Open AccessGeneralised rough intuitionistic fuzzy c‐means for magnetic resonance brain image segmentation
Author(s) -
Namburu Anupama,
Samayamantula Srinivas Kumar,
Edara Srinivasa Reddy
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0891
Subject(s) - mathematics , fuzzy logic , pattern recognition (psychology) , image segmentation , membership function , artificial intelligence , segmentation , fuzzy set , centroid , cluster analysis , rough set , algorithm , computer science
Intuitionistic fuzzy sets (IFSs), rough sets are efficient tools to handle uncertainty and vagueness present in images and recently are combined to segment medical images in the presence of noise and intensity non homogeneity (INU). These hybrid algorithms are sensitive to initial centroids, parameter tuning and dependency with the fuzzy membership function to define the IFS. In this paper, a novel clustering algorithm, namely generalized rough intutionistic fuzzy c‐means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function. In this algorithm, each pixel is categorized into three rough regions based on the thresholds obtained by the image data by minimizing the noise. These regions are used to create IFS. The distance measure based on IFS eliminate's the influence of noise and INU present in the image producing accurate brain tissue segmentation. The proposed algorithm is evaluated through simulation and compared it with existing k‐means (KM), fuzzy c‐means (FCM), Rough fuzzy c‐means (RFCM), Generalized rough fuzzy c‐means (GRFCM), soft rough fuzzy c‐means (SRFCM) and rough intuitionistic fuzzy c‐means (RIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.