Science China Information Sciences

, Volume 56, Issue 11, pp 1–14 | Cite as

A unified level set framework utilizing parameter priors for medical image segmentation

Research Paper Progress of Projects Supported by NSFC
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Abstract

Image segmentation plays an important role in many medical imaging systems, yet in complex circumstances it remains an open problem. One of the main difficulties is the intensity inhomogeneity in an image. In order to tackle this problem, we first introduce a region-based level set segmentation framework to unify the traditional global and local methods. We then propose two novel parameter priors, i.e., the local order regularization and interactive regularization, and then utilize them as the constraints of the objective energy function. The objective energy function is finally minimized via a level set evolution process to achieve image segmentation. Extensive experiments show that the proposed approach has gained significant improvements in both accuracy and efficiency over the state-of-the-art methods.

Keywords

image segmentation level set local order regularization interactive regularization 

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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition (NLPR), Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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