Tissue tracking: applications for brain MRI classification

John Melonakos; Yi Gao; Allen Tannenbaum

doi:10.1117/12.710063

3 March 2007 Tissue tracking: applications for brain MRI classification

John Melonakos, Yi Gao, Allen Tannenbaum

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 651218 (2007) https://doi.org/10.1117/12.710063
Event: Medical Imaging, 2007, San Diego, CA, United States

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.

Citation Download Citation

John Melonakos, Yi Gao, and Allen Tannenbaum "Tissue tracking: applications for brain MRI classification", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651218 (3 March 2007); https://doi.org/10.1117/12.710063

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