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Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability

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...

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Detalles Bibliográficos
Autores principales: Cui, Wenchao, Wang, Yi, Lei, Tao, Fan, Yangyu, Feng, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835522/
https://www.ncbi.nlm.nih.gov/pubmed/24302974
http://dx.doi.org/10.1155/2013/570635
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author Cui, Wenchao
Wang, Yi
Lei, Tao
Fan, Yangyu
Feng, Yan
author_facet Cui, Wenchao
Wang, Yi
Lei, Tao
Fan, Yangyu
Feng, Yan
author_sort Cui, Wenchao
collection PubMed
description 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.
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spelling pubmed-38355222013-12-03 Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability Cui, Wenchao Wang, Yi Lei, Tao Fan, Yangyu Feng, Yan Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2013 2013-11-05 /pmc/articles/PMC3835522/ /pubmed/24302974 http://dx.doi.org/10.1155/2013/570635 Text en Copyright © 2013 Wenchao Cui et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
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
title Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
title_full Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
title_fullStr Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
title_full_unstemmed Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
title_short Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability
title_sort level set segmentation of medical images based on local region statistics and maximum a posteriori probability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835522/
https://www.ncbi.nlm.nih.gov/pubmed/24302974
http://dx.doi.org/10.1155/2013/570635
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