MEDICAL IMAGE SEGMENTATION ON A CLUSTER OF PCS USING MARKOV RANDOM FIELDS

Medical imaging applications produce large sets of similar images. The huge amount of data makes the manual analysis and interpretation a fastidious task. Medical image segmentation is thus an important process in image processing used to partition the images into different regions (e.g. gray matter(GM), white matter(WM) and cerebrospinal fluid(CSF)). Hidden Markov Random Field (HMRF) Model and Gibbs distributions provide powerful tools for image modeling. In this paper, we use a HMRF model to perform segmentation of volumetric medical images. We have a problem with incomplete data. We seek the segmented images according to the MAP (Maximum A Posteriori) criterion. MAP estimation leads to the minimization of an energy function. This problem is computationally intractable. Therefore, optimizations techniques are used to compute a solution. We will evaluate the segmentation upon two major factors: the time of calculation and the quality of segmentation. Processing time is reduced by distributing the computation of segmentation on a powerful and inexpensive architecture that consists of a cluster of personal computers. Parallel programming was done by using the standard MPI (Message Passing Interface).