Literature DB >> 17482786

Patella sex determination by 3D statistical shape models and nonlinear classifiers.

Mohamed Mahfouz1, Ahmed Badawi, Brandon Merkl, Emam E Abdel Fatah, Emily Pritchard, Katherine Kesler, Megan Moore, Richard Jantz, Lee Jantz.   

Abstract

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.

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Mesh:

Year:  2007        PMID: 17482786     DOI: 10.1016/j.forsciint.2007.02.024

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  17 in total

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2.  Image analysis of pubic bone for sex determination in a computed tomography sample.

Authors:  Manuel López-Alcaraz; Pedro Manuel Garamendi González; Inmaculada Alemán Aguilera; Miguel Botella López
Journal:  Int J Legal Med       Date:  2013-08-25       Impact factor: 2.686

3.  Fully automatic initialization of two-dimensional-three-dimensional medical image registration using hybrid classifier.

Authors:  Jing Wu; Emam E Abdel Fatah; Mohamed R Mahfouz
Journal:  J Med Imaging (Bellingham)       Date:  2015-06-02

4.  AncesTrees: ancestry estimation with randomized decision trees.

Authors:  David Navega; Catarina Coelho; Ricardo Vicente; Maria Teresa Ferreira; Sofia Wasterlain; Eugénia Cunha
Journal:  Int J Legal Med       Date:  2014-07-23       Impact factor: 2.686

5.  Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach.

Authors:  David Navega; Ricardo Vicente; Duarte N Vieira; Ann H Ross; Eugénia Cunha
Journal:  Int J Legal Med       Date:  2014-09-04       Impact factor: 2.686

6.  Machine learning approaches for sex estimation using cranial measurements.

Authors:  Diana Toneva; Silviya Nikolova; Gennady Agre; Dora Zlatareva; Vassil Hadjidekov; Nikolai Lazarov
Journal:  Int J Legal Med       Date:  2020-11-11       Impact factor: 2.686

7.  Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements.

Authors:  Mubarak A Bidmos; Oladiran I Olateju; Sabiha Latiff; Tawsifur Rahman; Muhammad E H Chowdhury
Journal:  Int J Legal Med       Date:  2022-10-07       Impact factor: 2.791

8.  A comprehensive morphometric analysis of crista galli for sex determination with a novel morphological classification on computed tomography images.

Authors:  Erdal Komut; Murat Golpinar
Journal:  Surg Radiol Anat       Date:  2021-07-10       Impact factor: 1.246

9.  Three-dimensional morphology of the knee reveals ethnic differences.

Authors:  Mohamed Mahfouz; Emam Elhak Abdel Fatah; Lyndsay Smith Bowers; Giles Scuderi
Journal:  Clin Orthop Relat Res       Date:  2012-01       Impact factor: 4.176

10.  Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables.

Authors:  Efthymia Nikita; Panos Nikitas
Journal:  Int J Legal Med       Date:  2019-08-23       Impact factor: 2.686

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