Automated Techniques in Anthropometry Using A
AUTOMATED TECHNIQUES IN ANTHROPOMETRY USING A
THREE DIMENSIONAL LASER SCANNER
A Thesis Presented to
The Faculty of the
Fritz J. and Dolores H. Russ College of Engineering and Technology
Ohio University
In Partial Fulfillment
of the Requirement for the Degree
Master of Science
by Erick A. Lewark
August, 1998 ACKNOWLEDGEMENTS:
The author would like to thank all persons involved in the writing of this thesis.
Special thanks must go to Dr. Joseph Nurre, my academic and professional advisor, Amy
E. Lewark, my wife (the source of much of my inner strength and ambition), and the
Defense Logistics Agency's Apparel Research Network for funding research efforts in this emerging field. TABLE OF CONTENTS:
1. INTRODUCTION ...... 1
1.1. History of Anthropometry ...... 2
1.2. Present Applications ...... 4
1.3. Review of Scanning Technology ...... 6
1.4. Review of Anthropometric Software ...... 9
1.5. Problem Statement ...... 10
2 .METHODS ...... 11
2.1. Recognition of Surface Landmarks ...... 11
2.2. Shape Analysis ...... 20
3 . RESULTS ...... 29
3.1 . Landscape Marker Recognition ...... 29
3.2. Wrist Identification ...... 33
4 . DISCUSSION AND CONCUSIONS ...... 36
4.1 . Fudicial Landmark Location ...... 36
4.2. Wrist Identification ...... 37
4.3. Summary ...... 38
6. BIBLIOGRAPHY ...... 40
7 . APPENDIX ...... 43 LIST OF FIGURES:
Figure 1. The Cybenvare WB4 Whole-Body Scanner, with subject. (Photo courtesy of
Cybenvare, Inc.) ...... 7
Figure 2. Flow diagram of algorithms used to locate fudicials in 3-D scan data...... 13
Figure 3. Four two-dimensional texture maps generated in a typical Cybenvare WB4
scan. (Participant's identifying features were removed to assure anonymity.)...... 14
Figure 4. Matrix used in filtering texture maps. A = 1/13, B = -1136. This matrix is
convolved with the original texture maps to enhance the appearance of fudicials in a
scan...... 16
Figure 5. The four two-dimensional texture maps after filtering. Bright points indicate
located fudicials...... ,17
Figure 6. Two dimensional representation of neigbor finding routine used to group
marker candidates. Distance to every point remaining in scan is computed for each
point, points within a distance r are classified as neighbors. The union of the
intersecting sets yields marker candidates...... -18
Figure 7. Flow chart of procedures used to find location of the wrist in human body scan
data...... 2 1
Figure 8. Front and side views of data captured from a 3-D scan file. The body shown is
in a position ideally suited for successful wrist location ...... 22 Figure 9. Scan data segmented into the six major anatomical sections: right and left
arms and legs, torso and head, as performed by the segmentation software developed
by Nurre (1 997)...... 23
Figure 10. Side-by-side comparison of an original limb cross-section (A) with the cross-
section after processing by an outer-hull algorithm (B)...... 24
Figure 11. Gaussian PDF (left) and its first-order derivative (right). These functions
serve as the basis for the discrete filter used in computing Gaussian derivatives ...... 26
Figure 12. Plot of hull circumferences versus cross-section level in arm. Notice the peak
around level 50 (thumb), and the trough at about level 100 (wrist) ...... 27
Figure 13. Unprocessed scan with luminance data collected by the Cybenvare WB4
scanner (left). On the right, the same scan is shown after processing, with fudicials
labeled as the white spheres...... 30
Figure 14. Processed scan demonstrating problems in recognizing fudicials. Note how
the nose is interpreted as a fudicial...... 3 1
Figure 15. Processed scan showing unrecognized markers. Arrows indicate missed
markers on shoulder (top) and thigh (bottom)...... 32
Figure 16. Histogram of the difference between user- and software-determined wrist
height (z-axis)...... 33
Figure 17. Magnified view of a segmented scan with wrist located (white line)...... 34
Figure 18. Wrist misidentified at a position superior to the anatomical wrist...... 35 1. INTRODUCTION
Most people have been sized for an article of clothing at some point in their lives and are thus familiar with the measurement techniques used by tailors. Similar measurements of the human body are made in anthropometry, but they are performed with much more precision. While the goal of tailoring is to size a person for a garment, anthropometry serves to broaden our knowledge about the human form.
One may ask why quantification and identification of human morphology is necessary when these differences are readily visible. As explained by Richtsmeier et al.
(1 992), "First, the precision gained through quantification is important. Second, although differences between forms may appear obvious, the significance of the difference cannot be ascertained by the naked eye.. . Third, some of our most interesting questions entail comparison of comparisons in the form of ontogenetic and phylogenetic sequences. A comparison of comparisons is not possible without morphometric analysis."
