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Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering

Paper ID Volume ID Publish Year Pages File Format Full-Text
5153 343 2016 14 PDF Available
Title
Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering
Abstract

Intuitionistic fuzzy sets and rough sets are widely used for medical image segmentation, and recently combined together to deal with uncertainty and vagueness in medical images. In this paper, a rough set based intuitionistic fuzzy c-means (RIFCM) clustering algorithm is proposed for segmentation of the magnetic resonance (MR) brain images. Firstly, we proposed a new automated method to determine the initial values of cluster centroid using intuitionistic fuzzy roughness measure, obtained by considering intuitionistic fuzzy histon as upper approximation of rough set and fuzzy histogram as lower approximation of rough set. A new intuitionistic fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity inhomogeneity and noise in brain MR images. The results of segmentation of proposed algorithm are compared with the existing rough set based fuzzy clustering algorithms, intuitionistic fuzzy clustering and bias corrected fuzzy clustering algorithm. Experimental results demonstrate the superiority of proposed algorithm.

Keywords
Segmentation; Magnetic resonance; Brain image; Rough set; Intuitionistic fuzzy set; Fuzzy c-means clustering
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 36, Issue 2, 2016, Pages 413–426
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us