A novel computer-aided diagnosis method of nasopharyngeal carcinoma based on magnetic resonance images

Abstract

A novel computer-aided method based on magnetic resonance images (MRI) was proposed for the early detection and diagnosis of nasopharyngeal carcinoma (NPC). A local Chan-Vese level-set model, which integrated the maximum interclass-variance method with the Chan-Vese model, was built to detect foci with unobvious boundaries. For each of the suspected foci, 26 features, including suspected focus texture, shape, and grayscale characteristics, were extracted, and then classified with a support-vector-machine (SVM) classifier. The method was tested with 289 brain images of 48 patients with nasopharyngeal carcinoma and 33 healthy adults, which obtained an average successful-diagnosis rate of 90.74%.