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Related Experiment Video

Updated: Mar 30, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Automatic detection of abnormalities in mammograms.

Zobia Suhail1, Mansoor Sarwar2, Kashif Murtaza3

  • 1Punjab University College of Information Technology (PUCIT), University of the Punjab, Lahore, Pakistan. zobia.suhail@pucit.edu.pk.

BMC Medical Imaging
|November 8, 2015
PubMed
Summary

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Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.

Medical & biological engineering & computing·2018

A new low-cost Computer Aided Diagnostic (CAD) system effectively detects calcifications in mammograms. This method uses a novel "Ratio Energy" feature for efficient identification of microcalcification and macrocalcification, aiding early breast cancer diagnosis.

Area of Science:

  • Medical Image Processing
  • Computer Aided Diagnosis (CAD)

Background:

  • Rising interest in medical image processing and CAD systems for diagnostic assistance.
  • High cost of existing CAD systems, particularly in developing countries, necessitates low-cost solutions.
  • Mammogram classification often focuses on detecting calcification and abnormal masses as early breast cancer indicators.

Purpose of the Study:

  • To develop a low-cost Computer Aided Diagnostic (CAD) system for mammogram classification.
  • To introduce an efficient technique for detecting calcifications, a key early symptom of breast cancer.
  • To improve the accessibility of diagnostic tools in resource-limited settings.

Main Methods:

  • Proposed a scale-specific blob detection technique utilizing supervised learning for scale selection.

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  • Introduced a novel feature, "Ratio Energy," computed from pixel energy at two scales for efficient blob detection.
  • Achieved linear running time complexity with respect to image size due to feature simplicity and post-processing.

    • Main Results:

    • Successfully identified and highlighted two types of calcification: microcalcification and macrocalcification.
    • Results visualized using circular boundaries around detected calcification areas, ensuring clarity for radiologists.
    • Demonstrated visible and satisfactory detection outcomes.

    Conclusions:

    • The developed CAD system aids radiologists in verifying diagnoses by identifying calcifications.
    • The new method leverages the clustered, small-size property of microcalcification for detection.
    • The system offers encouraging results for the early detection of breast cancer.