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Updated: May 27, 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

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution

Mohamed Meselhy Eltoukhy1, Ibrahima Faye, Brahim Belhaouari Samir

  • 1Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt. tokhy2478@yahoo.com

Computers in Biology and Medicine
|November 26, 2011
PubMed
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This study introduces a novel method for breast cancer diagnosis using digital mammograms. The approach enhances classification accuracy by optimizing feature selection and dimensionality reduction, aiding in early cancer detection.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Digital mammography is crucial for breast cancer screening.
  • Accurate diagnosis relies on effective feature extraction and classification.
  • Existing methods face challenges in dimensionality and classification accuracy.

Purpose of the Study:

  • To develop an automated method for breast cancer diagnosis from digital mammograms.
  • To enhance classification accuracy by optimizing feature selection.
  • To reduce data dimensionality for improved diagnostic efficiency.

Main Methods:

  • Utilized multi-resolution representations (wavelet, curvelet) for image transformation.
  • Developed a feature extraction method based on statistical t-test for class differentiation.

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: May 27, 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

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

  • Employed a dynamic threshold for feature optimization and Support Vector Machine (SVM) for classification.

    • Main Results:

    • Achieved improved classification accuracy rates through optimized feature selection.
    • Demonstrated effective dimensionality reduction of data features.
    • Successfully classified normal vs. abnormal tissues and benign vs. malignant tumors.

    Conclusions:

    • The proposed method shows significant potential for accurate breast cancer detection in digital mammograms.
    • Feature optimization and dimensionality reduction are key to improving diagnostic performance.
    • The approach contributes to the advancement of computer-aided diagnosis in oncology.