Neutrosophic Sets and Fuzzy C-Means Clustering for Improving CT Liver Image Segmentation

In this paper, an improved segmentation approach based on Neutrosophic sets (NS) and fuzzy c-mean clustering (FCM) is proposed. An application of abdominal CT imaging has been chosen and segmentation approach has been applied to see their ability and accuracy to segment abdominal CT images. The abdominal CT image is transformed into NS domain, which is described using three subsets namely; the percentage of truth in a subset T, the percentage of indeterminacy in a subset I, and the percentage of falsity in a subset F. The entropy in NS is defined and employed to evaluate the indeterminacy. Threshold for NS image is adapted using Fuzzy C-mean algorithm. Finally, abdominal CT image is segmented and liver parenchyma is selected using connected component algorithm. The proposed approach denoted as NS-FCM and compared with FCM using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on nonuniform CT images.