Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework

Abstract

An improved support vector machine (SVM) framework has been developed to segment hepatic tumor from CT data. By this method, the one-class SVM (OSVM) and two-class SVM (TSVM) are connected seamlessly by a boosting tool, to tackle the tumor segmentation via both offline and online learning. An initial tumor region was first pre-segmented by an OSVM classifier. Then the boosting tool was employed to automatically generate the negative (non-tumor) samples, according to certain criteria. The pre-segmented initial tumor region and the non-tumor samples generated were used to train a TSVM) classifier. By the trained TSVM classifier, the final tumor lesion was segmented out. Tested on 16 sets of CT abdominal scans, quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than the OSVM and TSVM classifiers.