A variational multiphase level set approach to simultaneous segmentation and bias correction

K. Zhang, Lei Zhang, S.U. Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

71 Citations (Scopus)


This paper presents a novel level set approach to simultaneous tissue segmentation and bias correction of Magnetic Resonance Imaging (MRI) images. We first model the distribution of intensity belonging to each tissue as a Gaussian distribution with spatially varying mean and variance. Then a sliding window is used to transform the intensity domain to another domain, where the distribution overlap between different tissues is significantly suppressed. A maximum likelihood objective function is defined for each point in the transformed domain, which is then integrated over the entire domain to form a variational level set formulation. Tissue segmentation and bias correction are simultaneously achieved via a level set evolution process. The proposed method is robust to initialization, thereby allowing automatic applications. Experiments on images of various modalities demonstrated the superior performance of the proposed approach over state-of-the-art methods.
Original languageEnglish
Title of host publication2010 International Conference on Image Processing
Number of pages4
ISBN (Print)9781424479948
Publication statusPublished - 2010
EventIEEE International Conference on Image Processing [ICIP] - , Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameIEEE International Conference on Image Processing ICIP
ISSN (Print)1522-4880


ConferenceIEEE International Conference on Image Processing [ICIP]
Country/TerritoryHong Kong

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