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Author Xiang Shan ♦ Bing Nan Li
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Level set ♦ Image segmentation ♦ Force ♦ Biomedical imaging ♦ Image edge detection ♦ Magnetic resonance imaging ♦ Computed tomography ♦ medical imaging ♦ fuzzy clustering ♦ image segmentation ♦ level set methods
Abstract Level set methods (LSMs) have been extensively investigated for medical image segmentation. However, there are a few inherent drawbacks in common level set formulations using image variation or region competition. For example, edge-based LSMs are susceptible to weak or broken boundaries, while region-based ones are often dominated by suboptimal solutions. By incorporating the functional of fuzzy controlling, we propose a new level set model in this paper to combine the merits of edge-based and region-based LSMs while overcoming their drawbacks. It also provides a convenient framework to integrate prior information or knowledge for medical image segmentation. Its performance has been preliminarily verified for medical images of computed tomography (CT) and magnetic resonance imaging (MRI).
Description Author affiliation: Dept. of Biomed. Eng., Hefei Univ. of Technol., Hefei, China (Xiang Shan; Bing Nan Li)
ISBN 9781479986392
ISSN 21593450
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-11-01
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479986415
Size (in Bytes) 573.31 kB
Page Count 4
Starting Page 1
Ending Page 4


Source: IEEE Xplore Digital Library