Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

Details

Serval ID
serval:BIB_747B4E52DE44
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
Title
Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification
Title of the conference
ICIP 2011, International Conference on Image Processing
Author(s)
Gorthi S., Thiran J.P., Bach Cuadra M.
Address
Brussels, Belgium, September 11-14, 2011
Publication state
Published
Issued date
2011
Language
english
Abstract
This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.
Keywords
LTS5, Energy minimization, Markov random fields (MRF), Medical image segmentation, Brain tissue classification, CIBM-SPC
Create date
29/11/2011 16:40
Last modification date
20/08/2019 14:32
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