Liver segmentation based on SKFCM and improved GrowCut for CT images

Abstract

Accurate liver segmentation is an essential and crucial step for computer-aided liver disease diagnosis and surgical planning. In this paper, a new coarse-to-fine method is proposed to segment liver for abdominal computed tomography (CT) images. This hierarchical framework consists of rough segmentation and refined segmentation. The rough segmentation is implemented based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is performed based on the proposed improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint based on fuzzy c-means clustering (FCM) algorithm, which can reduce the effect of noise and improve the clustering ability. The IGC algorithm makes good use of the continuity of CT series in space which can automatically generate the seed labels and improve the efficiency of segmentation. The proposed method was applied to segment the liver for the whole dataset of abdominal CT images. The performance evaluation of segmentation results shows that the proposed liver segmentation method is accurate and efficient. Experimental results have been shown visually and achieve reasonable consistency.