Homogeneous Patch Based FCM Algorithm for Brain MR Image Segmentation

This paper presents a homogeneous patch based fuzzy c-means (FCM) clustering algorithm for brain magnetic resonance (MR) image segmentation. Currently, FCM is mainly improved by incorporating local spatial information for noise immunity. The proposed algorithm is based on image patch space, which can avoid introducing an extra control parameter for local spatial restriction. In order to decrease the edge blurring caused by local spatial restriction, the local polynomial approximation-intersection of confidence intervals (LPA-ICI) technique is used to construct the homogeneous patch. Brain MR image segmentation results indicate that the proposed algorithm is better than the other improved FCM algorithms that incorporate local spatial information, while the detail preservation need to be improved.