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- W2161377000 abstract "Previously, Deformable organisms were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images." @default.
- W2161377000 created "2016-06-24" @default.
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- W2161377000 date "2005-04-29" @default.
- W2161377000 modified "2023-09-29" @default.
- W2161377000 title "Physics-based deformable organisms for medical image analysis" @default.
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- W2161377000 doi "https://doi.org/10.1117/12.594856" @default.
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