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Related Experiment Videos

Automated seeded lesion segmentation on digital mammograms

M A Kupinski1, M L Giger

  • 1Department of Radiology, The University of Chicago, IL 60637, USA. m-kupinski@uchicago.edu

IEEE Transactions on Medical Imaging
|December 9, 1998
PubMed
Summary

Two new methods for segmenting mass lesions in mammograms, using radial gradient index (RGI) and probabilistic models, show improved accuracy over traditional algorithms. These techniques enhance computer-aided detection of breast cancer by better matching radiologist segmentations.

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Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation.

Technology in cancer research & treatment·2004

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate segmentation of mass lesions in mammograms is crucial for computer-aided detection systems.
  • Existing segmentation methods may not fully capture the complexity of lesion shapes and backgrounds.

Purpose of the Study:

  • To develop and evaluate two novel lesion segmentation techniques for digital mammograms.
  • To compare the performance of these new methods against a conventional region growing algorithm.

Main Methods:

  • Developed a segmentation technique based on the radial gradient index (RGI).
  • Developed a segmentation technique using simple probabilistic models.
  • Both methods utilize image partitions based on gray-level information and prior knowledge of lesion shapes.

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  • Tested against a conventional region growing algorithm on a database of biopsy-proven malignant lesions.

    • Main Results:

    • The RGI-based method achieved 92% correct lesion segmentation.
    • The probabilistic model-based method achieved 96% correct lesion segmentation.
    • Both novel methods significantly outperformed the conventional region growing algorithm (62% correct segmentation) at an overlap threshold of 0.30.
    • The new algorithms demonstrated closer agreement with radiologists' lesion outlines.

    Conclusions:

    • The developed radial gradient index (RGI) and probabilistic segmentation algorithms offer superior performance for mass lesion segmentation in digital mammograms.
    • These advanced techniques improve the accuracy of computer-aided detection systems, potentially leading to earlier and more reliable breast cancer diagnosis.
    • The findings suggest a significant advancement in automated lesion delineation for mammographic analysis.