Medical image segmentation based on the bayesian level set method

Yao Tien Chen, Din Chang Tseng

研究成果: 書貢獻/報告類型會議論文篇章同行評審

10 引文 斯高帕斯(Scopus)

摘要

A level set method based on the Bayesian risk is proposed for medical image segmentation. At first, the image segmentation is formulated as a classification of pixels. Then the Bayesian risk is formed by false-positive and false-negative fractions in a hypothesis test. Through minimizing the average risk of decision in favor of the hypotheses, the level set evolution functional is deduced for finding the boundaries of targets. To prevent the propagating curves from generating excessively irregular shapes and lots of small regions, curvature and gradient of edges in the image are integrated into the functional. Finally, the Euler-Lagrange formula is used to find the iterative level set equation from the derived functional. Comparing with other level-set methods, the proposed approach relies on the optimum decision of pixel classification; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach can accurately extract the complicated shape of targets and is robust for various types of images including high-noisy and low-contrast images, CT, MRI, and ultrasound images; moreover, the algorithm is extendable for multiphase segmentation.

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主出版物標題Medical Imaging and Informatics - 2nd International Conference, MIMI 2007, Revised Selected Papers
頁面25-34
頁數10
DOIs
出版狀態已出版 - 2008
事件2nd International Conference on Medical Imaging and Informatics, MIMI 2007 - Beijing, China
持續時間: 14 8月 200716 8月 2007

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4987 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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???event.eventtypes.event.conference???2nd International Conference on Medical Imaging and Informatics, MIMI 2007
國家/地區China
城市Beijing
期間14/08/0716/08/07

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