and microscope configuration - setup the camera interface, image size, aspect ratio and available microscope objectives Stage - setup for automatic stage control s Illumination Settings (B&W camera) The image analysis system uses a 256 gray level range. This extends from 0, which represents black, to 255, which represents white. In order to obtain a good image from the microscope for analysis, the maximum gray value (brightness) of any pixel in the image should not exceed 255. If the gray level of a pixel exceeds 255, it is considered "overflow" or over-saturated. The "Saturation" Overflow button allows the user to turn on or off the overflow indicator. Pixels that are over illuminated (gray scale over 255) are colored in bright blue. This is executed in real time so that the user can tune the illumination until the blue pixels of the saturation indicator are just extinguished. The proper saturation level is reached as the blue pixels disappear. This should be the illumination setting used to ensure reproducibility of the analysis. There might be special cases where some features are purposely over- or under-saturated to threshold a specific phase more effectively. In this cases the reproducibility of the analysis might be more difficult. @BuehlerLtd. 1999 1 The Institute for Microstructural Analysis ~ To use Saturation: Shading ~ 1 Press the 2. Adjust the illumination on the microscope until only a few blue overflow indication pixels are visible on the screen. 3. Reduce illumination slightly until no blue pixels are seen. button in the Image window. Correction The shading correction is used to correct minor illumination differences in the microscope when capturing images. Before setting the shading correction for an objective, make sure that the correct microscope and objective are set and that the illumination is optimally adjusted (i.e. that the microscope is set up for Kohler illumination, see Chapter 4). The Pseudocolor icon is a useful tool to find out whether shading correction is needed because of uneven illumination. This tool allows the user to see uneven the gray levels caused by the light source with color. A mirror type specimen surface is needed to perform this task (mirror or inclusion free, highly polished steel specimen). To apply the Pseudocolor: Press the III buttonin the main toolbar of the Image Shading Correction Setup -- 1. Place a mirrored surface on the microscope stage and view its surface through the Enterprise image window. 2. Click on the Saturation icon and adjust the level illumination until the blue pixels disappear. 3. From Enterprise main menu, click Setup. 4. In the pull down menu, click Shading Correction. 5. Select Set Shading Correction in the expanded menu. The shading correction will be stored in objective database. After this point, when an image is captured, the shading correction will be applied to the image. Please note that the use of shading correction will cause a general brightening of the image. ~Buehler Ltd. 1999 2 The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImage Analysis Chapter 5 Clear Shading Correction 1 Make sure that the correct objective and camera are selected. 2. From Enterprise main menu, click Setup. 3. In the pull down menu, click Shading Correction. 4. SelectClear Shading Correction in the expanded menu. Calibration The Image Analyzer allows the user to conduct quantitative measurements of features in an image. Each image is comprised of thousands of individual picture points or pixels (for example, 756 x 570 = 430,920). Any video system requires a pixel calibration to perform measurements. At a given magnification setting, the computer needs to determine a calibration factor. Therefore, pixels have to be equated to a real distance. Most systems will include an optical light microscope that has a selection of objectives: each of these must be calibrated. With the size of each pixel being known in both X and Y dimensions for each objective, it will be possible to measure the image features in that plane in any direction and at any of the included magnifications. It should be noted that while calibration is critical for many quantitative measures, there are certain ones that are non-calibration critical since they are dimensionless. Typical non-calibration critical measures are form factors (i.e. sphericity and roughness) and fractions (i.e. area percent, aspect ratio, and area fraction sample). Calibrate X Objective When using a CCD camera that has square pixels, then with the correct selection of the X- Y size of image to be sampled from the device, it can be assumed that subsequent images have a correct pixel aspect ratio. This is the case with the use of a standard CCD camera. In the case of a known X- Y pixel ratio, it is only necessary to calibrate the X-axis for each objective. 1 To calibrate an objective, choose Select Setup, Calibrate Objective, Calibrate x. 2. Choose a desired unit in the Units window. (See Figure 5-1) 3. Move the caliper ruler by placing the mouse pointer on the caliper ruler, pressing down the left mouse button and dragging the mouse. 4. Place the mouse pointer on the right leg of the caliper ruler, press down the left mouse button and drag the mouse to change the number of pixels. 5. After aligning the caliper type the distance that the calipers cover in the X Distance box. 6. @Buehler Ltd. 1999 The Aspect Ratio showsthe aspectratio of the imagepixelsfrom the 3 The Institute for Microstructural Analysis ~ objective database. '7 The Image Pixels shows the number of pixels between the legs of caliper ruler. 8. tal Factor shows the Calibration Factor that changes according to the value in the Image Pixels window. 9. Click OK. Figure 5-1 Objective X Calibration Calibrate XV Objective If video cameras are used where the X-V pixel ratio is not known, we need to calibrate the image in both X and V-axis to determine the proper pixel aspect ratio. @BuehlerLtd. 1999 1 To calibrate an objective, choose Select Setup, Calibrate Objective, Calibrate XV. 2. Choose a desired unit in the Units window (See Figure 5-2). 