Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation

Ahmed M. Anter, A. E. Hassenian, M. A. Elsoud, and M. F.Tolba, "Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.

Date Presented:

22-24 June

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 segmen-
tation 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 per-
centage 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 de ned and employed to evaluate the indeterminacy. Threshold
for NS image is adapted using Fuzzy C-mean algorithm. Finally, ab-
dominal 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 Coecient.
The experimental results demonstrate that the proposed approach is less
sensitive to noise and performs better on nonuniform CT images.