Unsupervised Learning for MRI Brain Tumor Segmentation with Spatially Variant Finite Mixture Model in Reversible Jump MCMC Algorithm

Abstract

MRI brain tumor segmentation is an important topic in medical image processing. Manual segmentation is risky and time-consuming when the MRI is in low quality. The automatic segmentation can be a solution to manage this problem. This paper proposed an improved modeling approach for unsupervised learning trough Spatially Variant Finite Mixture Model (SVFMM). The main contribution is the automation of the SVFMM algorithm to find the optimum number of clusters. This is achieved by employing the birth-death random process in Bayesian Reversible Jump MCMC. Validation of the proposed model is done by calculating the Correct Classification Ration (CCR) in comparison to the original SVFMM and Gaussian Mixture Model (GMM). The proposed model provides similar performance in image segmentation compared to the original SVFMM but is better than GMM. However, SVFMM-RJMCMC is faster and more efficient in finding the optimum number of clusters.