3. Move the caliper rulers by placing the mouse pointer on the caliper rulers, pressing down the left mouse button and dragging the mouse. 4. To change the X axis pixel number: place the mouse pointer on the right leg of the horizontal caliper ruler, press down the left mouse button and drag the mouse. 5. To change the Y axis pixel number: place the mouse pointer on the lower leg of the vertical caliper ruler, press down the left mouse button and drag the mouse. 6. After aligning the calipers, type the distance that the calipers cover in the X Distance box and the Y Distance box. '7 The Image Pixels shows the number of pixels between the legs of the caliper 4 The Institute for Microstructural Analysis Principlesand Practiceof Automatic Image - Analysis - Chapter 5 . I,,')I,! ruler. 8. Cal Factor shows the Calibration Factor that changes according to the value in the Image Pixels window. 9. The Aspect Ratio shows the calculated aspect ratio of the image pixels. 10. Click OK. Figure 5-2 X-V Objective Calibration ~Buehler Ltd. 1999 8 The Institute for Microstructural Analysis Principles and Practice of Automatic Image AnalySjs Cha ter 6 . Chapter 6 Ima e Detection GrayscaleThreshold Color Threshold Grayscale Threshold Thresholding is the way of representing ranges of grayscale values with different color bitplanes. The bitplanes are used for binary operations and measurements. Two ways to start Threshold: 1. In the OperationBuilderwindow,expandGrayscale Threshold branch node. Doubleclick Threshold leaf node. 2. Or click in the Enterprise toolbar. Pause, Threshold Pause, threshold is used to stop execution of a routine until thresholding is done. This is useful when there is variance in the grayscale ranges from one image to the next. To Pause, threshold: 1. In the Operation Builder window, expand Grayscale Threshold branch node. Double click on Pause, threshold. Or 2. Click in the Enterprise toolbar. Threshold Window The main thresholding dialog box displays a gray scale histogram of the image. The gray scale histogram has an X-axis from 0-255. The left end of the scale is zero (black) and the right end is white (255). The height or V-axis of the graph represents the number of pixels at each gray level. To detecta bitplane: Click one of the bitplane color buttons. @Buehler Ltd. 1999 The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImageAnalysis Chapter6 Place the cursor over the Upper Limit slider. Press and hold the left button of the mouse, and then drag the slider toward right over the desired gray scale range selecting the lightest gray area of interest. Placethe cursor over the Lower Limit slider. Press and hold the left button of the mouse, and then drag the slider toward right over the desired gray scale range selecting the darkest gray area of interest. If other bitplanes are desired, repeat the above steps. Clicking on another bitplane will cause the Lower Limit slider to be placed immediately to the right of the Upper Limit of the last detected bitplane. The Sticky Threshold option always moves the Lower Limit slider and the Upper Limit slider one video level to the right of the previous position of the Upper Limit slider. Thus, it is guaranteed that different bitplane colors will not overlap, or no video level will be included in more than one bitplane. If 2 bitplanes overlap the color will change to an olive green overlap color. There are three different Y scalings LInear, Sq. Root and Log. Select the appropriate scale to best view the distribution of the grayscale pixels. CBueNer Ltd. 1999 2 The Institute for Microstructural Analysis ~ Principles and Practiceof AutomaticImageAnalysis Chapter6 Color Threshold Color HLS Threshold . . . Hue Luminance Saturation Hue is the wavelength of light reflected from or transmitted through an object. More commonly, hue is identified by the name of the color such as red, orange, or green. Hue is measured as a location on the color wheel and is expressed as a degree between 0° and 360°. Luminance is the relative lightness or darkness of the color and is usually measured as a percentage from O%(black) to 100%(white). Saturation is the strength or purity of the color. Saturation represents the amount of gray in proportion to the hue and is measured as a percentage from 0% to 100% (fully saturated). On the color wheel, saturation increases as one approach the edge of the wheel, and saturation decreases as one approach the center. Scaling allows the user to choose the way the histogram will be displayed. Linear gives the least detail. Sq. Root gives a more detailed histogram. Lastly, Log gives the maximum detail. Undo will remove the last accumulative sample selected by the user. It is only active when an accumulative sample is selected. ~ I _8- 1~lw~t J ~~ ~ 19~25J ~r~ I-.~ PAccumulatrve Sample I~K @Buehler Ltd. 1999 I 3 The Institute for Microstructural Analysis Principles and Practice of Automatic Image Analysis This color wheel displays the range for Hue and Saturation that the user has selected (see below). The user can also configure the range of Hue and Saturation by using the mouse and clicking on the edges and pulling them until it reaches the necessary ranges for Hue and Saturation. Both inner and outer edges can be used for configuration. This threshold enables the user to set the range of intensity for the grayscale. @BuehlerLtd. 1999 . The Institute for Microstructural Analysis Chapter 6 ~ri-!:'-ciplesand Practk:e of Automatic Image Analysis Color HLS Threshold Color HLS Thresholding is the way of representing ranges of color values with different color bitplanes. The bitplanes are used for binary operations and measurements. Two ways to start the Color HLS Threshold: 1. In the Operation Builder window, expand Color Threshold branch node, Double click Color HLS Threshold leaf node. 2. Or CliCk. in the Enterprise toolbar. Pause, Color HLS Threshold Pause, Color HLS Threshold is used to stop execution of a routine until saturation, hue, and luminance thresholding is done. This is useful when there is variance in the color ranges from one image to the next. To Pause, Color HLS Threshold: 1. In the Operation Builder window, expand Color Threshold branch node. Double click on Pause, Color HLS Threshold. Or 2. @Buehler Ltd. 1999 Click II in the Enterprise toolbar. 