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Artificial Intelligence
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Computer Vision, Graphics, and Image Processing
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Artificial Intelligence
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Computer Vision, Graphics, and Image Processing
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IEEE Computer Graphics and Applications
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ASST '87 Aachener Symposium für Signaltheorie: Mehrdimensionale Signale und Bildverarbeitung
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Mathematics and Computers in Simulation
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This paper presents an approach to the modeling, analysis, and visualization of left ventricle motion and deformation. The authors' modeling of left ventricle shape and motion as a hierarchical representation enables them to develop a promising noninvasive technique for monitoring heart dynamics where both image analysis and image synthesis are involved. The proposed hierarchical motion model of left ventricle is constructed by combining several existing simple models and is able to capture major motion and deformation components of the left ventricle. The hierarchical decomposition characterizes the left ventricle motion and deformation in a coarse-to-fine fashion and leads to computationally efficient estimation algorithms. The authors estimate the global rigid motion of the left ventricle by establishing a time-varying object-centered coordinate system. The global deformations of the left ventricle are obtained by fitting the given data to superquadric modeling primitives. The local deformations are estimated by a tensor-description approach that is based on the locally deformable surface obtained by constructing spherical harmonic local surface from the residues of global shape estimation. The authors also describe in this paper methods of image synthesis and dynamic animation for visualizing the estimated results of the time-varying left ventricle shape, motion, and deformations. These animation results are consistent with the apparent motion pattern of the left ventricle and therefore show the success of the authors' hierarchical decomposition based approach.