Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Improved Mumford-Shah Functional for Coupled Edge-Preserving Regularization and Image Segmentation | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 37129 Pages 1-9 DOI ASP 2006 37129 Improved Mumford-Shah Functional for Coupled Edge-Preserving Regularization and Image Segmentation Zhang Hongmei1 2 and Wan Mingxi1 2 1 The Key Laboratory of Biomedical Information Engineering Ministry of Education 710049 Xi an China 2 Department of Biomedical Engineering School of Life Science and Technology Xi an Jiaotong University Xi an 710049 China Received 11 October 2005 Revised 16 January 2006 Accepted 18 February 2006 Recommended for Publication by Moon Gi Kang 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. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Mumford-Shah MS functional is an important variational model in image analysis. It minimizes a functional involving a piecewise smooth representation of an image and penalizing the Hausdorff measure of the set of discontinuities resulting in simultaneous linear restoration and segmentation 1 2 . However the MS functional is based on Bayesian linear restoration so the resultant linear diffusion not only smoothes all structures