5 The Institutefor Microstructural Analysis Chapter7 Principles and Practice of Automatic Image Analysis Cha ter 7 Ima e Modifications Image Clarifications . BinaryImage Modifications Image Clarifications The image clarifications are often called "image enhancements". The purpose of these clarifications is to: . enhance the image to improve its visual appearance Or . modify the characteristics of an image to allow the image analyzer to discriminate better between the features The image clarifications available can be separated in three different categories: 1. Look-up-tables Look-up-tables (or LUT's) are widely used in image processing. This is a tool to adjust brightness and contrast levels of the image as well as the gamma function. The transformed pixel values are used in the subsequent thresholding process. 2. Arithmetic operations between images A typical example of an arithmetic operation between images is the removal of "electronic noise". This noise can be generated by video cameras when faint images are viewed, or when the reflectivity or light transmission characteristics of an object or specimen are very low. A very efficient way to remove noise is by "averaging" two or more successive image frames. By adding multiple frames, followed by an averaging, the random noise is eliminated. 3. Neighborhood Transformations Neighborhood transformations, also called convolutions, are commonly used as gray image clarifications. The principle is that the pixel value is modified in relation to its immediate neighbors. This neighborhood averaging replaces each pixel with the average of itself and its neighbors. This is often described as a kernel operation. The neighborhood sizes are typically squares from 3x3, 5x5, 7x7, etc. The goal is an increase in local contrast at the phase boundaries. Often times, the use of a neighborhood transformation results in a more narrow gray scale distribution of a given phase, making the subsequent thresholding or detection process easier. C>BuehlerLtd. 1999 The Institute for Microstructural Analysis Principles and Practice of Automatic Image Analysis Binary Image Modifications After thresholding, the different phases in the image are represented by different bitplane colors. It is possible that more than one phase or feature of interest was detected by the same bitplane color because of a similar gray level range. A Binary operation is the process that separates and classifies features within the same bitplane, based on morphology or size. Below is a listing and short description of all the binary image modification commands available in the image analysis system: Boolean Boolean operations perform logical functions between bitplanes. lOR: combines two source bitplanes to form a single destination bitplane. AND: takes common parts of 2 source bitplanes and integrates into a destination bitplane. I XOR: excludes common parts of 2 sources bitplanes and combines remainder of 2 source bitplanes into destination bitplane. I NOR: excludes both bitplanes from entire image and puts rest of image into destination bitplane. I NAND: excludes common parts of 2 source bitplanes from common image and places remainder of image into destination bitplane. [4 , Cc: NXOR: combines common parts of 2 source bitplanes with remainder of image and puts combined bitplanes into destination bitplane. ~ MINUS: takes the part of Source #1 that is not in Source #2, and puts it into destination bitplane. This function is useful for removing overlap from 2 bitplanes (see coating thickness example, chapter 9). Boundary Fill This function smoothes the object boundary in the selected bitplane by expanding and shrinking the objects. The resulting objects have shapes similar to the original objects. This function is defined as dilation with a square kernel followed by erosion with a cross kernel. It is normally used to substantiate the boundaries before inverting. Border Eliminate This function eliminates all objects on the chosen bitplane that touch edge of image frame. Chord Size This function eliminates objects that cannot completely cover a specified test box of X by Y size. It is useful to eliminate elongated thin objects such as scratches. Do not use this function on grain boundaries, as thin boundaries will disappear. An object that does not cover the whole test box can be transferred to another bitplane or discarded (to none). See Trap. @BuehlerLtd. 1999 2 The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImageAnalysis I ' , Chapter7 Clear This function erases selected bitplane pixels over the entire image. Clear Outside Frame This function erases selected bitplane pixels outside the Process Frame. Close This is dilation followed by erosion. Both operations will be done with same kernel and same number of cycles. You can select number of cycles. Kernel choices include square, hexagon, octagon, horizontal, vertical, and cross. This function is used to fill holes or connect particles in a phase without significantly altering the original shape. Convex Hull This function rounds off edges and fills in crevices at the edge of particles. It can dilate any concave object until it becomes convex (turning a C into a circle). Copy This function copies the selected source bitplane into another bitplane. It does not alter the information in the source bitplane. The source bitplane is turned off and destination bitplane is turned on at the end of the operation. Delta Convex Hull This function sends the changes of the selected bitplane (added pixels) from convex hull to selected bitplane. This is the difference from original image (before convex hull) and the image after applying convex hull. Delta Dilate This function sends the changes of the selected bitplane (added pixels) from dilate to selected bitplane. This is difference from original image (before dilate) and image after applying Dilate. Delta Erode This function sends the changes of the selected bitplane (subtracted pixels) from erode to selected bitplane. This is difference from original image (before erode) and image after