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Integrated Deformable Boundary Finding Using Bayesian Strategies

  • Amit Chakraborty
  • James S. Duncan
Conference paper
Part of the Fundamental Theories of Physics book series (FTPH, volume 98)

Abstract

Precise segmentation and analysis of underlying structures in an image is crucial for a variety of image analysis and computer vision applications including robot vision, pattern recognition and biomedical image processing such as image guided surgery, registration of multimodality images, cardiac motion tracking, etc. However, a robust identification and measurement of such structure is not always achievable by using a single technique that depends on a single image feature. Thus, it is necessary to make use of various image features, such as gradients, curvatures, homogeneity of intensity values, textures, etc. as well as model-based information (such as shape). Integration provides a way to make use of the rich information provided by the various information sources, whereby consistent information from the different sources are reinforced while noise and errors are attenuated. Integration is achieved in this work by using region information in addition to gradient information within the deformable boundary finding framework. The integration problem is framed in a Bayesian framework using shape and region priors to influence a gradient-based deformable boundary finding.

Keywords

Machine Intelligence Region Information Boundary Method Iterate Conditional Mode Boundary Finding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Amit Chakraborty
    • 1
  • James S. Duncan
    • 1
  1. 1.Dept. of Electrical Engineering and Diagnostic RadiologyYale UniversityNew HavenUSA

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