Fusion of Gaussian Mixture Model and Spatial Fuzzy C-Means for Brain MR Image Segmentation

Brain image segmentation into white matter, grey matter and cerebrospinal fluid is a very popular yet challenging area in medical image processing. The Fuzzy C-Means clustering algorithm is quite used because of its ability to ensure multiple member of pixels in several clusters. This is further appreciated when the spatial information of the data is considered, as such, the algorithm becomes much more robust to noise. However, pixels which form part of the non-overlapped tissues often come out inaccurate. To solve this problem, a spatial fuzzy algorithm fused with the Gaussian mixture model using the expectation maximization algorithm is presented in this paper. The proposed algorithm is a fusion of the Fuzzy C-Means and gaussian Mixture Model algorithms for segmenting tissues having multiple cluster memembership as well as single clustering. The results of the proposed algorithm are compared with the other classical algorithms against the manually segmented results of the input image. The estimated results of the proposed algorithm using the Dice and Jaccard similarity index indicates improved accuracy to the other algorithms.