applying Erode. Delta Prune This function sends changes of the selected bitplane (subtracted pixels) from prune to selected bitplane. This is difference from original image (before prune) and image after applying Prune. Delta Thicken This function sends changes of the selected bitplane (added pixels) from thicken to selected bitplane. This is difference from original image (before thicken) and image after applying Thicken. Delta Thin This function sends changes of the selected bitplane (subtracted pixels) from thin to selected bitplane. This is difference from original image (before thin) and image after applying Thin. Dilate This function grows objects with the chosen kernel (square, cross, octagon hexagon, horizontal, or vertical) by adding a layer to the objects. Erode This function shrinks objects with the chosen kernel (square, cross, octagon hexagon, horizontal, or vertical) by peeling a layer off the objects. Fill This function fills holes (undetected areas) that are totally enclosed by the detected bitplane Feature Size Transfer Feature Size Transfer performs feature separation/sorting based on a chosen characteristic (shape, length etc.). You can send particles that meet characteristic criteria to any bitplane. You also have the ability to undertake a single specification (>5 microns) or multiple (>5 and <2). For multiple you can select from AND and OR. For each specification you can choose >, <, =, ~,2;. The calculate limits button computes highest and lowest value for the chosen characteristic in the selected bitplane. Intersect Transfer This function transfers any object in the source bitplane which overlaps or crosses any object on the intersector bitplane to a destination bitplane. The entire object is transferred not just the portion which overlaps/crosses. The transferred objects are eliminated from the source bitplane. The intersector bitplane is not affected. All three bitplanes are still on at the end of the operation. Invert This function inverts the source bitplane. Every pixel that was on is turned off and vice versa. Open This function is erosion followed by dilation with the same kernel and the same number of cycles for each operation. Open generally smoothes the contour of an object breaks narrow "necks" between particles and eliminates thin protrusions, as well as removing small features. Prune This function trims short thin lines by a selected number of cycles. Choosing "to end" will trim lines until triple points are reached. Any pixel that has only 1 neighboring pixel is removed with each cycle. This function only works after thinning. It is available for square and hexagon kernels. The user selects the number of cycles or to end. Radial Grid This function generates a circular grid using the selected bitplane. Grid, radial dialog box allows user to type in number of circles and diameter lines to form the grid. The maximum grid is 100 circles and 100 diameters. Seed This function finds the single line or point at the center of an object. Sometimes objects like a dumbbell will have more than one seed point. This function is available with square and hexagon kernels. @Buehler Ltd. 1999 4 The Institute for Microstructural Analysis ~ Separate This function automatically separates objects that touch each other. Separate algorithm looks for valleys or necks, which may connect adjacent objects. Square Grid This function generates an X by Y grid using selected bitplane. The frame option will put a border around the outside of the image or process frame. The maximum grid is 100 X 100. Thicken This function adds layers of pixels to the objects in the selected bitplane for the selected number of cycles. Unlike dilate, thicken will not connect objects that did not touch prior to applying the thicken function. You can select number of cycles or to end. To end dilates objects until 1 pixel is left between objects. Both square and hexagon kernels are available. Thin This function removes layers of pixels on the objects in the selected bitplane for the selected number of cycles. Unlike erode, thin will not remove objects but rather stops when the object is 1 pixel wide or reduced to a point. Square and hexagon kernels are available. The user can select the number of cycles or to end. Trap This function eliminates objects that completely fit inside a specified test box of X by Y size. Particles that do not fill the test box can be sent to either another bitplane or discarded (to none). See Chord Size. ~Buehler Ltd. 1999 5 The Institute for Microstructural Analysis ~ andPracticeof Automa~ImageAnatyais i ~ Feature Measurements Field Measurements Area Fraction Plane Measurement Area Fraction Sample Measurement Feature Measurements The feature algorithms are described below in alphabetical order: 45 Feret ThisDegree function is the measurement of the diagonal feret. The angle between the feret and positive X-axis is 45 degrees. The result is provided as a number in the chosenunits of measurement. 90 Feret ThisDegree function is the measurement of the vertical feret. The angle between the feret and positive X-axis is 90 degrees. The result is provided as a number in the chosen units of measurement. 135 Degree This functionFeret is the measurement of the diagonal feret. The angle between the feret and positive X-axis is 135 degrees. The result is provided as a number in the chosen units of measurement. Aspect Ratio is the longest feret in a detected object divided by shortest feret in This function the same object. This is the same as LengthIWldth. The result is provided as a dimensionlessnumber. CBuehierLtd. 