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Automated image segmentation for breast analysis using infrared images.

N Scales1, C Herry, M Frize

  • 1Department of Electronics, Carleton University, Ottawa, Ontario, Canada.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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This study presents an automated method for breast thermogram analysis to detect breast cancer. The approach successfully identifies breast regions using edge detection and segmentation techniques, paving the way for automated analysis.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Automated thermogram analysis is crucial for breast cancer detection.
  • Accurate identification of the region of interest (breast region) is a prerequisite for reliable thermogram analysis.
  • Current methods may require manual intervention, limiting full automation.

Purpose of the Study:

  • To develop a nearly fully automated approach for locating breast regions in thermal images.
  • To evaluate different image processing strategies for accurate breast segmentation.
  • To facilitate the development of a fully automated thermogram analysis package for breast cancer detection.

Main Methods:

  • Utilized a sequence of Canny edge detectors to identify body and breast boundaries.

Related Experiment Videos

  • Investigated three strategies for bottom breast boundary detection: Hough transform, longest connected edges, and edge density.
  • Focused on segmenting the breast region from the overall thermal image.

    • Main Results:

    • The proposed approach successfully located breast regions in most analyzed images.
    • Edge density and longest connected edges algorithms showed significant promise for breast segmentation.
    • The Hough transform variation also contributed to boundary identification.

    Conclusions:

    • A nearly fully automated method for breast region identification in thermograms has been demonstrated.
    • The developed techniques, particularly edge density and longest connected edges, are effective for breast segmentation.
    • This work represents a significant step towards a fully automated thermogram analysis system for breast cancer detection.