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Deformable Organisms For Medical Image Analysis

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Deformable Models

In medical image analysis strategies based on deformable models, controlling the deformations of models is a desirable goal in order to produce proper segmentations. Interaction, 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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  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Dr. Ghassan Hamarneh & Chris McIntosh

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Hamarneh, G., McIntosh, C. (2007). Deformable Organisms For Medical Image Analysis. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68413-0_12

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