3D Statistical Shape Models of Patella for Sex Classification

This paper proposes a new sex classification method from patellae using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was collected and CT scanned. After the CT data was segmented, a set of features was automatically extracted, normalized, and ranked. These features include geometric features, moments, principal axes, and principal components. A feature vector of 45 dimensions for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the patellar feature vectors according to sex. Different classification methods were compared. Classification success ranged from 83.77% average classification rate with labeling using fuzzy C-Means method (FCM), to 90.3% for linear discriminant function (LDF) analysis. We obtained results of 96.02% and 93.51% training and testing classification rates (respectively) using feedforward backpropagation Neural Networks (NN). These promising results encourage the usage of this method in forensic anthropology for identifying the sex from incomplete skeletons containing at least one patella.