Until recently, all measurements of the human body were collected by a human observer in a manual fashion. Advances in laser scanner technology, however, have initiated the development of automated systems which acquire measurement data about the human body directly from surface scans. Such systems incorporate both hardware and software solutions to many of the challenges faced when attempting to quantify the features of an irregular object like the human form. Nevertheless, one of the most complex issues remains to be solved: the automated identification, registration, and measurement of the three dimensional human body scan data collected by these systems. In this thesis, two practical automated methods are presented. The first takes advantage of classical image processing technique to detect and identify externally placed reference markers. The second uses 3-D shape analysis methods to locate the wrist of a human subject in scan data.
1.1. History of Anthropometry
In the past, measurements have been gathered using mechanical devices, such as the segmometer devised by Carr et al. (1993) to measure distances on the body. Other methods of anthropometry have been employed with regards to the quantification of body surfaces. First, body surface measurements have been estimated from physical dimensions such as body length and mass. Clearly, there is room for significant error with this approach. In the late 1800's, anthropologists devised instrumentation to
"integrate" the surface of the body from directly measured points. Before this method was invented, anthropologists had drawn triangles on the surface of the body and had calculated the surface area of the body as the sum of the areas of the triangles. An even more interesting method employed the use of a removable material which was placed over the surface of the body and then measured. Finally, the skinning of cadavers has also been used to quantify body surface area (Brozek et al. 1987). The major drawback of these methods is that they are time consuming and inaccurate.
In general, the limitations of physical anthropology are dependent on the nature of the human body. For example, Gordon and Bradtmiller (1992) found that soft tissue compression which results from physical measurement techniques results in unreproducible error. In addition, the position of the subject can contribute to measurement error. Research has found that some measurements are best made when a subject is in a recumbent position, while others are best performed while the subject is standing (such as waist circumference which may be exaggerated when a subject is sitting) (Williamson et al. 1993). Regardless of position, movement of both the person making the measurements and the subject can contribute to error as well (Jones & Rioux
1997).
Interobserver error in physical anthropology has shown to be significant in a number of studies, making it impossible for measurements to be made by multiple researchers over the course of one study. Bennett and Osborne (1986) found that eight separate observers who each made sixty-three measurements on sixteen different subjects produced significant error in those measurements. Most interestingly, measurement error was greater on female subjects. Whether this error is due to soft tissue compression or psychological factors is not known. Nonetheless, Gordon and Bradtmiller (1992) found that error could be decreased by having one person take a limited number of measurements across all subjects. Another study done by Himes (1989) calculated the number of times one measurement would have to be repeated to achieve the greatest reliability for a particular measurement using the Spearman-Brown Prophesy. Most notably, eleven separate measurements of the lateral thigh skinfold would have to be made to achieve reliability of 95 percent. Other measurements, such as chest circumference and wrist breadth need to be made only four times each to achieve the same reliability. A few measurements, however, such as weight, stature and calf circumference can be made with 95 percent reliability in only one measurement trial.
Clearly, the speed and accuracy of physical anthropology is limited. Moreover, physical anthropological measurements can cost from $50 to $500 per subject, which hinders the ability to produce large sets of data for population studies (Jones & Rioux
1997). Scanning has the benefit of not causing soft tissue compression during data collection, thereby increasing validity of the measurements taken in this manner. The reliability of the system is expected to be higher than that of physical measurements, but is yet to be proven in research. Also, laser scanning technology is less costly because it can take less than 30 seconds per subject.
1.2. Present Applications
Medical research is already making use of data collection through automated means in the Visible Human Project from the National Library of Medicine (Vannier &
Robinette 1995). In this study, one male and one female cadaver were cryosectioned into transverse slices every few millimeters. The data from each slice was stored using several techniques including Computer Tomography (CT), Magnetic Resonance Imaging
(MRI), and standard photography. This information has been computationally reconstructed into the human form providing valuable volumetric and surface data for the complete human body. These methods of data collection are not as efficient for producing surface measurement data as the techniques are more focused towards the analysis of the internal structures of a human subject. If a description of the external surface of an individual is desired, a 3-D surface scanner is a more feasible alternative.
Scanner-assisted anthropometrics have been implemented to quantify changes in facial morphology through the course of plastic surgery (Vannier & Robinette 1995).
Linney et al. (1997) have also discussed the use of morphometrics in three-dimensional surface imaging for quantifying facial deformity while providing a record of the correction after surgery. Such technology may even allow an individual to see the results of corrective surgery in three dimensions before any invasive techniques are performed.
Related techniques can be used to improve the way prosthetics are fit to amputees.
Currently, judgment of the fit of prosthetics relies on patient reports of comfort and other subjective measurements made by the designer. Several attempts at fit may be made before the correct adjustments allow for comfort. Plaster of Paris is used commonly to make a 3-D mold of the remaining limb on which the prosthetic will attach; however, this technique involves the compression of soft tissue to the point where proper attachment is difficult to achieve. Three-dimensional scanning allows the surface of that region to be carefully scrutinized and analyzed for the proper fit, without contact. (Vannier &
Robinette 1995)
Scanning technology has also been applied to the field of human factors engineering (ergonomics) for such products as anti-gravity suits, face masks, automobiles, computer keyboards, and work spaces. For this field, anthropometric surveys are used to develop man-models which help quantify the average body size and shape of a potential user of an ergonomically-designed device. Man-models can then be used to test the fit of that item (Rioux 1997).
