Intuitionistic fuzzy sets (IFSs) and rough sets are extensively used mathematical tools to handle uncertainty and vagueness present in images and recently are combined together to segment MR medical images in the presence of intensity non uniformity (INU) and noise. In this paper, a novel clustering algorithm, namely modified rough intuitionistic fuzzy c-means (MRIFCM) is proposed for the segmentation of the brain magnetic resonance images to extract the white matter, grey matter and the cerebrospinal fluid from MR brain image with bias field correction. A new intuitionistic fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity in homogeneity and noise in brain MR images.Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. The proposed algorithm is evaluated through simulation and compared it with existing k-means (KM),Rough k-means(RKM), fuzzy C-means (FCM), Rough fuzzy c-means(RFCM), Generalized rough fuzzy c-means (GRFCM), soft fuzzy rough c-means (SFRCM),rough intuitionistic fuzzy c-means(RIFCM) and Generalized rough Intuitionistic fuzzy c-means(GRIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.