Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
Joint Authors
Fan, Yangyu
Feng, Yan
Wang, Yi
Cui, Wenchao
Lei, Tao
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity.
In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances.
According to maximum a posteriori probability (MAP) and Bayes’ rule, we first derive a local objective function for image intensities in a neighborhood around each pixel.
Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion.
In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image.
Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process.
Experimental results for synthetic and real images show desirable performances of our method.
American Psychological Association (APA)
Cui, Wenchao& Wang, Yi& Lei, Tao& Fan, Yangyu& Feng, Yan. 2013. Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-481601
Modern Language Association (MLA)
Cui, Wenchao…[et al.]. Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-481601
American Medical Association (AMA)
Cui, Wenchao& Wang, Yi& Lei, Tao& Fan, Yangyu& Feng, Yan. Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-481601
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-481601