Adaptive scale fuzzy local Gaussian mixture model for brain MR image segmentation
The Gaussian mixture model (GMM) has been widely used in brain magnetic resonance (MR) image segmentation. However, due to the MR bias field effect, the implied stochastic assumption that the intensities of each tissue type are sampled from an identical distribution may not be valid. In this paper, we propose a novel adaptive scale fuzzy local GMM (AS-FLGMM) algorithm for accurate and robust brain MR image segmentation. We assume that the local image data within the neighborhood of each pixel follow the GMM, in which the difference of variance among Gaussian components can be ignored. Based on this assumption, we develop a local scale estimation method to adaptively calculate the variance in each distribution. The segmentation is then performed under the fuzzy clustering framework and the objective is defined as the integration of the weighted GMM energy of each pixel. The AS-FLGMM algorithm has been compared to five state-of-the-art segmentation approaches in both synthetic and clinical MR images. Our results show that the proposed algorithm can produce more accurate segmentation results and its performance is more robust to initialization.
adaptive scale image segmentation gaussian mixture model (gmm) fuzzy clustering magnetic resonance imaging (mri)