Paper Info
Title
A new fuzzy clustering algorithm for the segmentation of brain tumor
Abstract
This paper introduces a new method of clustering algorithm based on interval-valued intuitionistic fuzzy sets (IVIFSs) generated from intuitionistic fuzzy sets to analyze tumor in magnetic resonance (MR) images by reducing time complexity and errors. Based on fuzzy clustering, during the segmentation process one can consider numerous cases of uncertainty involving in membership function, distance measure, fuzzifier, and so on. Due to poor illumination of medical images, uncertainty emerges in their gray levels. This paper concentrates on uncertainty in the allotment of values to the membership function of the uncertain pixels. Proposed method initially pre-processes the brain MR images to remove noise, standardize intensity, and extract brain region. Subsequently IVIFSs are constructed to utilize in the clustering algorithm. Results are compared with the segmented images obtained using histogram thresholding, k-means, fuzzy c-means, intuitionistic fuzzy c-means, and interval type-2 fuzzy c-means algorithms and it has been proven that the proposed method is more effective.
Year
DOI
Venue
2016
10.1007/s00500-015-1775-5
Soft Comput.
Keywords
Field
DocType
Brain MR image, Segmentation, Interval-valued intuitionistic fuzzy set, Brain tumor, Clustering
Fuzzy clustering,Histogram,Fuzzy classification,Computer science,Fuzzy set,Artificial intelligence,Thresholding,Cluster analysis,Pattern recognition,Fuzzy logic,Algorithm,Membership function,Machine learning
Journal
Volume
Issue
ISSN
20
12
1433-7479
Citations 
PageRank 
References 
8
0.47
17
Authors
3
Name
Order
Citations
PageRank
V. P. Ananthi1222.45
P. Balasubramaniam291752.98
Kalaiselvi T391.88