Image segmentation is at a preliminary stage of inclusion in diagnosis tools and the accurate segmentation of tissue images is crucial for a correct diagnosis by these tools. Due to in-homogeneity, low contrast, noise and inequality of content with semantic; image segmentation is a challenging job. A review of the Gaussian Mixture Model based segmentation algorithms for lung tissue images is presented. The review covers algorithms for segmentation algorithms and their comparative evaluations based on reported results.