Magnetic resonance imaging (MRI) is a medical imaging technique used to investigate the anatomy and physiology of the body is widely used in hospitals for medical diagnosis and staging of diseases. The research efforts have been devoted to processing and analyzing medical images to extract meaningful information to detect abnormalities. MRI segmentation aims at extraction of object boundary features which plays a fundamental role in understanding image content. A challenging problem is to segment regions are boundary insufficiencies, blur edges, lack of texture contrast between regions of interest and background. To address this problem two categories of approaches are used on medical image segmentation: (i) enhancement technique i.e. histogram equalizer technique is implemented on selected image to enhance the contrast of image. Brightness preserving Bi Histogram Equalization (BBHE) technique is used for enhancing the image because previous technique perverse contrast but only BBHE consider Brightness of an image. (ii) Apply fuzzy-C mean (FCM) clustering segmentation algorithm on enhanced image. Fuzzy C mean algorithm helps to compute clusters from the image and calculate the centers of clusters. Examples of medical data segmentation and general conclusions from the methods are described and we give future directions for solving challenging and open problems in medical image segmentation.

uzzy-C Means Clustering; Magnetic Resonance Imaging; Histogram Equalization Technique; Segmentation

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