Hidden Markov measure field models for image segmentation
- 27 October 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 25 (11) , 1380-1387
- https://doi.org/10.1109/tpami.2003.1240112
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.Keywords
This publication has 24 references indexed in Scilit:
- An accurate and efficient Bayesian method for automatic segmentation of brain MRIIEEE Transactions on Medical Imaging, 2002
- Motion segmentation and tracking using normalized cutsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithmIEEE Transactions on Medical Imaging, 2001
- Markov Random Field Modeling in Image AnalysisPublished by Springer Nature ,2001
- Parametric estimate of intensity inhomogeneities applied to MRIIEEE Transactions on Medical Imaging, 2000
- Automated model-based tissue classification of MR images of the brainIEEE Transactions on Medical Imaging, 1999
- The application of mean field theory to image motion estimationIEEE Transactions on Image Processing, 1995
- Probabilistic Solution of Ill-Posed Problems in Computational VisionJournal of the American Statistical Association, 1987
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Cardinal Spline InterpolationPublished by Society for Industrial & Applied Mathematics (SIAM) ,1973