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.

A hybrid supervised learning nasal tumor discrimination system for DMRI

“…Chen (2006) used SVM to delete the noise patterns from the training set. Zhou et al (2006) used a two-class SVM and a kernel trick to derive a new algorithm called the 'query-based two-class SVM classifier', which was better than traditional multilayer perceptron network-based classifiers. This SVM classifier is now available for radiologists to use as a pre-operative diagnostic tool.…”

“…Wang et al (2009) applied the Adaboost technique as the second stage to handle the most hard-to-classify samples to decrease both the training and testing errors. Zhou et al (2006) also noted that with current diagnostic imaging or radiation therapy, a radiologist or radiation therapist has to describe the scope of the tumor manually and then estimate the size of the tumor, which is quite tedious. This process takes a great deal of time and is highly dependent on the operator's skills and experience.…”

An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection

“…The results showed that one-class SVM is superior to two-class SVM. Zhou et al [12] use two-class SVM and kernel trick [13] to derive a new algorithm called query-based two-class SVM classifier, which is better than traditional MLP-based classifier [14]. It is available for radiologist to use as a preoperative diagnostic tool.…”

“…Ritthipravat et al [15] use region growing method and probabilistic map to find some candidate tumor region. Zhou et al [12] also mentioned that so far, in the current diagnostic imaging or radiation therapy, a radiologist or radiation therapists need to manually describe the scope of the tumor. Therefore, we propose this new algorithm to be able to detect tumor and draw the candidate regions automatically for radiologists.…”

Artificial Neural Networks in Cancer Recurrence Prediction

“…From the previous studies, ANNs were chiefly applied in cancer diagnosis and detection [1]. The applications include classification of benign and malignant tumors [2], classification of tumor extent in medical images [3] etc. This is different from cancer prognosis in which ANNs were relatively studied in recent years.…”