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Updated: Jul 10, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Digital mammographic computer aided diagnosis (CAD) using adaptive level set segmentation.

John E Ball1, Lori Mann Bruce

  • 1Navy Surface Warfare Center, Dahlgren, VA 22485, USA. john.e.ball@navy.mil

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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This study introduces an adaptive level set segmentation method (ALSSM) for mammographic computer-aided diagnosis (CAD). The ALSSM achieves 87% accuracy, detecting 28/30 malignant cases for improved breast cancer diagnosis.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Segmentation

Background:

  • Mammography is crucial for breast cancer screening.
  • Accurate segmentation of suspicious masses is vital for reliable computer-aided diagnosis (CAD).
  • Existing CAD systems face challenges in segmenting difficult cases.

Purpose of the Study:

  • To develop and evaluate an adaptive level set segmentation method (ALSSM) for segmenting suspicious masses in mammography.
  • To improve the accuracy and efficacy of CAD systems in detecting malignant breast masses.
  • To introduce an adaptive speed function for enhanced level set segmentation control.

Main Methods:

  • The study employed an adaptive level set segmentation method (ALSSM) operating in the polar domain.

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  • The ALSSM adaptively adjusts the border threshold at each angle for high-quality segmentation.
  • Sixty difficult mammographic cases (30 benign, 30 malignant) from the Digital Database of Screening Mammography (DDSM) were analyzed.

    • Main Results:

    • The ALSSM achieved an overall accuracy of 87%.
    • The area under the receiver operating characteristic curve (A(Z)) was 0.9687.
    • The system successfully detected 28 out of 30 malignant cases, demonstrating high sensitivity.

    Conclusions:

    • The ALSSM provides excellent segmentation and classification results for mammographic masses.
    • The proposed method shows significant potential for improving the performance of CAD systems.
    • Results compare favorably to previous CAD systems utilizing the DDSM database.