Segmentation of Volumetric Medical Data Using Hidden Markov Random Field Model

Abstract

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, white matter and cerebrospinal fluid). 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 use and compare three optimization techniques that are Gibbs Sampler and Metropolis sampling with Simulated Annealing scheme, and the Iterated Conditional Modes (ICM).