Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI.

Segmentation of brain tissues from MRI often becomes crucial to properly investigate any region of the brain in order to detect abnormalities. However, the accurate segmentation of the brain tissues is a challenging task as the different tissue regions are usually imprecise, indiscernible, ambiguous, and overlapping. Additionally, different tissue regions are non-linearly separable. Noises and other artifacts may also present in the brain MRI. Therefore, conventional segmentation techniques may not often achieve desired accuracy. To deal those challenges, a robust kernelized rough fuzzy C-means clustering with spatial constraints (KRFCMSC) is proposed in this article for brain tissue segmentation. Here, the brain tissue segmentation from MRI is considered as a clustering of pixels problem. The basic idea behind the proposed technique is the judicious integration of the fuzzy set, rough set, and kernel trick along with spatial constraints (in the form of contextual information) to increase the clustering (segmentation) performance. The use of rough and fuzzy set theory in the clustering process handles the ambiguity, indiscernibility, vagueness and overlappingness of different brain tissue regions. While, the kernel trick increases the chance of linear separability of the complex regions which are otherwise not linearly separable in its original feature space. In order to deal the noisy pixels, here in the clustering process, the spatio-contextual information is introduced from the neighbouring pixels. Experiments are carried out on different real and synthetic benchmark brain MRI datasets (publicly available from Brainweb, and IBSR) without and with added noise. The performance of the proposed method is compared with five other counterpart clustering based segmentation techniques and evaluated using various supervised as well as unsupervised validity indices such as, overall accuracy, precision, recall, kappa, Jaccard, dice, and kernelized Xie-Beni index. Experimental results justify the superiority and robustness of the proposed method over other state-of-the-art methods on both benchmark real life and synthetic brain MRI datasets with and without added noise. Statistical significance of the better segmentation accuracy can be confirmed from the paired t-test results in favour of the proposed method compared to other counterpart methods.