Markov random field modeling in computer vision
Markov random field modeling in computer vision
Image Registration by Maximization of Combined Mututal Information and Gradient Information
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Detection of Myocardial Boundaries in MR Cardio Perfusion Images
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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In this paper we present an integrated image registration algorithm for segmenting the heart muscle, the myocardium (MC). A sequence of magnetic resonance (MR) images of heart are acquired after injection of a contrast agent. An analysis of the perfusion of the contrast agent into myocardium is utilized to study its viability. Such a study requires segmentation of MC in each of the images acquired which is a difficult task due to rapidly changing contrast image the images. In this paper we present an information theoretic registration framework which integrates two channels of information, the pixel intensities and the local gradient information, to reliably and accurately segment the myocardium. In our framework, the physician hand draws contours representing the inner (the endocardium) and the outer (the epicardium) boundaries of the myocardium. These hand drawn contours are then propagated to the other images in the sequence of images acquired to segment the MC.