Mahfouz Mohamed, Badawi Ahmed, Merkl Brandon, Fatah Emam E Abdel, Pritchard Emily, Kesler Katherine, Moore Megan, Jantz Richard, Jantz Lee
Biomedical Engineering Department, University of Tennessee, 301 Perkins Hall, Knoxville, TN 37996, United States.
Forensic Sci Int. 2007 Dec 20;173(2-3):161-70. doi: 10.1016/j.forsciint.2007.02.024. Epub 2007 May 7.
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.
性别判定是从骨骼遗骸中识别个体身份的关键步骤之一。骨骼的大多数元素都已用于性别估计的判别函数分析，但关于髌骨的研究较少。本文提出了一种利用新型自动特征提取技术从髌骨进行性别判定的新方法。从田纳西大学的威廉·M·巴斯捐赠骨骼收藏中收集了228个髌骨的数据集（95例女性和133例男性），并对其进行了无创高分辨率计算机断层扫描（CT）。在对CT数据进行分割后，自动提取、归一化并排列了一组特征。具有表面平滑功能的分割过程可将来自附着点骨赘的噪声降至最低，最终使我们的方法能够区分髌骨形态的差异。这些特征包括几何特征、矩、主轴和主成分。然后为每个受试者构建了一个维度为45的特征向量。使用了一组统计和监督神经网络分类方法对髌骨特征向量的性别进行分类。神经网络等非线性分类器已在先前的研究中用于分析多个医学诊断问题，包括定量组织表征和自动染色体分类。本文比较了不同的分类方法。分类成功率从使用模糊C均值（FCM）聚类步骤的标签得到的平均分类率83.77%，到线性判别分类（LDC）的90.3%不等。使用前馈反向传播神经网络（NN），我们分别获得了96.02%和93.51%的训练和测试分类率。这些使用新开发特征和非线性分类器应用的有前景的结果，鼓励在法医人类学中使用这些方法，以便从保留至少一个髌骨的不完整骨骼中识别个体的性别。
