Brain tissue segmentation using fuzzy clustering techniques.

Background: Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images.

Objective: To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis.

Methods: An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner.

Results: Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%.

Conclusion: Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.