The application of 3-D human body scanning with which most people are probably familiar is its use in the entertainment industry for computer animation. Movies such as Terminator II, Batman and Robin and The Abyss implemented scanning technologies in many of their special effects. However, the uses of this technology by
Hollywood, while entertaining, are limited in scope. What may be described as the most practical use of this technology actually benefits everyday people.
The fashion industry has taken advantage of scanning to make clothing specialized for the individual. The possibility exists that within several decades, clothing stores will use whole-body scanning systems to tailor clothes for each customer. At this time, scanning technology is being developed to fit uniforms to military personnel; however, as soon as it has evolved into a reliable and cost-effective tool, one will likely see whole-body laser scanners at local department stores.
1.3. Review of Scanning Technology
In the future, anthropometry will rely heavily on emerging imaging technologies such as three-dimensional laser scanners. Such electronic measurement devices can collect data in three primary ways: the first being mechanical devices which physically contact the individual. In the second, structured light is projected onto the subject. Using imaging techniques, the light is interpreted to become 3-D data. Finally, the third method relies on phase detection of a wave generating source which is reflected off the subject
(Deason 1997). Electronic measurements allow for reproducible measurements to be made without the problem of interobserver error. However, these types of measurements have problems of their own.
Figure 1. The Cybenvare WB4 Whole-Body Scanner, with subject. (Photo courtesy of Cybenvare, Inc.)
Depending upon the scanner system used, qualities of the captured data vary. For example, Comer et al. (1992) evaluated Macaque skull data captured by the Polyhemus
3Space Digitizer and determined that this particular scanner had no significant difference in error of resolution between the x, y and z coordinate axes. However, more advanced scanners like the Cybenvare WB4 Whole Body Scanner (Figure 1) collects data in a non- uniform fashion for the three coordinate axes. In other words, the resolution for the three axes differs. Greater control of resolution will eventually have to be developed in order to advance technology so it will be suitable for use in identification applications such as fingerprinting, and face recognition (Rioux 1997). Moreover, the resolution provided by modern scanners is not high enough to detect body regions covered in hair.
In addition, with the time it takes to scan one subject (17 seconds for the
Cybenvare WB4 Whole Body Scanner), natural body motion can produce inaccurate 3-D data collection (Jones & Rioux 1997; Comer & Hu 1997). Some scanners are even more sensitive to body motion, and thus controls are presently being developed to correct for this factor.
As in standard anthropometry, body position and posing of the human subject has an influence on the quality of measurements harnessed from 3-D data. In fact, since only one scan is taken of an individual at a time, correct posturing of a subject is crucial to maximize the number of collectable measurements (see study by Brunsman et al. 1997).
Failure to properly orient a subject prior to scanning can result in the occlusion of certain data points, particularly in areas such as the underarms and rise. Again, as in standard anthropometry, some measurements are better taken while a subject is sitting, and some are more accurate when a subject is standing (Williamson et al. 1993). For scanning technology, to best minimize the number of occluded data points, the most appropriate posture for a subject is standing. 1.4. Review of Anthropometric Software
Scanning technology allows for precise data collection in a short amount of time.
Moreover, the amount of data collected from just one scan is vast. In a Cybenvare scan, there are over 200,000 separate data points collected. Therefore, the next logical step in the development of this technology is to find user-friendly methods to handle such large amounts of data (Deason 1997).
For standard anthropometry, geometric morphometrics evolved in an attempt to reduce the amount of data used to describe the shape of the human body into shape quantification which is analyzable by traditional multivariate methods (Thompson &
Moggi-Cecchi 1993). However, new scanning technology has necessitated the development of algorithms to handle even larger amounts of data (a step back in the reduction of data). If data storage is of primary concern, point reduction methods are available, which eliminate vertices either uniformly or through adaptive cost-based functions (Nurre et al. 1995). Uniform reduction methods have the advantage of being easy to implement but may eliminate data in regions where high resolution is required.
Non-uniform techniques are much more complicated theoretically, but yield a more precise representation of the original scanned subject.
With all the data from scanned subjects stored, any number of traditional anthropological measurements could also be made. Pargas et al. (1997) have developed a software package, 3DM, to assist a user in measuring various aspects of scanned bodies.
3DM is primarily a tool for aiding a system user in obtaining measurements from scans manually. Tools such as arbitrary slice generation, hull approximations, and surface measurements are included in the package.
In software terms, however, it is desired to have a fully-automated product which is capable of recognizing and registering desired aspects of the human body form. For example, Nurre (1 997) has developed an algorithm which automatically performs anthropometric segmentation of the human body into head, arms, legs and torso regions, facilitating the identification of anthropometric landmarks. Continuing on this basis, the
Apparel Research Network (ARN) Project seeks to fully automate all measurements for tailoring clothes in the Cybenvare software package CyScanARN and eventually do the same for anthropometric measurements.
