Patella sex determination by 3D statistical shape models and nonlinear classifiers.
Sex determination is one of the essential steps in personal identification of an individual from skeletal remains. Most elements of the skeleton have been subjected to discriminant function analysis for sex estimation, but little work has been done in terms of the patella. This paper proposes a new sex determination method from the patella using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was amassed from the William M. Bass Donated Skeletal Collection from the University of Tennessee and was subjected to noninvasive high resolution computed tomography (CT). After the CT data were segmented, a set of features was automatically extracted, normalized, and ranked. The segmentation process with surface smoothing minimizes the noise from enthesophytes and ultimately allows our methods to distinguish variations in patellar morphology. These features include geometric features, moments, principal axes, and principal components. A feature vector of dimension 45 for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors. Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems, including quantitative tissue characterization and automated chromosome classification. In this paper, different classification methods were compared. Classification success ranged from 83.77% average classification rate using labels from a Fuzzy C-Means (FCM) clustering step, to 90.3% for linear discriminant classification (LDC). We obtained results of 96.02% and 93.51% training and testing classification rates, respectively, using feed-forward backpropagation neural networks (NN). These promising results using newly developed features and the application of nonlinear classifiers encourage the usage of these methods in forensic anthropology for identifying the sex of an individual from incomplete skeletons retaining at least one patella.
Mahfouz
Mohamed
M
Biomedical Engineering Department, University of Tennessee, 301 Perkins Hall, Knoxville, TN 37996, United States. mmahfouz@utk.edu
Badawi
Ahmed
A
Merkl
Brandon
B
Fatah
Emam E Abdel
EE
Pritchard
Emily
E
Kesler
Katherine
K
Moore
Megan
M
Jantz
Richard
R
Jantz
Lee
L
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D016002:Discriminant Analysis; D005260:Female; D018732:Forensic Anthropology / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010329:Patella / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D012727:Sex Characteristics; D014057:Tomography, X-Ray Computed