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Improved Mumford-Shah Functional for Coupled Edge-Preserving Regularization and Image Segmentation

  • Zhang HongmeiEmail author
  • Wan Mingxi
Open Access
Research Article

Abstract

An improved Mumford-Shah functional for coupled edge-preserving regularization and image segmentation is presented. A nonlinear smooth constraint function is introduced that can induce edge-preserving regularization thus also facilitate the coupled image segmentation. The formulation of the functional is considered from the level set perspective, so that explicit boundary contours and edge-preserving regularization are both addressed naturally. To reduce computational cost, a modified additive operator splitting (AOS) algorithm is developed to address diffusion equations defined on irregular domains and multi-initial scheme is used to speed up the convergence rate. Experimental results by our approach are provided and compared with that of Mumford-Shah functional and other edge-preserving approach, and the results show the effectiveness of the proposed method.

Keywords

Information Technology Computational Cost Convergence Rate Quantum Information Image Segmentation 

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Copyright information

© Hongmei and Mingxi 2006

Authors and Affiliations

  1. 1.The Key Laboratory of Biomedical Information EngineeringMinistry of EducationXi'anChina
  2. 2.Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina

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