Medical image segmentation based on the bayesian level set method

Yao Tien Chen, Din Chang Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


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.

Original languageEnglish
Title of host publicationMedical Imaging and Informatics - 2nd International Conference, MIMI 2007, Revised Selected Papers
Number of pages10
StatePublished - 2008
Event2nd International Conference on Medical Imaging and Informatics, MIMI 2007 - Beijing, China
Duration: 14 Aug 200716 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4987 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Medical Imaging and Informatics, MIMI 2007


  • Bayesian risk
  • Hypothesis test
  • Image segmentation
  • Level set method


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