1.5. Problem Statement
The new work presented in this paper is incorporated into the ARN software for automated apparel fitting. To this end, the algorithms developed achieve two goals: first, they enable the automated location of externally placed fudicial markers on a subject for locating anthropometric landmarks; second, a shape-based method has been developed to find specific circumferences of the human body. Here, the wrist is located using this method. The implementation of both methods, the benefits of using these new tools, and the difficulties encountered in developing them are fully described in the following sections. 2. METHODS
Two software algorithms are described in this section. One algorithm is designed to locate and identify externally placed fudicial markers in 3D scan data collected by the
Cybenvare WB4 scanner. The second is a shape analysis tool which demonstrates the
capability of recognizing human features based on the geometry of scan data alone. Each technique demonstrates the merits of well-designed anthropometry software. Both
algorithms are currently fully integrated into the Cybenvare CyScanARN human body
analysis package. The current development platform is on Silicon Graphics, Inc. (SGI)
computers, while the software is written in C++ and TclITk. Use of the Open Inventor
Tool Kit, available from SGI, facilitates 3-D modeling and viewing. Interested parties may contact Cybenvare, Inc. for source code or further specifications.
2.1. Recognition of Surface Landmarks
The software presented in this section is used to locate and identify external marking devices topically applied to the subject before scanning is initiated. These
fudicial markers are %" diameter matte white self-adhesive stickers that are applied
directly to the skin or clothing of the subject being scanned. Markers are usually placed
in identifiable anthropometric locations such as the leftlright acromion (shoulder),
cervicale (base of neck), stylion (wrist), etc. The most important feature of these markers
is that they are highly reflective of laser light and are distinguishable visually from their
background in a completed scan. Currently, the landscape marker recognition algorithms are being used solely for the extraction of anthropometric and apparel measurements from the three-dimensional scan data produced by the Cybenvare WB4 scanner. The intention of this project to is to eliminate the use of fudicials in scanning as much as possible, since considerable time is taken for human application and removal of all the labels during a scanning session.
However, the simplification achieved when using fudicials is still a great asset when detailed measurements of a scan are to be recovered. With a minimal requirement of geometry-based algorithm development, new markers may be added or removed from the scanning procedure at any time allowing flexibility in any measurement determinations.
As long as the need for precision location of data points within a scan persists, a fudicial landscape marker approach will always be a viable method for extracting such information.
The marker recognition procedures can be broken into five discrete steps. First, texture maps containing luminance data are processed by a marker-enhancing filter. The texture maps are then reapplied to the 3-D data set. The data set is then thresholded to a level at which only markers remain visible and background features disappear. The final stage of the marker identification process is to group the points composing fudicials into marker candidates and identify their location heuristically. The entire process is diagrammed in Figure 2. I 1 2 Luminance Data Mapping to 1. Image Processing of 3-D Point Information I
3. Thresholding on Luminance 4. Grouping of Marker Candidates Level
5. Registration and Identification of Fudicials
Figure 2. Flow diagram of algorithms used to locate fudicials in 3-D scan data. The methodology described is dependent upon the format of the scan data collected from the scanner at run-time. Specifically, all operations were performed using data from the Cybenvare WB4 Whole Body Laser Scanner. In addition to the three- dimensional data, each scan head of the WB4 also collects an 8-bit gray-scale texture map containing the luminance information collected at each point of the scan from the intensity of the reflected laser light (Figure 3). The resulting images are the target of all image processing techniques.
Figure 3. Four two-dimensional texture maps generated in a typical Cybenvare WB4 scan. (Participant's identifying features were ren-loved to assure ar,onymity.)
A pattern recognition method based upon conventional two-dimensional image filtering is utilized in finding the highly reflective fudicials in the luminance data. The approach is based on finding the correlation between two images: one being the texture map collected by the scanner and the other being an ideal representation of what the marker should look like in a scan (Ritter & Wilson 1996). The general idea behind this method is to take a weighted difference between the luminance of a marker and the region composing its background. If, for instance, a highly luminescent object (with high intensity values) is located on a dark background (a region of low intensity), subtracting the average luminance values of the background from that of the object will result in a high positive value (correlation). Conversely, if a dark object were visible on a region of lesser reflective intensity, using the same subtractive approach would yield a negative correlation. Finally, if the object and background were approximately the same in brightness or intensity with very little contrast between them, then the derived result would be a correlation near zero. It is from this line of reasoning that the following algorithm is adapted.
The key step in devising an image processing filter to locate markers in a scan is to develop a filter kernel (matrix) that closely matches the size of the fudicials as they appear in the image map. When convolved with the texture maps, a well designed filter should enhance the brightness of the markers while reducing background intensity.
