Volumetric Segmentation Using Shape Models In The Level Set Framework

  • Fuxing Yang
  • Jasjit S. Suri
  • Milan Sonka
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

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.


Active Contour SHAPE Model Active Contour Model Active Appearance Model Active Shape Model 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Fuxing Yang
    • 1
  • Jasjit S. Suri
    • 2
  • Milan Sonka
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of IowaIowa CityUSA
  2. 2.Eigen LLCGrass ValleyUSA

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