1999 . ASTM E112 This function gives the ASTM grain size number as defined by ASTM specification E112 for each individual grain. The result is provided as a dimensionless number. Average Feret This function is the average measurement of all the ferets of an object. The result is provided as a number in the chosen units of measurement. Breadth This function is also called Orthogonal feret that measures the feret perpendicular to the angle of the longest feret (length). The result is provided as a number in the chosen units of measurement. Circular Diameter This function determines the equivalent circular diameter of each individual object in a selected bitplane in the image. The area of the object is measured first, and then an equivalent circle with the same area is calculated. The diameter of this circle is the equivalent circular diameter of the object. The result is provided as a number in the chosen units of measurement. Compactness This function is defined as 4*pi * Area / (Convex perimeter). provided as a dimensionless The result is number. Convex Perimeter This function is an approximation of the perimeter of the particles that have concave edges. Convex perimeter is a rubber band around all ferets in a particle. The result is provided as a number in the chosen units of measurement. Density This function is also called "Photographic Density." This is the measurement of the average gray level values of all detected pixels in the detected bitplane in the image divided by 255. If the image is completely black, which means no light can come from it, the density of this image is 0%. If the image is completely white, which means all light can come from it, the density of this image is 100%. Length This function gives the longest measured feret. The result is provided as a number in the chosen units of measurement. Orientation This function gives the angle at which the longest feret occurs. This function is used to give an angle of the particle relative to the X-axis on the monitor screen. The result is provided in degrees. Perimeter It is the distance around each individual feature or object. It gives the perimeter for each particle. The result is provided as a number in the chosen units of measurement. @Buehler Ltd. 1999 2 The Institute for Microstructural Analysis Principles and Practice of Automatic Image Analysis i"c.. Chapter 8 Roughness This function gives the roughness for each object in the selected bitplane in a range of 0 to 1.0.Roughness is defined as the ratio of the convex perimeter, which is a rubber band around all feret, to the perimeter. If there is no concave at the edge of the particle, the roughness of this particle is 1.0. The result is provided as a dimensionless number. Spherical Diameter Spherical diameter is defined as the (circular diameter * 1.22474). This measurement gives the equivalent spherical diameter of each individual object in a bitplane in the image. The result is provided as a number in the chosen units of measurement. Sphericity This function measures the sphericity of each object in a selected bitplane in a range of 0 to 1.0.Sphericity is defined as (4 * PI * area) I (perimeter). If the shape of the particle is a perfect circle, the sphericity of this particle is 1. The more bumps a particle has, the lower sphericity value. The result is provided as a dimensionless number. String Length This function is used to measure the actual (curved) length of objects that are thin, curved and elongated. The result is provided as a number in the chosen units of measurement. String Width This function is used to measure the actual (curved) width of objects that are thin, curved and elongated. The result is provided as a number in the chosen units of measurement. Width Width is the shortest of the measured feret. It measures the minimum feret diameter of each particle in a selected bitplane. The result is provided as a number in the chosen units of measurement. Principlesand Practiceof AutomaticImageAnalysis! i... " Cha X Centroid This function gives the Centroid X coordinate with respect to the origin of the image or Guard Frame (if used). Y Centroid This function gives the Centroid Y coordinate with respect to the origin of the image or Guard Frame (if used). Field Measurements Field measurements are performed on a whole field or image, providing the sum of the individual measurements in a field of view. Statistical information is only generated if multiple fields are analyzed. This can point out microstructural variations within different fields of a specimen. Below is a listing of the field measurements in alphabetical order: Anisotropy This measurement is defined as the mean horizontal chord divided by the mean vertical chord for all detected particles in the selected bitplane. The result is a dimensionless ratio such as 1.6. Area This function gives the total area of all objects or phases of interest for a particular bitplane. The result is provided as a number in the chosen units of measurement. Area Percent This measurement is defined as the area of a particular bitplane divided by the area of the field being measured. The result is expressed as percentage such as 45.3%. ASTM E112 This measurement gives the average ASTM grain size of the metal microstructure using the average chord length as defined by ASTM specification E112. The result is provided as a dimensionless number such as 10.69. Average Area This measurement is the area of all the particles in the selected bitplane divided by the total number of particles. It is the mean of feature area measurement. The result is expressed as a number in the chosen units of measurement. Circular Diameter This measurement gives the average equivalent circular diameter of all the objects in a bitplane of an image. The average area of each object in the field is measured first, and then an equivalent circle with the same area is determined. The diameter of this circle is the equivalent circular diameter of the object. The result is expressed as a number in the chosen units of measurement. @Buehler Ltd. 1999 4 The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImage Analysis Chapter 8 Count This measurement gives the total number of objects in the selected bitplane within the measurement frame. The result is provided as a dimensionless number such as 67. Note: Holes in particles must be filled before measurement or a miscount may occur. Density This function is also called "Photographic Density." This is the measurement of the average gray level values of all detected pixels in the detected bitplane in the image divided by 255. If the image is completely black, which means no light can come from it, the density of this image is 0%. If the