Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine

Abstract

A two-class support vector machine (SVM)-based image segmentation approach has been developed for the extraction of nasopharyngeal carcinoma (NPC) lesion from magnetic resonance (MR) images. By exploring two-class SVM, the developed method can learn the actual distribution of image data without prior knowledge and draw an optimal hyperplane for class separation, via an SVM parameters training procedure and an implicit kernel mapping. After learning, segmentation task is performed by the trained SVM classifier. The proposed technique is evaluated by 39 MR images with NPC and the results suggest that the proposed query-based approach provides an effective method for NPC extraction from MR images with high accuracy.