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Carotid artery segmentation using an outlier immune 3D active shape models framework.

Karim Lekadir1, Guang-Zhong Yang

  • 1Visual Information Processing Group, Department of Computing Imperial College London, United Kingdom. lekadir@doc.ic.ac.uk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 16, 2007
PubMed
Summary

This study introduces an outlier-immune 3D active shape model for robust carotid artery segmentation. The novel framework accurately assesses plaque burden by effectively handling outliers in landmark data.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computer Vision

Background:

  • Accurate carotid artery segmentation is crucial for plaque burden assessment.
  • Traditional 3D active shape models struggle with outliers, compromising segmentation accuracy.
  • Robustness against outliers is essential for reliable volumetric analysis.

Purpose of the Study:

  • To present an outlier-immune 3D active shape model framework.
  • To achieve robust volumetric segmentation of the carotid artery.
  • To enhance plaque burden assessment accuracy through improved segmentation.

Main Methods:

  • Developed a shape metric invariant to scaling, rotation, and translation using inter-landmark distance ratios.
  • Implemented outlier detection and correction based on shape dissimilarity and appearance information.

Related Experiment Videos

  • Integrated geometrical knowledge from outlier analysis to optimize feature point search and introduced a combined intensity-phase search.

    • Main Results:

    • The proposed framework demonstrates robust volumetric segmentation of the carotid artery.
    • The outlier handling technique effectively manages a significant presence of outliers.
    • Improved overall search accuracy and reduced outlier presence in feature point detection.

    Conclusions:

    • The outlier-immune 3D active shape model provides a robust solution for carotid artery segmentation.
    • This method enhances the accuracy of plaque burden assessment.
    • The framework is capable of handling outliers independently of their amplitudes.