Unsupervised partial volume estimation in single-channel image data

Partial volume effects are present in nearly all medical imaging data. These artifacts blur the boundaries between different regions, making accurate delineation of anatomical structures difficult. Here, the authors propose a method for unsupervised estimation of partial volume effects in single-cha...

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Bibliographic Details
Published in:IEEE Workshop on Mathematical Methods in Biomedical Image Analysis : proceedings, Hilton Head Island, South Carolina, June 11-12, 2000 pp. 170 - 177
Main Authors: Pham, D.L., Prince, J.L.
Format: Conference Proceeding
Language:English
Published: IEEE 2000
Subjects:
Anatomical structure
Bayesian methods
Biomedical imaging
Brain modeling
Clustering algorithms
Cognition
Gerontology
Image segmentation
Laboratories
Pathology
ISBN:9780769507378, 0769507379
Online Access:Get full text
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Summary:Partial volume effects are present in nearly all medical imaging data. These artifacts blur the boundaries between different regions, making accurate delineation of anatomical structures difficult. Here, the authors propose a method for unsupervised estimation of partial volume effects in single-channel image data. Based on a statistical image model, an algorithm is derived for estimating both partial volumes and the means of the different tissue classes in the image. To compensate for the ill-posed nature of the estimation problem, the authors employ a Bayesian approach that places a prior probability model on the parameters. They demonstrate on simulated and real images that the new algorithm is superior in several respects to the fuzzy and Gaussian clustering algorithms that have previously been used for modeling partial volume effects.
ISBN:9780769507378
0769507379
DOI:10.1109/MMBIA.2000.852375