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Proceedings Paper

Tissue tracking: applications for brain MRI classification
Author(s): John Melonakos; Yi Gao; Allen Tannenbaum
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Paper Abstract

Bayesian classification methods have been extensively used in a variety of image processing applications, including medical image analysis. The basic procedure is to combine data-driven knowledge in the likelihood terms with clinical knowledge in the prior terms to classify an image into a pre-determined number of classes. In many applications, it is difficult to construct meaningful priors and, hence, homogeneous priors are assumed. In this paper, we show how expectation-maximization weights and neighboring posterior probabilities may be combined to make intuitive use of the Bayesian priors. Drawing upon insights from computer vision tracking algorithms, we cast the problem in a tissue tracking framework. We show results of our algorithm on the classification of gray and white matter along with surrounding cerebral spinal fluid in brain MRI scans. We show results of our algorithm on 20 brain MRI datasets along with validation against expert manual segmentations.

Paper Details

Date Published: 3 March 2007
PDF: 9 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651218 (3 March 2007); doi: 10.1117/12.710063
Show Author Affiliations
John Melonakos, Georgia Institute of Technology (United States)
Yi Gao, Georgia Institute of Technology (United States)
Allen Tannenbaum, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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