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

2016 ◽  
Vol 36 (2) ◽  
pp. 413-426 ◽  
Author(s):  
Yogita K. Dubey ◽  
Miind M. Mushrif ◽  
Kajal Mitra
Keyword(s):  
Rough Set ◽  
Fuzzy Clustering ◽  
Mr Images ◽  
Intuitionistic Fuzzy ◽  
Brain Mr Images ◽  
Intuitionistic Fuzzy Clustering

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

2019 ◽  
Vol 31 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Yogita Dubey ◽  
Milind Mushrif ◽  
Kajal Mitra
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
White Matter ◽  
Rough Set ◽  
Gray Matter ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Cystic Component ◽  
Brain Mr Images

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

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