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Combining region-based and imprecise boundary-based cues for interactive medical image segmentation.

Jonathan-Lee Jones1, Xianghua Xie, Ehab Essa

  • 1Department of Computer Science, Swansea University, Swansea, UK.

International Journal for Numerical Methods in Biomedical Engineering
|November 8, 2014
PubMed
Summary

This study introduces a user-assisted image segmentation method using point and region selection. The approach offers flexible and robust segmentation for medical imaging, improving accuracy with interactive guidance.

Keywords:
Dijkstra's algorithmIVUSOCTgraph methodsegmentationshortest path

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Computational imaging

Background:

  • Accurate medical image segmentation is crucial for diagnosis and treatment planning.
  • Existing methods often struggle with image artifacts common in intravascular ultrasound (IVUS) and optical coherence tomography (OCT).
  • User guidance is often necessary for robust segmentation in challenging medical images.

Purpose of the Study:

  • To develop and validate a user-assisted image segmentation method combining region and point selection.
  • To apply and demonstrate the method's effectiveness in segmenting media-adventitia borders in IVUS and lumen borders in OCT images.
  • To show the method's versatility for segmenting various medical images beyond the specific applications.

Main Methods:

  • A novel approach combining point-based soft constraints and stroke-based regional constraints for user-assisted segmentation.
  • Formulating segmentation as an energy minimization problem on a multilayered graph, solved via shortest path search.
  • Utilizing user-defined points as attraction points and strokes to define regions of interest, calculating pixel probabilities and boundary discontinuity.

Main Results:

  • The proposed method achieves efficient and effective interactive segmentation for both open and closed curves.
  • Demonstrated successful segmentation in challenging medical images (IVUS and OCT) with artifacts like acoustic shadow and calcification.
  • Qualitative and quantitative analyses showed the method's effectiveness compared to existing interactive segmentation techniques.

Conclusions:

  • The combined user constraint approach provides flexible and robust image segmentation.
  • The method is suitable for various medical imaging applications requiring interactive guidance.
  • This technique enhances the accuracy and efficiency of medical image segmentation, particularly in the presence of image artifacts.