Delineation and classification of liver cancer using level set method in CT images

The paper proposes a modified approach of delineation and classification of two different types of liver cancers viz. Hepatocellular Carcinoma (HCC) and Metastatic Carcinoma (MET) from different slices of computed tomography (CT) scans images. A combined framework of reorganization and extraction of region of interest (ROI), texture feature extraction followed by texture classification by different machine learning approaches has been presented. Initially, adaptive thresholding has been applied to segment the liver region from CT images. Level set algorithm has been used for detecting the region of cancer tissues. In the classification stage, the delineated output lesions have been extracted with 38 features to build up the dataset. Two machine learning classifiers, support vector machine (SVM) and random forest (RF), have been used to train the dataset for correct prediction of cancer classes. Ten-fold cross-validation has been used to evaluate the performance of two classifiers. The efficiency of the proposed algorithm is tested in terms of accuracy, where the RF classifier achieved a higher accuracy of 95% compared to SVM classifier of 87%. The experimental result proves the superiority of RF classifier compared to SVM classifier with level-set features. © 2017 National Taiwan University.