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A Latent Source Model for Patch-Based Image Segmentation.

George H Chen1, Devavrat Shah1, Polina Golland1

  • 1Massachusetts Institute of Technology, Cambridge MA 02139, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 19, 2016
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Summary
This summary is machine-generated.

This study provides the first theoretical performance guarantee for patch-based nearest-neighbor and weighted majority voting medical image segmentation methods. The new probabilistic model explains why and how well these popular nonparametric techniques achieve accurate results.

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Machine learning

Background:

  • Patch-based nearest-neighbor and weighted majority voting methods are popular for medical image segmentation.
  • Despite empirical success, a theoretical understanding of these nonparametric approaches is lacking.

Purpose of the Study:

  • To bridge the theoretical gap by providing performance guarantees for nearest-neighbor and weighted majority voting segmentation.
  • To introduce a new probabilistic model for patch-based image segmentation.

Main Methods:

  • Developed a new probabilistic model for patch-based image segmentation.
  • Incorporated a novel local property describing patch similarity.
  • Integrated theories from natural imagery patch modeling and nonparametric classification.

Main Results:

  • Derived a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation.
  • Introduced a new patch-based segmentation algorithm based on the developed model.
  • Demonstrated that existing algorithms are special cases of the new approach.

Conclusions:

  • The new probabilistic model provides a theoretical foundation for patch-based segmentation methods.
  • The derived algorithm offers a unified framework for various patch-based segmentation techniques.
  • This work advances the understanding and application of nonparametric methods in medical image segmentation.