Adaptive liver segmentation from multi-slice CT scans

In this paper, an adaptive method was proposed to segment the liver from computed tomography (CT) images. Four sets of CT images were segmented by a binary classification method, support vector machine (SVM), after supervised thresholding and K means clustering. A leave-one-out method was applied to compare with the adaptive method. The result shows that the adaptive method can more accurately identify the class of the pixel with evidently lower false negative volume fraction than that of the leave-one-out method.