Interactive liver tumor segmentation from ct scans using support vector classification with watershed.

In this paper, we present an interactive method for liver tumor segmentation from computed tomography (CT) scans. After some pre-processing operations, including liver parenchyma segmentation and liver contrast enhancement, the CT volume is partitioned into a large number of catchment basins under watershed transform. Then a support vector machines (SVM) classifier is trained on the user-selected seed points to extract tumors from liver parenchyma, while the corresponding feature vector for training and prediction is computed based upon each small region produced by watershed transform. Finally, some morphological operations are performed on the whole segmented binary volume to refine the rough segmentation result of SVM classification. The proposed method is tested and evaluated on MICCAI 2008 liver tumor segmentation challenge datasets. The experiment results demonstrate the accuracy and efficiency of the proposed method so that indicate availability in clinical routines.