Local and global Gaussian mixture models for hematoxylin and eosin stained histology image segmentation

Abstract

This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and connecting contents from background. Specifically, local GMM is firstly applied to detect nuclei by scanning the input image, which is followed by global GMM to separate other tissue constituents from background. Regular RGB (red, green and blue) color space is employed individually for the local and global GMMs to make use of the H&E staining features. Experiments on a set of cervix histology images show the improved performance of the proposed algorithm when compared with traditional K-means clustering and state-of-art multiphase level set methods.