Improved fuzzy c-means segmentation algorithm for images with intensity inhomogeneity

Image segmentation is a classic problem in computer image comprehension and related fields. Up to now, there are not any general and valid partition methods which could satisfy different purposes, especially for medical images such as magnetic resonance images, which often corrupted by multiple imaging artifacts, for example intensity inhomogeneity, noise and partial volume effects. In this paper, we propose an improved fuzzy c-means image segmentation algorithm with more accurate results and faster computation. Considering two voxels with the same intensity belonging to the same tissue, we use q intensity levels instead of n intensity values in the objective function of the fuzzy c-means algorithm, which makes the algorithm clusters much faster since q is much smaller than n. Furthermore, a gain field is incorporate in the objective function to compensate for the inhomogeneity. In addition, we use c-means clustering algorithm to initialize the centroids. This can further accelerate the clustering. The test results show that the proposed algorithm not only gives more accurate results but also makes the computation faster.