Fuzzy c-means clustering method based on prior knowledge for brain MR image segmentation

Image segmentation is mostly used as a fundamental step in medical image processing, especially for clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) algorithm is one of the well known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MR images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentation done by experts, prior knowledge can be useful to modify image segmentation methods. In this article we proposed a method to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in order to modify segmentation of brain MR images with high noise level and spatial complexities. In real images, we had considerable improvement in similarity index of three classes (white matter, gray matter, cerebrospinal fluid) and in simulated images with different noise levels evaluation criteria of white matter and gray matter improved.