Automatic estimation of the number of segmentation groups based on MI

Authors:
Ziming Zeng;Wenhui Wang;Longzhi Yang;Reyer Zwiggelaar
Affiliations:
Department of Computer Science, Aberystwyth University, UK and Faculty of Information and Control Engineering, Shenyang Jianzhu University, Liaoning, China;Network Information Center of the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou and Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China;Department of Computer Science, Aberystwyth University, UK;Department of Computer Science, Aberystwyth University, UK
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

Citing 2
Cited 1

Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis

Markov random field modeling in image analysis

Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

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.