image is completely white, which means all light can come from it, the density of this image is 100%. Horizontal Intercept This measurementgivesthe total numberof horizontalinterceptsof horizontal scan lineswith the particlesin the selectedbitplane.The result is expressedas a dimensionlessnumbersuch as 252. Horizontal Mean Chord This measurement determines the approximate width of the selected objects. It is defined as the total horizontal chord lengths divided by the total number of horizontal intercepts. The result is provided as a number in the chosen units of measurement. Number/Area This measurement is defined as the total number of objects divided by the total field area within the measuring frame. It determines the number of objects per unit area. When using this measurement, it is better to use large area units of measurement (i.e., millimeters or inches) rather than small units (i.e., microns). If small units are selected, values for this measurement, such as 0.0013 particles per unit area can be the result. The result is provided as a number in the chosen units of measurement. Perimeter This measurement determines the sum of the total individual object perimeters (length of the edges around an object) in a particular bitplane. The result is provided as a number in the chosen units of measurement. Spherical Diameter Spherical diameter is defined as the circular diameter, multiplied by 1.22474. This measurement gives the total equivalent spherical diameter of all the objects in a bitplane of an image. The result is provided as a number in the chosen units of measurement. Vertical Intercept Verticalinterceptgives the number of vertical intercepts of vertical lines with the particles in the selected bitplane. The result is expressed as a dimensionless number such as 577. Vertical Mean Chord This measurement determines the approximate height of the selected objects. It is defined as the total vertical chord lengths divided by the total number of vertical intercepts. The result is provided as a number in the chosen units of measurement. ~Buehler Ltd. 1999 . The Institute for Microstructural Analysis Principlesand Practiceof AutomaticImageAnalysis @BuehlerLtd. 1999 Chapter8 6 The Institute for Microstructural Analysis ~~ andpractk:8of Automatic ImageAnalysis I ~ Area Fraction Plane Measurement This function measures the area percentage of a particular bitplane relative to the measurement of the total area of a reference bitplane. This function allows the selection of a specific bitplane as the reference for measuring the percentage. Although only 1 reference bitplane is chosen, as many source bitplanes as required may be selected. In other words, Area % = (source bitplane area)/(reference bitplane area). 100%. Results are always reported as a percentage. Area Fraction Sample Measurement Chapter 9 Glossary of Ima~e Analysis Terms Analog Video Signals Electronic signals that contain a continuous or gray tone representation of the image. Analysis A measurement or series of measurements made upon a material to determine quantitative data (number of particles, area percentage) of the constituents in that material. Applications Utilization of a piece of equipment to perform a specific measurement on a particular material. Typical image analysis applications are measurement of ASTM grain size on steels, percentage of phases in aluminum, porosity in powdered metals, etc. Area A measurement of the amount of surface area of particles of interest on a two dimensional image. Area % The total area of all detected particles within the frame, divided by the area of the measurement field. Automated Image Analysis A technologyrelyingon special equipmentfor extracting quantitativeinformationfrom images. Boundary A closed line or contourwhich defines the area occupied by a feature or field. Breadth The shortest tangent-to-tangent distance in any direction across a feature. An orientation independent measurement. Brightness Incorrect term for luminance, used to describe intensity of light generated on the Image Monitor screen. With reference to an image, this term describes the intensity of light reflected or transmitted through an image or its components. Also the video level on a range from black to white) of the fill-in color of detected particles. Calibration The process by which image analysis units of measurement (Pixels) are related to real units of measurement (inches, microns) at a given magnification. The process is accomplished by comparing a feature with a known value in "machine units of measurement," such as area in picture. Points. See also Calibration Factor and Calibration Slide.. Calibration Factor The numerical relationship between measured values in "machine units of measuremenf' (pixels) and real world units of measurement (inches, microns, etc.) determined at a particular magnification or magnifications. ~ BuehlerLtd. 1999 1 The Institute for Microstructural Analysis PrinciplesandPracticeof Automatic Ima e Analysis Chapter9 Calibration Slide A glass slide that has, deposited in chrome, features of accurately known dimensions. The calibration slide is certified and therefore makes the calibration NIST traceable. CCD Acronym for Charged Coupled Device, frequently used to incorrectly describe all solid state cameras. See also .:SQllQ State Camera. Chord A line segment which joins two points on the boundary of a feature. Also "intercept." Contrast The degree of difference in gray levels between features and background, or between different portions of an image. Generally, the degree of difference in brightness between the lightest and darkest areas in an image. Count A determination of the number of features or feature components in the image being analyzed. Data Manipulation Use of commercially available data base software to manipulate stored raw data in methods not available in normal Omnimet operation. Data Processing The mathematical operations performed on raw data to determine statistical analysis such as average and standard deviation. Derived Measurement Measurements which are computed using the results from basic measurements and constants combined