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

Abstract

Abstract. 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.

Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking

“…It has an inherent ability to handle the indeterminate information like the noise included in images [17][18][19][20][21] and video sequences. Until now, NS has been successfully applied in many areas [22].…”

“…For the computer vision research fields, the NS theory is widely utilized in image segmentation [17][18][19][20][21], skeleton extraction [23] and object tracking [24], etc. Before calculating the segmentation result for an image, a specific neutrosophic image was usually computed via several criteria in NS domain [17][18][19][20][21]. For object tracking, in order to improve the traditional color based CAMShift tracker, the single valued neutrosophic cross-entropy was employed for fusing color and depth information [24].…”

A New Algorithm for Improving Basic Model Based Foreground Detection Using Neutrosophic Similarity Score

“…It has an inherent ability to handle the indeterminate information like the noise included in images [10][11][12][13] and video sequences. Till now, NS has been successfully applied into many computer vision research fields, such as image segmentation [10][11][12][13] and skeleton extraction [14]. Algorithms for clustering also employ the NS theory.…”

“…For instance, to improve the performance of the method of c-means clustering, indeterminate factors were considered by using a NS model [15]. For the applications of the NS theory in image segmentation domain, a specific neutrosophic image was usually computed [10][11][12][13] to conquer the noise interference. In addition, many kinds of NS criteria for handling the noise disturbance are proposed [10][11][12][13]16].…”

“…For the applications of the NS theory in image segmentation domain, a specific neutrosophic image was usually computed [10][11][12][13] to conquer the noise interference. In addition, many kinds of NS criteria for handling the noise disturbance are proposed [10][11][12][13]16]. In [16], multiple criteria were proposed for solving the thresholding problem on a static image, and a neutrosophic image was built before calculating the optimal threshold .…”