Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI.
作者： Alexander Andreopoulos , John K. Tsotsos
DOI: 10.1016/J.MEDIA.2007.12.003
关键词: Active shape model 、 Optimization algorithm 、 High dimensional 、 Artificial intelligence 、 Optimization problem 、 Segmentation 、 Mr images 、 Computer vision 、 Statistical model 、 Mathematics 、 Active appearance model
摘要: Abstract We present a framework for the analysis of short axis cardiac MRI, using statistical models shape and appearance. The integrates temporal structural constraints avoids common optimization problems inherent in such high dimensional models. first contribution is introduction an algorithm fitting 3D active appearance (AAMs) on MRI. observe 44-fold increase speed segmentation accuracy that par with Gauss–Newton optimization, one most widely used algorithms problems. second involves investigation hierarchical 2D + time (ASMs), integrate simultaneously improve AAM based segmentation. obtain encouraging results (endocardial/epicardial error 1.43 ± 0.49 mm/1.51 ± 0.48 mm) 7980 MR images acquired from 33 subjects. have placed our dataset online, community to use build upon.
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