Inspection of several typical scan files reveal that a typical marker is expressed by a circular region with a diameter of approximately four to six pixels. In order for a matrix to have a true center element, its dimensions must be odd; thus, the inner region of the filter has been selected to have a diameter of five pixels. The final completed filter is then constructed as a 9 by 9 element matrix, leaving a two-pixel circular border for determination of the average background value. All values in the square filter not contained in either of these two regions is set to zero as a larger background region can possibly disrupt the performance of the filter.
Figure 4. Matrix used in filtering texture maps. A = 1/13, B = -1136. This matrix is convolved with the original texture maps to enhance the appearance of hdicials in a scan.
Matrix coefficients must be scaled such that the fudicial representation and background sampling regions are weighted equally in the filtering process. In this case the matrix coefficients were chosen so that the values contained in the elements of each region sum to one. The subtraction operation described earlier can be implemented into the filter design by making all of the coefficients of the background ring of the filter negative. The final filter design is shown in Figure 4.
A standard spatial approach is used to convolve the filter with the texture map.
Given an input image, a, and an output image, c, of equal dimensions, and the filter kernel, p, with dimensions (2m - 1) by (2n - I), the following definition applies (Ritter &
Wilson 1996): If the resultant matrix dimensions (x+k, y+l) ever exceed the bounds of image a, then a value of zero should be assumed for this term (in image processing, non-circular convolution is used). This formula calculates only one pixel value for the resultant image, c. In order to compute the entire filtered image, this computation must be repeated for every element of the output image up to the dimensions of the input image.
For the CyScanARN implementation, this procedure is repeated four times, once for each texture map which results in four new images in which marker candidates have been enhanced and the background luminance is reduced (Figure 5).
Figure 5. The four two-dimensional texture maps after filtering. Bright points indicate located fudicials. Once the filtered image map is generated, it is then mapped onto the three- dimensional point cloud contained in the scan data by built-in CyScanARN software available from Cybenvare. The result is a 3-D point cloud in which every point is assigned a luminance value in addition to its corresponding xyz information.
A thresholding routine is then applied to the scan eliminating all three- dimensional points with luminance values less than a given intensity value. The gray- scale value at which thresholding occurs is up to the discretion of the user, but it has been demonstrated in practice that a level between 40-60% of maximum intensity achieves the greatest results. With the typical number of hdicials used in test scans, only a few hundred points are retained from the original 200,000 after completion of the thresholding routine.
Figure 6. Two dimensional representation of neigbor finding routine used to group marker candidates. Distance to every point remaining in scan is computed for each point, points within a distance r are classified as neighbors. The union of the intersecting sets yields marker candidates. The points left in the scan represent all the fudicials that were detected by the filteringlthresholding process; however, they must be grouped before they may be recognized as markers (Figure 6). The grouping algorithm functions by a recursive, neighbor locating method, in which the distance between two points is used to determine whether or not they are part of the same marker. Cartesian point distances are calculated
and compared among neighbors to an epsilon value. Practice has shown that a distance of
one centimeter works effectively. If the distance between two points is found to be
smaller than the comparison value, the points are included in the same group. After
recursion terminates and marker candidate groups have been formed, each group is
classified by the number of points it contains. A group with a large number of points is unlikely, but those found may be identified and discarded quite easily. The same is true
for clusters containing very few points; such clusters are common and can often be
attributed as an artifact caused by the image processing techniques. Valid marker
candidates typically contain between 10-30 points, but the number may vary greatly.
Thus, in automated recognition systems it is best to rank the markers based on size, and
eliminate less promising groups until the number of groups remaining is equivalent to the number of fbdicials originally placed in the scan.
Markers are identified and registered heuristically based upon their exact location on the body (acromion, wrist, scye, etc.). This registration process is accomplished in
CyScanARN by using a series of bounding-box algorithms to identify the various body
segment locations (torso, right arm, left leg, etc.). Software which performs the registration function is available through Cyberware, Inc. and is contained in the
Cy ScanARN package.
2.2. Shape Analysis
The second method used to identify anthropometric landmarks in three dimensional whole body scans directly analyzes the geometry of the scan data without the help of physically placed fudicials. The algorithms presented here are used to geometrically identify the wrist of a scanned subject. With just a few modifications, this procedure could be used to identify a number of body locations including the ankle, calf, or elbow. The wrist is defined as the smallest circumference on the arm between the hand and elbow. Given this a definition, development of software tools for wrist identification is straightforward. The algorithm described below is more robust, however, to make allowances for data irregularities. The entire process of automated wrist location can be broken into several distinct stages: first, the subject must be postured correctly before scanning occurs to ensure validity of acquired results. Next, the completed scan must be categorized by a segmentation algorithm to identify major anatomical regions. The fully identified arm segment is then processed into slices and arm circumferences are found.
