Robust cephalometric landmark identification using support vector machines

Abstract

A robust and accurate image recognizer for cephalometric landmarking is presented. The recognizer uses Gini support vector machine (SVM) to model discrimination boundaries between different landmarks and also between the background frames. Large margin classification with non-linear kernels allows to extract relevant details from the landmarks, approaching human expert levels of recognition. In conjunction with projected principal-edge distribution (PPED) representation as feature vectors, GiniSVM is able to demonstrate more than 95% accuracy for landmark detection on medical cephalograms within a reasonable location tolerance value.