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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Computer-aided classification of mammographic masses using visually sensitive image features.

Yunzhi Wang1, Faranak Aghaei1, Ali Zarafshani1

  • 1School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.

Journal of X-Ray Science and Technology
|December 3, 2016
PubMed
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This summary is machine-generated.

This study developed a new computer-aided diagnosis (CAD) scheme using visually sensitive image features to classify breast masses from mammograms. The CAD system achieved high accuracy, aiding radiologists in distinguishing malignant from benign tumors.

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Digital mammography is crucial for breast cancer screening.
  • Computer-aided diagnosis (CAD) systems aim to improve diagnostic accuracy.
  • Radiologists rely on visually sensitive features for mass interpretation.

Purpose of the Study:

  • To develop a novel CAD scheme for classifying malignant and benign breast masses.
  • To compute visually sensitive image features mimicking radiologist interpretation.
  • To create a machine learning classifier for breast mass diagnosis.

Main Methods:

  • Retrospective analysis of 301 breast masses from digital mammograms.
  • Computation of five categories of visually sensitive image features.
Keywords:
Computer-aided diagnosis (CAD)classification of mammographic massesquantification of visually sensitive image featuresquantitative image feature selection in CAD

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  • Application of logistic regression models and selection of optimal features.
  • Development of a CAD interface displaying segmentation, features, and classification scores.

    • Main Results:

    • Achieved an area under the ROC curve (AUC) of 0.786±0.026 for single-view classification.
    • Increased AUC to 0.806±0.025 by fusing classification scores from two views.
    • Demonstrated the effectiveness of visually sensitive features in classification.

    Conclusions:

    • A new CAD scheme based on visually sensitive features was successfully developed.
    • The CAD system, with a "visual aid" interface, enhances explainability and observer confidence.
    • This approach offers an alternative to conventional CAD systems using complex texture features.