Document Detail

Patella sex determination by 3D statistical shape models and nonlinear classifiers.
MedLine Citation:
PMID:  17482786     Owner:  NLM     Status:  MEDLINE    
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.
Mohamed Mahfouz; Ahmed Badawi; Brandon Merkl; Emam E Abdel Fatah; Emily Pritchard; Katherine Kesler; Megan Moore; Richard Jantz; Lee Jantz
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Publication Detail:
Type:  Journal Article     Date:  2007-05-07
Journal Detail:
Title:  Forensic science international     Volume:  173     ISSN:  1872-6283     ISO Abbreviation:  Forensic Sci. Int.     Publication Date:  2007 Dec 
Date Detail:
Created Date:  2007-10-23     Completed Date:  2007-12-31     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7902034     Medline TA:  Forensic Sci Int     Country:  Ireland    
Other Details:
Languages:  eng     Pagination:  161-70     Citation Subset:  IM    
Biomedical Engineering Department, University of Tennessee, 301 Perkins Hall, Knoxville, TN 37996, United States.
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MeSH Terms
Aged, 80 and over
Computer Simulation*
Discriminant Analysis
Forensic Anthropology / methods*
Imaging, Three-Dimensional
Middle Aged
Models, Statistical*
Neural Networks (Computer)
Patella / anatomy & histology*,  radiography
Sex Characteristics*
Tomography, X-Ray Computed

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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