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Segmentation and tracking of myocardial boundaries using dynamic programming
Athira J. Jacob,
Published in Springer Verlag
2017
Volume: 10124 LNCS
   
Pages: 118 - 126
Abstract
Increasing interest in quantification of local myocardial properties throughout the cardiac cycle from tagged MR (tMR) calls for treatment of the cardiac segmentation problem as a spatio-temporal task. The method presented for myocardial segmentation, uses dynamic programming to choose the optimal contour from a set of possible contours subject to maximizing a cost function. Robust Principle Component Analysis (RPCA) is used to decompose the time series into low rank and sparse components and initialization of the contour is done on the low rank approximation. The 3D nature of the images and tag grid location is incorporated into the cost function to get more robust results. 3D+t segmentation of patient data is achieved by propagating contours spatially and temporally. The method is ideal as a pre-processing step in motion quantification and strain rate mapping algorithms. © Springer International Publishing AG 2017.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Verlag
ISSN03029743
Open AccessNo
Concepts (23)
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    Approximation theory
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    Computation theory
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    Computational methods
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    Conformal mapping
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    Cost functions
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    Heart
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    HOSPITAL DATA PROCESSING
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    Image segmentation
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    Magnetic resonance imaging
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    Medical imaging
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    Principal component analysis
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    Strain rate
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    SURFACE DISCHARGES
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    Time series analysis
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    CARDIAC IMAGES
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    Deformable contours
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    LOW RANK APPROXIMATIONS
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    MR IMAGES
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    MYOCARDIAL SEGMENTATIONS
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    Principle component analysis
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    ROBUST PCA
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    SEGMENTATION AND TRACKING
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    Dynamic programming