Title :
A new level set model for unified medical image segmentation
Author :
Xiang Shan;Bing Nan Li
Author_Institution :
Department of Biomedical Engineering, Hefei University of Technology, China
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).
Keywords :
"Level set","Image segmentation","Force","Biomedical imaging","Image edge detection","Magnetic resonance imaging","Computed tomography"
Conference_Titel :
TENCON 2015 - 2015 IEEE Region 10 Conference
Print_ISBN :
978-1-4799-8639-2
Electronic_ISBN :
2159-3450
DOI :
10.1109/TENCON.2015.7373025