Deformable Organisms for Medical Image Analysis
نویسندگان
چکیده
teraction, global-to-local deformations, shape statistics, setting low-level parameters, and incorporating new forces or energy terms. However, incorporating expert knowledge to automatically guide deformations can not be easily and elegantly achieved using the classical deformable model low-level energy-based fitting mechanisms. In this chapter we review Deformable Organisms, a decision-making framework for medical image analysis that complements bottom–up, data-driven deformable models with top–down, knowledgedriven mode-fitting strategies in a layered fashion inspired by artificial life modeling concepts. Intuitive and controlled geometrically and physically based deformations are carried out through behaviors. Sensory input from image data and contextual knowledge about the analysis problem govern these different behaviors. Different deformable organisms for segmentation and labeling of various anatomical structures from medical images are also presented in this chapter.
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