arithmetically. Example: Average Area. DetectedFeature A feature or constituent of interest that is selected for measurement by adjustment of the threshold settings to include or span the particular range of gray levels of the feature. DetectedImage The image representing the detected features, displayed by activating fill-in button. Detection First step in automatic image analysis for isolating an image, on the basis of their gray level value, features of interest to be measured. Diameter The length of a straight line that passes through the center of a circle and divides it in half. Display Also Monitor, TV Monitor, Screen. A module containing a CRT (Cathode Ray Tube) for displaying the image being analyzed, the results of such analysis, and other information. @ Buehler Ltd. 1999 2 The Institute for Microstructural Analysis PrinciplesandPracticeof Automatic 1m e Analysis Cha ter 9 Duplex Consisting of two distinct phases or two distinct sizes of particles, or ranges of sizes of particles. Elongation A term for describing a geometric shape of a feature which is not symmetrical about a center point. Also ratio of longest dimension to breadth. Elongation Ratio The ratio of longest dimension to breadth of an elongated feature. Also knows as "aspect ratio." See Elongation Feature As related to image analysis, a definable component, substructure, or particle in an image. Example: the nucleus of a cell or a grain in metal. Feature Measurement The individual measurement of each detected feature in the field of view. Feret' Diameter The tangent-to-tangent dimension of a feature in a specified angular direction. Field One complete vertical scan across the field of view. See also Field of Measurement. Field Area A measurement mode in which the sum of the areas of all detected features in the field of measurement is determined. Field Measurement The aggregate total of a measurement of all detected features in the field of view. Field of Measurement The area on the Image Monitor Screen bounded by the fixed or variable frame, within which measurements are made. Field of View The whole picture visible on the Image Monitor Screen. Field ProjectedLength A measurement mode in which the sum of the projected lengths of all detected features in the field of measurement is determined. Grain With reference to metallurgy, a bounded area containing metal atoms arranged in a preferred orientation forming a part of the microstructure. Also sometimes used to designate particle. Grain Size The size of a grain. The grain size can be measured in dimensionless numbers according to ASTM specification E112, or measure in microns in diameter. Gray Level Also "gray scale value," a term related to optical density defining the brightness of a particle or area as a value lying on a scale between a minimum (black=O) and a maximum (white=225). @ Buehler Ltd. 1999 3 The Institute for Microstructural Analysis Gray Level Detection Also "thresholding," "threshold detection." An electronic technique for selection of features or areas in an image based on the differences between their brightness (optical density) and that of background or other features. Hole With reference to image analysis, an enclosed nondetected area in a feature. Horizontal Chord A continuous length in the horizontal direction across a detected feature coinciding with the direction of the scan Illumination uniformity See Shading Image A two dimensional representation of an object. For the purpose of image analysis, a reproduction in a plane of the spatial brightness distribution in the specimen. Image Analysis A technique for extraction of a quantitative data from images, usually with the objective to analyze some property of the specimen represented by the image. The term conventionally implies automatic image analysis. Intercept The portion of a scan line which crosses a detected feature, thereby forming the chord joining points of intersection between the scan line and feature boundaries. Intercept Count A count relatedto the total numberof interceptionsof scan lines with all the detectedfeatures in the field of measurement. Interface An assembly (usually electronic) which serves as an adapter or communication link between modules, devices or systems. Example: Teletype interface in the Processor. Linearity The ability of a Scanner to accurately and repeatedly reproduce the size and shape of a particle, no matter where it occurs in the field of measurement. Longest Dimension The maximum tangent-to-tangent dimension of a feature in any direction. The measurement is independent of orientation. See also ~ Diameter. Macroviewer An optical device for forming images and permitting analysis of large specimens, photographs, etc. Magnification To enlarge in fact or appearance. Also the numerical value of such magnification. Measurement As related to image analysis, quantitative determination of size, shape, optical density, orientation, etc. of features in the image being analyzed. @ Buehler Ud. 1999 4 The Institutefor Microstructural Analysis Principles - and Practice of Automatic Image - Analysis -c Chapter 9 Measurement Field See~ QfMeasurement. Menu On the Image Monitor, a stationary vertical list of entries from which the user can select one option at a time as part of the analysis process. This selection may result in another menu being presented to the user. Micrometer Abbreviated as mm, one millionth part of a meter, one thousandth part of a millimeter or (approximately) 40 millionths of an inch. In Omnimet Systems, a unit often used to express dimensions of microscopic features or structures. Micron The unit of length equal to one thousandth of a millimeter. 