A Novel 3D Joint Markov-Gibbs Model for Extracting Blood Vessels from PC---MRA Images

Authors:
Ayman El-Baz;Georgy Gimel'Farb;Robert Falk;Mohamed Abou El-Ghar;Vedant Kumar;David Heredia
Affiliations:
Bioimaging Laboratory, University of Louisville, Louisville, USA;Department of Computer Science, University of Auckland, Auckland, New Zealand;Director, Medical Imaging Division, Jewish Hospital, Louisville, USA;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt;Bioimaging Laboratory, University of Louisville, Louisville, USA;Bioimaging Laboratory, University of Louisville, Louisville, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

New techniques for more accurate segmentation of a 3D cerebrovascular system from phase contrast (PC) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe PC---MRA images and desired maps of regions by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modified the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.