Unsupervised algorithm for the segmentation of three-dimensional magnetic resonance brain images

This paper presents a multiple resolution algorithm for the segmentation of three-dimensional magnetic resonance (MR) images. The algorithm consists in the unsupervised segmentation of the MR volume into regions of different statistical behavior. Firstly, an unsupervised merging algorithm estimates...

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Hauptverfasser:	Capelle, A.S., Alata, O., Fernandez-Maloigne, C., Ferrie, J.C.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Brain modeling Engineering Sciences Gaussian processes Hidden Markov models Image resolution Image segmentation Magnetic resonance Magnetic resonance imaging Merging Signal and Image processing Statistics Stochastic processes
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creator	Capelle, A.S. Alata, O. Fernandez-Maloigne, C. Ferrie, J.C.
description	This paper presents a multiple resolution algorithm for the segmentation of three-dimensional magnetic resonance (MR) images. The algorithm consists in the unsupervised segmentation of the MR volume into regions of different statistical behavior. Firstly, an unsupervised merging algorithm estimates a block segmentation of the volume while determining the region number and the parameters of those regions. This estimation is computed by minimizing a global information criterion. Next, the small regions are eliminated using statistic criteria. Finally, the segmentation is performed using the neighboring relationships between voxels via hidden Markov random fields and a multiple resolution iterated conditional mode algorithm. Some results on volumetric brain MR images are presented and discussed.
doi_str_mv	10.1109/ICIP.2001.958306
format	Conference Proceeding
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subjects	Brain modeling Engineering Sciences Gaussian processes Hidden Markov models Image resolution Image segmentation Magnetic resonance Magnetic resonance imaging Merging Signal and Image processing Statistics Stochastic processes
title	Unsupervised algorithm for the segmentation of three-dimensional magnetic resonance brain images
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