research-article

Hidden Markov Measure Field Models for Image Segmentation

Authors:

Jose L. Marroquin,

Edgar Arce Santana,

Salvador BotelloAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 25, Issue 11

Pages 1380 - 1387

https://doi.org/10.1109/TPAMI.2003.1240112

Published: 01 November 2003 Publication History

Abstract

Parametric image segmentation consists of finding a label field that defines a partition of an image into a set of nonoverlapping regions and the parameters of the models that describe the variation of some property within each region. A new Bayesian formulation for the solution of this problem is presented, based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators for both this field and the model parameters by the minimization of a differentiable function. An efficient minimization algorithm and comparisons with existing methods on synthetic images are presented, as well as examples of realistic applications to the segmentation of Magnetic Resonance volumes and to motion segmentation.

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Cited By

Yan SDong Y(2021)GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT ApplicationScale Space and Variational Methods in Computer Vision10.1007/978-3-030-75549-2_40(503-515)Online publication date: 16-May-2021
https://dl.acm.org/doi/10.1007/978-3-030-75549-2_40
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https://dl.acm.org/doi/10.1007/s11042-017-4951-z
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https://dl.acm.org/doi/10.1007/s10916-016-0623-1
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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 25, Issue 11

November 2003

144 pages

ISSN:0162-8828

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Copyright © Copyright © 2003 IEEE. All Rights Reserved.

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IEEE Computer Society

United States

Publication History

Published: 01 November 2003

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Cited By

Yan SDong Y(2021)GMM Based Simultaneous Reconstruction and Segmentation in X-Ray CT ApplicationScale Space and Variational Methods in Computer Vision10.1007/978-3-030-75549-2_40(503-515)Online publication date: 16-May-2021
https://dl.acm.org/doi/10.1007/978-3-030-75549-2_40
Xia YLi Z(2018)VBI-MRF model for image segmentationMultimedia Tools and Applications10.1007/s11042-017-4951-z77:11(13343-13361)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11042-017-4951-z
Jain NKumar V(2016)IFCM Based Segmentation Method for Liver Ultrasound ImagesJournal of Medical Systems10.1007/s10916-016-0623-140:11(1-12)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s10916-016-0623-1
Peng RVarshney P(2015)On performance limits of image segmentation algorithmsComputer Vision and Image Understanding10.1016/j.cviu.2014.11.004132:C(24-38)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.cviu.2014.11.004
Lember JKoloydenko A(2014)Bridging Viterbi and posterior decodingThe Journal of Machine Learning Research10.5555/2627435.262743615:1(1-58)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.5555/2627435.2627436
Yu YGu JWang J(2014)Bhattacharyya distance‐based irregular pyramid method for image segmentationIET Computer Vision10.1049/iet-cvi.2013.01498:6(510-522)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1049/iet-cvi.2013.0149
Balafar M(2014)Gaussian mixture model based segmentation methods for brain MRI imagesArtificial Intelligence Review10.1007/s10462-012-9317-341:3(429-439)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s10462-012-9317-3
Wang ZSong QSoh YSim K(2013)An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentationComputer Vision and Image Understanding10.1016/j.cviu.2013.05.001117:10(1412-1420)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1016/j.cviu.2013.05.001
Dalmau ORivera M(2011)Alpha Markov Measure Field model for probabilistic image segmentationTheoretical Computer Science10.1016/j.tcs.2010.11.008412:15(1434-1441)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1016/j.tcs.2010.11.008
Dalmau ORivera M(2010)Beta-measure for probabilistic segmentationProceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I10.5555/1927149.1927181(312-324)Online publication date: 8-Nov-2010
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