Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Atlas Matching Using Active Appearance Models
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
MAP MRF Joint Segmentation and Registration
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A Variational Framework for Joint Segmentation and Registration
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Nonrigid Registration of Dynamic Renal MR Images Using a Saliency Based MRF Model
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
A variational PDE based level set method for a simultaneous segmentation and non-rigid registration
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Joint non-rigid motion estimation and segmentation
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
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In this paper we propose a Markov random field (MRF) based method for joint registration and segmentation of cardiac perfusion images, specifically the left ventricle (LV). MRFs are suitable for discrete labeling problems and the labels are defined as the joint occurrence of displacement vectors (for registration) and segmentation class. The data penalty is a combination of gradient information and mutual dependency of registration and segmentation information. The smoothness cost is a function of the interaction between the defined labels. Thus, the mutual dependency of registration and segmentation is captured in the objective function. Sub-pixel precision in registration and segmentation and a reduction in computation time are achieved by using a multiscale graph cut technique. The LV is first rigidly registered before applying our method. The method was tested on multiple real patient cardiac perfusion datasets having elastic deformations, intensity change, and poor contrast between LV and the myocardium. Compared to MRF based registration and graph cut segmentation, our method shows superior performance by including mutually beneficial registration and segmentation information.