@InProceedings{10.1007/978-3-642-21257-4_66, author="Zeng, Ziming and Wang, Wenhui and Yang, Longzhi and Zwiggelaar, Reyer", editor="Vitri{\`a}, Jordi and Sanches, Jo{\~a}o Miguel and Hern{\'a}ndez, Mario", title="Automatic Estimation of the Number of Segmentation Groups Based on MI", booktitle="Pattern Recognition and Image Analysis", year="2011", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="532--539", abstract="Clustering is important in medical imaging segmentation. The number of segmentation groups is often needed as an initial condition, but is often unknown. We propose a method to estimate the number of segmentation groups based on mutual information, anisotropic diffusion model and class-adaptive Gauss-Markov random fields. Initially, anisotropic diffusion is used to decrease the image noise. Subsequently, the class-adaptive Gauss-Markov modeling and mutual information are used to determine the number of segmentation groups. This general formulation enables the method to easily adapt to various kinds of medical images and the associated acquisition artifacts. Experiments on simulated, and multi-model data demonstrate the advantages of the method over the current state-of-the-art approaches.", isbn="978-3-642-21257-4" }