Finally, a Gaussian derivative technique is used to identify the minimum circumference of the arm which is identified as the wrist (Figure 7). 1. Subject Posturing 2. Body Segmentation and Scanning
1 3 Arm Cross-Sectioning 4. Convex Hull Application I I to Each Slice
5. Location of Minimum Circumference (Wrist)
Figure 7. Flow chart of procedures used to find location of the wrist in human body scan data. When using shape recognition algorithms on human scan data, certain details about the positioning and posturing of the scanned subject must be assumed. In the WB4 scanning environment, a subject stands and is positioned on a platform in the center of the scanning volume. Only after a subject is properly oriented and positioned is the scanner operated. Many posturing controls are in place to optimize the quality of the scans produced (Collier 1998). The current posturing protocol used in the ARN project includes provisions for the subject to be standing, with arms at his side but slightly elevated (40 cm.), thumbs forward, the feet should be separated (20 cm.) and parallel to one another. The subject should be facing forward and all jewelry must be removed.
Despite this protocol, the software described must be as flexible as possible to allow for difference in body morphology. The subject shown in Figure 8 is posed correctly for analysis.
Figure 8. Front and side views of data captured from a 3-D scan file. The body shown is in a position ideally suited for successful wrist location. The elimination of data extraneous to the region of concern is necessary to properly interpret scan data. The algorithm is designed to find the wrist of the subject given that an arm (right or left) has already been identified. Limb location is not trivial when precision is required. The human body segmentation software designed by Nurre
(1997) is an excellent means of dividing the body into its six major components (head, torso, right and left arms, and right and left legs; Figure 9). Other techniques may also be used to separate limbs from the rest of the body, as long as the data composing the region from the elbow to fingertips is isolated.
Figure 9. Scan data segmented into the six major anatomical sections: right and left arms and legs, torso and head, as performed by the segmentation software developed by Nurre (1 997). Many of the techniques for the registration of three dimensional data use algorithms which operate in two dimensions, thereby simplifying the procedures and computations needed to accomplish specific tasks. Processing individual two dimensional cross sections (or slices) of a data set allows a number of proven image processing concepts to be applied to otherwise daunting three dimensional information.
These slices are easily acquired by intersecting a single plane with the data set. For the wrist finder, it is necessary to manipulate the three dimensional data as a series of 2D slices parallel with the scanning platform. One simple algorithm for "slicing" the data set quickly is to sort the data in a 3D cloud via the coordinate component normal to the scanner platform. For the Cybenvare WB4 Scanner, the x-axis is perpendicular to the scanning platform. Once the data is sorted, collection of individual body slices based on a common x-value is a trivial process.
Figure 10. Side-by-side comparison of an original limb cross-section (A) with the cross- section after processing by an outer-hull algorithm (B). The data points in each slice are randomly arranged, and must be ordered to calculate meaningful measurements. An outer hull algorithm capable of determining the encompassing convex polygonal boundary of a 2-D data set is used (O'Rourke 1994).
The results of calculating the outer hull for a single slice are shown in Figure 10. The added structure of the hull easily yields a straight-line approximation to the circumference of the body slice. This procedure is repeated for every slice composing the arm.
At this point in processing, the 3-D data describing the limb of a subject has been reduced to a series of numbers describing hull perimeters of arm slices. From the definition for the wrist, determining the smallest perimeter in the lower region of the arm should yield the appropriate result. However, proceeding in such a manner does not yield a robust re-usable algorithm for application to other landmarks on the human body.
Slight irregularities in the shape of the body have been troublesome for algorithms using the minimum perimeter concept, usually triggering false identification of landmark features. Another problem associated with the above heuristic is it assumes that the quality of data collected is very consistent from slice to slice with no voids or gaps in the data. Such reliance on data quality can be dangerous, and is easily avoided by resorting to the maximum/minimum finding methods based on setting the first derivative of a function to zero.
Determination of the first and second derivative of a discrete data set can be accomplished by taking advantage of a Gaussian derivative filter. The probability distribution function (PDF) of a Gaussian random process is given by where aand m are the standard deviation and mean, respectively (see Figure 11). The derivative of any discrete data set may be found through the convolution by the Gaussian derivative hnction of the same order as the desired differentiation. That is
f,(x) = g(x) * f (4
where f(x) is the discretized function to be differentiated and n is the order of differentiation to be performed (Mokhtarian & Mackworth 1992). This process has the added effect of filtering the input data set for noise, which allows one to control the sensitivity of the differentiation by modifying the standard deviation of the distribution.
Figure 11. Gaussian PDF (left) and its first-order derivative (right). These functions serve as the basis for the discrete filter used in computing Gaussian derivatives. Using these formulas, a spatial convolution of the hull perimeter data set with a first order derivative of the Gaussian PDF yields an output function which is used to determine the exact location of the wrist. The slices closest to the zeroth position are the perimeters about the hand and fingertips while the larger values indicate the circumferences of the upper arm as it approaches the shoulder region (see Figure 12). In the unprocessed data set, the human eye can detect the peak value which represents the thumb, and the minimum which represents the wrist. The convolution operation helps to automate an algorithm for finding these exact locations.
