Paper Info
Title
Neutrosophic Sets and Fuzzy C-Means Clustering for Improving CT Liver Image Segmentation.
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.
Year
DOI
Venue
2014
10.1007/978-3-319-08156-4_20
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014)
Field
DocType
Volume
Pattern recognition,Sørensen–Dice coefficient,Segmentation,Binary image,Fuzzy logic,Image segmentation,Artificial intelligence,Connected component,Jaccard index,Cluster analysis,Mathematics
Conference
303
ISSN
Citations 
PageRank 
2194-5357
9
0.47
References 
Authors
4
4
Name
Order
Citations
PageRank
Ahmed M. Anter1447.37
Aboul Ella Hassanien21610192.72
Mohamed Abu ElSoud3193.59
Mohamed F. Tolba48027.94