Original and segmented simulated brain image by different algorithms: (a) axial view of original simulated T1-weighted brain image with INU=0 and 1% noise, (b) skull stripping simulated brain image, (c) manual segmented CSF, GM and WM images, (d) IIFCM algorithm, (e) IFCM algorithm, (f) FLICM algorithm, (g) EnFCM algorithm, (h) FGFCM algorithm, (i) FCM_S1 algorithm, (j) FCM_S2 algorithm, (k) ImFCM algorithm. The segmentation of brain magnetic resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. Intuitionistic fuzzy c-means (IFCM) algorithm, one of the variants of FCM, is found suitable for image segmentation. It incorporates the advantage of intuitionistic fuzzy sets theory. The IFCM successfully handles the uncertainty but it is sensitive to noise as it does not incorporate any local spatial information. In this paper, we have presented a novel approach, named an improved intuitionistic fuzzy c-means (IIFCM), which considers the local spatial information in an intuitionistic fuzzy way. The IIFCM preserves the image details, is insensitive to noise, and is free of requirement of any parameter tuning. The obtained segmentation results on synthetic square image, real and simulated MRI brain image demonstrate the efficacy of the IIFCM algorithm and superior performance in comparison to existing segmentation methods. A nonparametric statistical analysis is also carried out to show the significant performance of the IIFCM algorithm in comparison to other existing segmentation algorithms.
['Hanuman Verma', 'R. K. Agrawal', 'Aditi Sharan']
An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation