A fast level set method for segmentation of low contrast noisy biomedical images |
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| Affiliation: | 1. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Québec, Canada;2. Centre de recherche du Centre Hospitalier de l’Université de Montréal (CR-CHUM) and Institut du cancer de Montréal, Montréal, Québec, Canada;3. Martini-Klinik, University Hospital Hamburg-Eppendorf, Hamburg, Germany;4. Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy;5. Vita-Salute San Raffaele University, Milan, Italy;6. Academic Department of Urology, IRCCS Policlinico San Donato, University of Milan, Milan, Italy;7. Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany;8. Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany;9. Department of Urology, Medical University of Vienna, Vienna, Austria;1. Ph. D. Program in Biomedical Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan;2. College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China;3. Faculty of Information Technology, Beijing University of Technology, Beijing, China;4. Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan;5. Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan;6. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan;1. Dpto. EDAN, Universidad de Sevilla, C/Tarfia, s/n. 41012 Sevilla, Spain;2. IMUS, Universidad de Sevilla, C/Tarfia, s/n. 41012 Sevilla, Spain;1. Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Station 8, CH-1015 Lausanne, Switzerland;2. MOX, Department of Mathematics, Politecnico di Milano, P.za Leonardo da Vinci 32, I-20133 Milano, Italy |
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| Abstract: | This paper presents a new fast front propagation algorithm for image segmentation. To approximate the partial differential equation (PDE) in level set algorithm, instead of moving the front in a small constant time step, the point with a minimum arrival time will be touched in one iteration. Only in a neighbourhood of this point, should the level set function be updated. Like the previously proposed level set methods, it is a robust method for image segmentation with capabilities to handle topological changes, significant protrusions and narrow regions. It is faster than the narrow band algorithm and more robust than the monotonically advancing scheme in image segmentation. The effectiveness and the capabilities of the algorithm were verified by simulated and real experiments. |
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