Kernal Analysis Mercer’s Theorem on Matrix Model Ependymomas Tumour in MR Brain Image

Abstract

Uncontrollable Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, the existence of noise and intensity inhomogeneity in brain MR images, many image segmentation algorithms suffer from limited accuracy. In this paper, we have assume that the local image data within each voxel’s neighborhood satisfy the Gaussian Mixture Model (GMM), and thus propose the Fuzzy Local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior likelihood by minimizing an objective energy function  during which  a truncated Gaussain kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to progressive segmentation approaches in both synthetic and clinical data. An experimental result shows that the proposed algorithm will mostly overcome the difficulties raised by noise, low contrast, and bias field, and considerably improve the accuracy of brain MR image segmentation.
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