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Related Experiment Video

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

Textural assessment in digital mammograms.

F E Trujillo-Zamudio1, J Márquez, Y Villaseñor

  • 1Inst. de Fisica, UNAM, Mexico City, Mexico. flaviotrujillo@gmail.com

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study evaluated digital mammogram features to detect cancer. Textural and morphological analysis showed limited diagnostic accuracy, correctly identifying cancer in 46% of cases.

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Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Engineering
  • Oncology

Background:

  • Digital mammography is crucial for breast cancer screening.
  • Accurate interpretation of mammograms is essential for early cancer detection.
  • BI-RADS 4 and 5 classifications indicate suspicious or malignant findings, necessitating further investigation.

Purpose of the Study:

  • To assess the effectiveness of textural and morphological parameters in characterizing digital mammograms.
  • To differentiate between cancerous and non-cancerous cases using image analysis techniques.
  • To evaluate the diagnostic performance of proposed parameters in a clinical setting.

Main Methods:

  • Analysis of digital mammograms from patients with BI-RADS 4 or 5 classifications.

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Last Updated: Jul 10, 2026

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

Published on: August 30, 2013

Clinical Imaging of Microwave Mammography
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  • Utilizing textural roughness via the mean height-width ratio of extrema (MHWRE).
  • Employing morphological feature analysis using circularity.

    • Main Results:

    • The study achieved a correct diagnosis rate of 46% for the analyzed mammograms.
    • Incorrect diagnoses were observed in 25% of the cases.
    • A significant portion (29%) of diagnoses remained undetermined based on the tested parameters.

    Conclusions:

    • Textural and morphological parameters show potential but limited ability in definitively diagnosing breast cancer from mammograms.
    • Further refinement of image analysis techniques is needed to improve diagnostic accuracy.
    • The current parameters require enhancement for reliable clinical application in differentiating malignant findings.