Figure 12. Plot of hull circumferences versus cross-section level in arm. Notice the peak around level 50 (thumb), and the trough at about level 100 (wrist). Two heuristics have been tested to detect the appropriate minimum from the first derivative output with varying results. The first is to sample the hull data at each of the maxima/minima and take the smallest perimeter as the wrist. This technique is somewhat unpredictable and may still be plagued by data quality issues. The second is to find the first maximum in the data when transversing the data from fingertip to shoulder. This location is the thumb of the scanned subject. In posing a subject for scanning, a requirement is made that the thumb point forward and be separated from the hand. This produces a local maximum in the perimeter for convex hulls at the thumb level.
Once the estimated location of the thumb is determined, the wrist is found as the next local minima in the hull perimeter data set. In the CyScanARN software this information is then used in measurement calculations such as the sleeve inseam. Results from using the technique on 3-D human body scan data sets are presented in a following section. 3. RESULTS
The images displayed in this section graphically illustrate the effectiveness in using landscape markers and physical geometry to identify various regions of the body.
All computations were performed using a Silicon Graphics 1ndigo2workstation with 128
MB RAM and a 175 MHz R10000 Impact Processor.
3.1. Landscape Marker Recognition
Testing of the landscape marker recognition algorithm was performed on 62 subjects from whom data was collected at the U.S. Marine Corps Recruiting Center (San
Diego, CA). During scanning, five markers are placed on the subjects to indicate the scapulae (2), base of neck (I), thumb (I), and waist (1). All scans were first visually inspected by a user to count the total number of fudicials present in the scan data.
Occasionally, during scan time, markers fall off the subject or are occluded by body hair to the point where they could not be located visually, or by a marker recognition system.
Scans were then processed using the marker recognition algorithm. Landmark detection was rapid; only 5-10 seconds of processing time was needed to fully analyze each scan.
Error was calculated for all subjects, and for all markers, using the visually- counted number of markers as a basis for correctness. On any given subject, all visually located markers were automatically found successfully 58% of the time. In 40 % of the cases, one marker from the visually identified markers was missed. Two percent of the time, two or more markers were missed. Out of a total of 296 markers visually identified across all 62 subjects, 90.8% (269) were identified correctly. Usually, the misidentified marker was the one located on the waist. Body hair in this region frequently obscured the marker to the point where its luminance value was too low to be detected by the filter.
Occasionally, false positives were detected by the marker finding routine. These artifact markers were the result of highly reflective objects being worn by the subject like eyeglasses or jewelry. It should be noted that in this study, when there was disagreement between the actual number of markers and the number of markers found, images were not re-filtered at higher sensitivities, as would probably be done in practice.
Figure 13. Unprocessed scan with luminance data collected by the Cybenvare WB4 scanner (left). On the right, the same scan is shown after processing, with fudicials labeled as the white spheres. Figure 13 shows a human body scan containing markers before and after processing. This individual is wearing a different marker configuration than was used at the Marine Recruit scanning session. Small bright areas on the left side of the figure indicate the presence of fudicials. After complete processing, the identified markers are overlaid by spheres (Figure 12, right). Markers are shown in the following locations from top to bottom: right cervicale (side of neck), left and right acromion (shoulder), base of neck, scye (armpit), preferred waist, stylion (wrist), and left thigh.
Figure 14. Processed scan demonstrating problems in recognizing fudicials. Note how the nose is interpreted as a fudicial.
In some instances, facial features may be recognized as fudicial markers, as can be seen in Figure 14. Some jewelry may also be identified mistakenly as landmarked regions. Usually, misidentification of such items can be linked to abnormally high luminance values in those regions. At scan time, wearable reflective jewelry should be removed from the subject. At the time of analysis, artifact markers on the extremities can be easily discarded based on their location and proximity to true markers.
In other cases, when the bright marker is placed on a region of similar intensity, the marker is not recognized at all (see Figure 15). In this case, the clothing worn by the subject was light in color and somewhat reflective, so the luminance differential between the shorts on the subject and the marker placed on the shorts was not large enough to be detected by the filter. To remedy this problem, clothing worn should be darker than the marker.
Figure 15. Processed scan showing unrecognized markers. Arrows indicate missed markers on shoulder (top) and thigh (bottom). 3.2. Wrist Identification
Testing of the wrist locating software was performed on the same population of subjects as the marker identification software (62 U.S. Marine Recruits, from a 1998 scanning session). For each scan, a user manually identified the location of the wrist based on the location of the stylion in the scan. The stylion is the small nodular bony protrusion slightly superior to the wrist in the lower arm. Its level on the vertical axis (z- coordinate) was recorded. The wrist finding algorithm was applied to the same scan. The z-values obtained by both methods were then compared with the manual measurement serving as the control to calculate error.
Difference in Wrist (in meters)
Figure 16. Histogram of the difference between user- and software-determined wrist height (z-axis).
The mean difference between an observer-identified wrist and the software- identified wrist was .471 centimeters (o = 1.49) in the 62 subject sample. The mean offset corresponds to a software-identified wrist lower in the arm than the stylion.
Overall, the software located the wrist to within 2.98 centimeters of the user-defined location 96% of the time, and within 1.49 centimeters 64% of the time. The mean off-set is probably due to the fact that the user-defined wrist was based on the location of the stylion, whereas the minimum circumference is located just inferior to the stylion. The differences in observer and software-identified z-value for the wrist were relatively normally distributed, and are shown in Figure 16.
