One of the major tasks or even the most prevalent task in medical imageprocessing is image segmentation. Among them, brain MR images sufferfrom some difficulties such as intensity inhomogeneity of tissues, partialvolume effect, noise and some other imaging artifacts and so their segmentation is more challenging. Therefore, brain MRI segmentationbased on just gray values is prone to error. Hence involving problem specific heuristics and expert knowledge in designing segmentation algorithms seems to be useful. A two-phase segmentation algorithm basedon Bayesian method is proposed in this paper. The Bayesian part uses thegray value in segmenting images and the segmented image is used as theinput to the second phase to improve the misclassified pixels especially inborders between tissues. Similarity index is used to compare our algorithmwith the well known method of Ashburner which has been implemented inStatistical Parametric Mapping (SPM) package. Brainweb as a simulatedbrain MRI dataset is used in evaluating the proposed algorithm. Resultsshow that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating expert knowledge and problem specific heuristics improve segmentation result.The major advantage of proposed method is that one can update theknowledge base and incorporate new information into segmentationprocess by adding new rules.Keywords: Magnetic Resonance Imaging (MRI); Segmentation; Bayesianclassifier; Heuristic
