A robust soft decision mixture model for image segmentation

Authors:
Pan Lin;Feng Zhang;ChongXun Zheng;Yong Yang;Yimin Hou
Affiliations:
Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Department of Automatic Control of Northwestern Polytechnical University, Xi'an, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

In this paper, we present a novel soft decision mixture model for image segmentation. This model adopts the soft decision classify into gaussian mixture model to represent the probability distribution of the observed image feature. The model for the underlying true context images is designed to serve as prior contextual constraints on unobserved pixel labels in term of markov random field model. Experiments with synthetic image and real image show that the use of soft decision mixture model definitely improves the quality of the segmentation results for noisy images and results in reduced classification errors in the interior area of the region.