MRI Brain Images Segmentation

In this paper, a modified fuzzy c-means (FCM) clustering for medical image segmentation is presented. A conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the method are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. Originality of this research is the methods applied on a normal MRI brain image and a glioma MRI brain images, and analyze the area of tumor from segmented images. The results show that the method effectively segmented Magnetic Resonance Imaging (MRI) brain images with spatial information, and the segmented normal and glioma MRI brain images can be analyzed for diagnosis purpose. And, the area of abnormal mass is identified from 9.65 to 27.71 cm. Index Term— Adaptive image segmentation, fuzzy logic, FCM clustering, MRI brain image, spatial information