Automatic estimation of the number of segmentation groups based on MI

Ziming Zeng, Wenhui Wang, Longzhi Yang, Reyer Zwiggelaar

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis
EditorsJordi Vitria, Joao Sanches, Mario Hernández
Place of PublicationLondon
ISBN (Print)978-3-642-21256-7
Publication statusPublished - 2011


Dive into the research topics of 'Automatic estimation of the number of segmentation groups based on MI'. Together they form a unique fingerprint.

Cite this