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Deformable Models pp 161–207Cite as

Volumetric Segmentation Using Shape Models In The Level Set Framework

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It is an arduous task to extract the structural detail in medical images because of noisy or partial volume effects, or incomplete information. However, expert-identified segmentation results are often available, and most of the structures to be extracted have a similar shape from one subject to another. Then to model the family of shapes and restricting the new structure to be extracted within the class is of particular interest. Generally, active shape models are used to implement this framework. However, the definition of the image term is the most challenging factor in such an approach. The level set methods define a powerful optimization framework via an implicit description of different shapes in various dimensional spaces. This advantage can help recover objects of interest by the propagation of curves or surfaces. The properties of the level set methods support complex topologies, considered in higher dimensions, are implicit, intrinsic, and parameter free. In this chapter, we give a review of the level set method and show the usage of the shape models for segmentation of objects in 2D and 3D in a level set framework via regional information.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA

    Fuxing Yang & Milan Sonka

  2. Eigen LLC, 95945, Grass Valley, CA, USA

    Prof. Jasjit S. Suri

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  1. Fuxing Yang
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  3. Milan Sonka
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Yang, F., Suri, J.S., Sonka, M. (2007). Volumetric Segmentation Using Shape Models In The Level Set Framework. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_6

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  • DOI: https://doi.org/10.1007/978-0-387-68343-0_6

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