A NEW MODIFIED FUZZY C-MEANS FOR SEGMENTING MAGNETIC RESONANCE IMAGES (MRIS)

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, medical image segmentation is complex, and automatically detecting regions or clusters of such widely varying sizes is a challenging task In this paper; a new modified fuzzy c-means algorithm (MFCM) is presented that could improve the medical image segmentation. The proposed MFCM algorithm is realized by modifying the objective function of the conventional FCM algorithm with a flexible penalty. This penalty is based on a data shape and data size used for the generation offuzzy terms. The complexity of the proposed algorithm is reduced using initial seed information into the objective function instead of whole data set The performance of the proposed algorithm is tested on noisy real images. The results of the conducted experiments show that the efficiency of the proposed method in preserving the regions homogeneity and its robustness in segmenting noisy images is better than other FCM-based methods