1000 micron = 1 mm. Microscope, Optical An optical device to generate magnified images for analysis or observation. Microscope, Scanning An electronic scanning device for Scanning Electron Microscope (SEM) generation of highly magnified images for analysis or observation. Microscope, Transmission An electronic device for Transmitting Electron Microscope (TEM) generation of images of extremely high magnification. Monitor A television module containing a CRT and operation controls for displaying the image of the sample to be analyzed, menus to select the derived analysis, the results of such analysis and other information. Numerical Aperture (N.A.: With reference to microscope objectives, the term which defines the amount of light collected by the objective. The higher the N.A. number, the greater the resolution of the objective. Outline A line pattern generated on the screen of the Image Monitor and superimposed on the image to coincide with the boundaries of detected features. The purpose of outlines is to indicate the features which are detected.to the operator. Oversize In size distribution, the number of particles whose measurement is larger than the upper limit of the size distribution selected by the operator. Percent Area A computed measurement in which the Total Area of the detected features is divided by the Total Area within the Measurement Frame. Perimeter The length of a feature's boundary. An orientation independent measurement. @ Buehler Ltd. 1999 5 The Institute for Microstructural Analysis Principlesand Practiceof Automatic Ima e Analysis C ter 9 Phase With respect to materials (particularly metals): a homogeneous, physically distinct component present in a non-homogeneous matter. For example, the pearlite phase occurring in some low carbon steels. Picture Point Also "pixel," "picture element", "sampling point". In image analysis, the pixel is the building block with which the image is composed and is the basic unit of measurement, from which all other measurements are calculated. Power Supply An electronic unit which supplies electrical power to the appropriate electronic circuits. Power supplies usually derive their power from electric power lines. Precision Agreement of a group of measurements which, exhibit a low standard deviation; the measurements mayor may not be accurate. Projected Length (Vertical) The vertical Feret' Diameter plus the tangent-to-tangent distance of all holes and concavities with the feature. Resolution In image analysis, the term usually means the size of the smallest elements in an image that the instrument can detect, measure or count. It also relates to the sharpness of a focused image. See Picture.EQ!n!. Screen The visible front portion of the CRT in the monitor, coated with phosphor, on which the picture is generated. ScrollingList On the Menu/Data Monitor, a rolling vertical list of entries from which the user can select options. Rolling is controlled by the "Up" and "Down" arrow keys. Selection of an option may result in another menu being presented to the user. The "measurement" list and "recall configuration" lists are examples of scrolling lists. Sensitivity In image analysis, the minimum amount of light in an image required by the video camera to accurately convert that image to an electronic signal. Shade of Gray A term expressing a fractional and detectable difference between two gray levels Shading A term adapted from television technology which describes distortions in gray level which occur due to uneven illumination and variation in scanner sensitivity across the field of view during the conversion of a specimen's image into video signals for the purpose of image analysis. See Shadina Corrector Shape A spatial form, independent of size, of a feature. @ Buehler Ltd. 1999 6 The Institute for Microstructural Analysis Princi les andPracticeof Automatic Ima e Anal sis Cha ter 9 Shape Factor A number (usually derived as a ratio of two different types of measurements) which is sensitive to the shape of a feature. The shape factor is non-dimensional. Example: the ratio of longest dimension divided by breadth is a shape factor called elongation. Type shape factors are elongation ratio, circularity, sphericity. Size Distribution A tabular or graphic method for summarizing results of feature measurements, in which the results are arranged in accordance with the size and number of features in each size range. Solid State Camera The camera consists of a fine matrix array of photosensitive picture elements. The output of the individual picture element is proportional to the intensity of the portion of the image falling upon it. Electronically sampling each photo element in a regular pattern converts the sample image into an electronic video signal. Specimen Also "Sample." A small quantity representative of the material, matter, or object to be analyzed. Example: metallurgical specimen, biological specimen. Status Window On the Menu/Data Monitor, a rectangular area displaying various selections made by the operator from the menus, such as: calibration factor, measurements, data processing of measurements, frame exclusion, etc. Stereology Science of interpreting two dimensional measurements of a three dimensional object. Tangent-to- Tangent Defines the Feret' Diameter of a distance across a feature in a given direction; e.g.., the maximum distance between parallel tangent to the boundary of a feature. Threshold A control used by the operator to select the range of gray levels (Video Levels) which will be detected and measured. The threshold control is usually adjusted so that the range of detected gray levels (Video Levels) spans or encompasses the gray levels (Video Levels) of the particles to be measured. Typical modern image analysis systems have two such controls, an Upper Threshold and a Lower Threshold. Trinocular On a microscope, a third port, near the binocular eyepieces. to which a scanner can be attached to see the image of the sample formed by the microscope. Undersize In Size Distribution, the number of particles whose measurement is smaller than the lower limit (graph starting point) of the size distribution selected by the operator. @ Buehler Ltd. 1999 7 The Institute for Microstructural Analysis PrinciplesandPracticeof Automatic ImageAnalysis; Chapte Variable Frame Same as fixed frame, but with dimensions and location controlled by the operator. Video Term pertaining to electronic signals representing images of sections of systems, which carry or process such signals. See Analoa Video and Binarv Video. Video Level See Gray Level. @ Buehler Ltd. 1999 8 The Institute for Microstructural Analysis