title: Tissue Tracking: Applications for Brain MRI Classification.
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 atissue trackingframework. 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.
authors.affiliation: Georgia Institute of Technology, 414 Ferst Dr, Atlanta, GA, USA.
authors.forename: John
authors.initials: J
authors.lastname: Melonakos
authors.affiliation: Georgia Institute of Technology, 414 Ferst Dr, Atlanta, GA, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Georgia Institute of Technology, 414 Ferst Dr, Atlanta, GA, USA.
authors.forename: Allen
authors.initials: A
authors.lastname: Tannenbaum
keywords: Bayesian Classification; Brain MRI; Expectation-Maximization; Image Segmentation