Figure 17 shows a magnified view of the arm of a processed scan with a wrist successfully determined. The wrist is identified with a white horizontal line.
Figure 17. Magnified view of a segmented scan with wrist located (white line). A misidentified wrist can be caused by any number of discrepancies between an actual and idealized scan. In Figure 18, the minimum circumference about the arm for this subject is not the wrist. This is probably due to the musculature of the subject's forearm. To remedy this sort of error, additional controls could be added to the algorithm to search for the stylion.
Figure 18. Wrist misidentified at a position superior to the anatomical wrist, 4. DISCUSSION AND CONCUSIONS
4.1. Fudicial Landmark Location
In the majority of test scans, recognition of fudicials was successful (90.8%) and reliable. The algorithm is now used as the basis for much of the anthropometric measurement extraction features of CyScanARN. When used in conjunction with a body segmentation algorithm such as the one described in Nurre (Nurre 1997), markers can be classified by their general body location (arm, leg, torso, etc.) and then locally identified by their relative position to one another. Once the markers are completely labeled in this fashion, actual measurements can then be extracted from the scan.
As stated earlier, some problems can arise in recognition of fudicials with this algorithm. In a few instances, jewelry, light-colored clothing, or certain body parts such as noses, earlobes or chins have been misidentified as landmarks. Also, markers placed on light colored clothing, or those occluded in the scan can be missed completely.
Usually, the misidentification can be determined and steps to correct the problem can be taken before measurements are calculated. For example, if the fudicial is missing, software will prompt the user for manual input.
As with all image processing algorithms, applications involving large images or filters tend to be computationally expensive tasks. Currently, the marker detection procedure requires 5-10 seconds per typical scan. Nearly all this time is consumed in the spatial convolution algorithm on the four separate texture maps. The use of a 2-D fast
Fourier transform (FFT) algorithm to convert both the filter and image to the frequency domain (where convolution operations become multiplication) could be used to hasten the computational algorithm. This feature has not been incorporated into the Cybenvare product since only small filters are used, and the performance gain would be minimal.
4.2. Wrist Identification
In scans obtained from a recent scanning session of U.S. Marine Recruits in San
Diego, the algorithm described performs adequately. Wrists are identified correctly in the majority of cases when compared to user-defined wrist location, and the measurements calculated are reasonable. Future validation studies are currently planned and will be based on a compilation of manual measurements taken by physical anthropologists during scanning sessions, including measurements calculated from strategically placed reference fudicials, as well as a number of statistical references. In these studies, the automated wrist-finding technique will be cross-checked with that of a professional anthropologist.
The original specifications of the algorithm call for a wrist measurement that is perpendicular to the medial axis of the arm. A medial axis is a best straight-line fit which represents the position of the arm. For example, one can approximate the medial axis of the lower arm as the line segment connecting the centroid of the elbow with the center of the wrist. This "stick" representation of the arm may be calculated for a limb in any position in the scan thereby eliminating many of the constraints required for per-scan posturing. As the horizontally-based method performed adequately for current fitting techniques for sleeve length, it was not deemed necessary to implement a medial axis finding algorithm until validation studies are completed. It is likely that medial axis finding routines will be added in the future.
The misidentified wrist shown previously was caused by improper positioning of the hand. Such breaks from the standard posing protocols will always plague the analysis of human body scan data. The human form is so flexible in shape that quantification techniques relying solely on physical geometry cannot account for all. Future algorithms can be adapted to rely on multiple landmarks for feature determination. For example, one possibility may be to analyze a number of specific features in parallel (elbow, thumb, scye, etc.) and perform error checking between them. Such an operation would be more complex, however, the robust nature of the approach could yield better results.
The utility of a wrist-finding algorithm, by itself, is a step forward in the overall scheme to fully automate anthropometrics and tailoring. The realization of full automation requires the development of many of such identification strategies, and as time progresses the summation of these techniques will make scanner based tailoring and anthropometry a reality.
4.3. Summary
The state of the classification, measurement, and analysis of the human form has come a long way since the inception of anthropometry. From the hand tools of the physical anthropologists to modem laser scanning systems, the goal has always remained to find efficient and precise quantification of the morphology of the human body. In the next few years, leaps in technology will undoubtedly drive the advancement of concepts like those presented in this paper to new ground. Many of the applications for precise three dimensional form measurement have not yet been realized, but their implementation will depend on reliable software techniques in addition to improved hardware devices.
The location of specific features in 3-D scan information often appears to be a simple task to observers outside the field. However, challenging innovations are required to analyze the data set; most operations must be considered carefully for efficiency and reliability. The techniques presented in this thesis for extracting measurement information from scan data are among the most efficient and robust algorithms available, and have advanced the technology of automated tailoring and anthropometry to new levels of accuracy and reliability. 6. BIBLIOGRAPHY
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Table 1. User- and software-defined wrist location and number of landmarks.