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Related Experiment Video

Updated: May 25, 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

Hybrid cosine and Radon transform-based processing for digital mammogram feature extraction and classification with

Salim Lahmiri1, Mounir Boukadoum

  • 1Department of Computer Science, University of Québec at Montréal, C P 8888, SuccursaleC-V, Montréal, Québec H3C 3P8, Canada. lahmiri.salim@courrier.uqam.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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This study introduces a novel hybrid method for automated mammogram analysis, enhancing breast cancer detection accuracy. The system combines discrete cosine transform (DCT) and Radon transform for superior feature extraction and classification.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Machine Learning

Background:

  • Mammography is crucial for early breast cancer detection.
  • Automated feature extraction and classification can improve diagnostic accuracy.
  • Existing methods using DCT or Radon transform alone have limitations.

Purpose of the Study:

  • To develop and evaluate a novel hybrid methodology for automated feature extraction and classification of mammograms.
  • To improve classification accuracy in mammogram analysis compared to existing single-transform methods.

Main Methods:

  • A hybrid system sequentially applies the Discrete Cosine Transform (DCT) to capture high-frequency components.
  • The Radon transform is then applied to the DCT-processed image to extract directional features.

Related Experiment Videos

Last Updated: May 25, 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

  • Extracted features are classified using a Support Vector Machine (SVM).

    • Main Results:

    • The hybrid approach demonstrated improved classification accuracy on a test database of 100 mammograms.
    • Performance was superior to methods utilizing only DCT or Radon transform individually.
    • The combined approach effectively leverages high-frequency and directional information.

    Conclusions:

    • The proposed hybrid DCT-Radon transform methodology offers a more effective approach for automated mammogram analysis.
    • This technique shows promise for enhancing the accuracy of computer-aided diagnosis systems in mammography.
    • Further validation on larger datasets is warranted to confirm clinical utility.