Gaussian Mixture Model with the Inclusion of Spatial Factor and Pixel Re-labelling: Application to MR Brain Image Segmentation

Abstract

A fully automatic and accurate brain image segmentation divided into the three tissue types—Grey Matter, White Matter and Cerebrospinal Fluid (CSF)—is necessary in medical analysis. Gaussian Mixture Model (GMM) is one of the most widely implemented methods for MR Brain Image Segmentation, and Expectation–Maximization algorithm is used to estimate the parameters of the model. The major drawback existing in conventional GMM is that it considers each pixel as independent and does not incorporate the spatial interaction among the neighbouring pixels. This will lead to poor segmentation accuracy in the presence of noise and intensity inhomogeneity. To overcome this drawback, in the proposed method, the posterior probability is weighted with a spatial factor which improves the segmentation accuracy and makes the convergence also faster. Also, in the automatic segmentation of the IBSR MR Brain Image Data, a new approach of re-labelling the voxels for Grey Matter and CSF is proposed. The proposed method is tested on 20 low-resolution T1-weighted IBSR Brain Images and BrainWeb Simulated MR Images. The results show that the proposed method can improve the performance of MR Brain Image Segmentation by around 4 % over the state-of-the-art algorithm.