Level set image segmentation with Bayesian analysis

Authors:
Huiyu Zhou;Yuan Yuan;Faquan Lin;Tangwei Liu
Affiliations:
Brunel University, Uxbridge, Middlesex UB8 3PH, UK and Guangxi Medical University, Nanning 530027, PR China;Aston University, Birmingham B4 7ET, UK;Guangxi Medical University, Nanning 530027, PR China;Guangxi Medical University, Nanning 530027, PR China
Venue:
Neurocomputing
Year:
2008

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Abstract

Classical level set methods easily suffer from deficiency in the presence of noise and other significant edges adjacent to the real boundary. This problem has not been effectively solved in the research community. In this paper, we propose an improved energy function to tackle this problem by continuously rectifying the deviation of the level set function according to the signed distance function. This is achieved using an expectation-maximisation algorithm. Experimental work shows the proposed framework outperforms the classical level set algorithms in accuracy and efficiency of image segmentation.