This paper presents novel algorithms for magnetic resonance (MR) brain images segmentation using textural analysis. Classification for MR images using features extracted from the texture is done using two algorithms, the Fuzzy Rule Based system and Fuzzy Similarity measures. The cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). Image preprocessing was first done to improve the quality of brain MR images and reducing artifacts. The feature vector was selected to vary according to the textural structure of the images. The two algorithms are of supervised nature where in the first we build fuzzy rules while in the second we build fuzzy prototypes. The classification in the first method uses fuzzy inference and implication techniques to derive the classes of images. The classification in the second method uses pattern matching and fuzzy similarity measures. These algorithms are tested using sets of MR brain images. The results showed the efficient and robust performance of these algorithms. In this paper a comparison of these algorithms with Fuzzy C-Means algorithm based on texture features is presented.