Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust active appearance model matching
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Piecewise and local image models for regularized image restoration using cross-validation
IEEE Transactions on Image Processing
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Parallelization of particle filter algorithms
ISCA'10 Proceedings of the 2010 international conference on Computer Architecture
Pattern Recognition and Image Analysis
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Cardiovascular researchers are constantly developing new and innovative medical imaging technologies, striving to improve the understanding, diagnosis, and treatment of cardiovascular dysfunction.Combining these sophisticated imaging methods with advancements in image understanding via computational intelligence will continue to advance the frontier of cardiovascular medicine. Recently, researchers have turned to a new class of tissue motion imaging techniques, including displacement encoding with stimulated echoes (DENSE) in cardiac magnetic resonance (cMR) imaging, to directly quantify cardiac displacement and produce accurate spatiotemporal measurements of myocardial strain, twist, and torsion. The associated analysis of DENSE cMR and other tissue motion imagery, however, represents a major bottleneck in the study of intramyocardial mechanics. In the computational intelligence area of deformable models, this paper develops an automated motion recovery technique termed active trajectory field models (ATFMs) geared toward these new motion imaging protocols, offering quantitative physiological measurements without the pains of manual analyses. This novel generative deformable model exploits both image information and prior knowledge of cardiac motion, utilizing a point distributionmodel derived froma training set of myocardial trajectory fields to automatically recover cardiac motion froma noisy image sequence. The effectiveness of the ATFM method is demonstrated by quantifying myocardial motion in 2-D short-axis murine DENSE cMR image sequences both before and after myocardial infarction, producing results comparable to existing semiautomatic analysis methods.