DocumentCode :
2152727
Title :
Medical image segmentation via coupled curve evolution equations with global constraints
Author :
Yezzi, Anthony, Jr. ; Tsai, Andy ; Willsky, Alan
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2000
fDate :
2000
Firstpage :
12
Lastpage :
19
Abstract :
In this work the authors modify the coupled curve evolution approach to snakes presented by the authors in previous work for bimodal and trimodal imagery through the introduction of global constraints. The key idea, as before, is to derive curve evolution equations which “pull apart” the values of one or more statistics within the image. However, by imposing a new constraint on the evolution of these statistics, the authors are able to segment a larger class of medical imagery for which their original model would fail
Keywords :
image segmentation; medical image processing; statistics; coupled curve evolution equations; global constraints; image statistics; medical diagnostic imaging; medical image segmentation; snakes; statistics evolution; Biomedical imaging; Computed tomography; Equations; Image segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mathematical Methods in Biomedical Image Analysis, 2000. Proceedings. IEEE Workshop on
Conference_Location :
Hilton Head Island, SC
Print_ISBN :
0-7695-0737-9
Type :
conf
DOI :
10.1109/MMBIA.2000.852355
Filename :
852355
Link To Document :
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