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.Keywords Brain MR image · Segmentation · Intervalvalued intuitionistic fuzzy set · Brain tumor · Clustering Communicated by V. Loia.

A Hybrid Fuzzy Clustering Approach for the Recognition and Visualization of MRI Images of Parkinson’s Disease

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“…Hung et al [129] [126] Torra et al [292] Thong and Son [284] Pelekis et al [234] Verma and Agrawal [296] Xu et al [316] Agrawal and Tripathy [3] Xu [313] Aliahmadipour and Eslami [5] Cai et al [44] Ananthi et al [12] Karthikeyani Visalakshi et al [142] Balasubramaniam and Ananthi [27] Todorova and Vassilev [288] Chen and Liu [57] Xu and Wu [317] Dubey et al…”

“…Recently, intuitionistic fuzzy clustering methods have been proposed by Agrawal and Tripathy,[3] , Aliahmadipour and Eslami [5] , Ananthi et al [12] , Chen and Liu [57] , Dubey et al [101] , Kacprzyk et al [137] , Prabu et al [244] , Shang et al [270] , Tripathy et al [293,294] and Zhou et al [351] .…”

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