Automatic estimation of the number of segmentation groups based on MI

Zeng, Ziming, Wang, Wenhui, Yang, Longzhi and Zwiggelaar, Reyer (2011) Automatic estimation of the number of segmentation groups based on MI. In: Pattern Recognition and Image Analysis. Springer, London, pp. 532-539. ISBN 978-3-642-21256-7

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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.

Item Type:	Book Section
Subjects:	G400 Computer Science G700 Artificial Intelligence
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Longzhi Yang
Date Deposited:	07 Jan 2014 08:59
Last Modified:	12 Oct 2019 22:29
URI:	http://nrl.northumbria.ac.uk/id/eprint/13094

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