AUTOMATED TECHNIQUES IN ANTHROPOMETRY USING A THREE DIMENSIONAL LASER ...

48 Pages·2007·2.42 MB·English

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 ............................................................................................1...0..
. ...................................................................................................................
2 METHODS 11
2.1. Recognition of Surface Landmarks ......................................................................1..1.
2.2. Shape Analysis .....................................................................................................2..0
. .....................................................................................................................
3 RESULTS 29
3.1 . Landscape Marker Recognition ..........................................................................2..9..
3.2. Wrist Identification .............................................................................................3..3. .
. ..........................................................................
4 DISCUSSION AND CONCUSIONS 36
4.1 . Fudicial Landmark Location ...............................................................................3..6..
4.2. Wrist Identification .............................................................................................3.7.. .
4.3. Summary ...........................................................................................................3..8.. ..
. ........................................................................................................
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. ............1 3
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
sca n... ......................................................................................................................1..6. .
Figure 5. The four two-dimensional texture maps after filtering. Bright points indicate
located fudicials. ..................................................................................................,.1..7..
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. .............................................................-.1. 8
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 ..........................................2..2
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). .....................................................................................................2..3..
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). ..........................................2..4
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 ......2 6
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) ..........................2. 7
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. ..................................................................................3.0.. .
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). ........................................................3..2
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. ................3 5
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