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Updated: May 11, 2026

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

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

Published on: August 30, 2013

Decision support system for breast cancer detection using mammograms.

Karthikeyan Ganesan¹, Rajendra U Acharya, Chua K Chua

¹Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore. g.karthikeya@gmail.com

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|May 3, 2013

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Automated analysis of mammograms can improve breast cancer detection accuracy. A new pipeline using advanced feature extraction and a decision tree classifier achieved high accuracy in distinguishing normal, benign, and malignant cases.

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Machine Learning in Healthcare

Background:

Mammography is a primary breast cancer screening tool, but human interpretation introduces variability and potential errors.
Interobserver and intraobserver variability, along with image quality and radiologist expertise, affect mammogram sensitivity.
Automated techniques are needed to standardize breast cancer diagnosis and grading, reducing diagnostic errors.

Purpose of the Study:

To develop and evaluate an automated classification pipeline for improved accuracy in differentiating normal, benign, and malignant mammograms.
To assess the performance of various feature extraction and selection methods in breast cancer detection.
To compare the effectiveness of multiple machine learning classifiers for mammogram analysis.

Main Methods:

Keywords:

Mammogram cancer classification feature selection texture

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Last Updated: May 11, 2026

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

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

Published on: August 30, 2013

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Feature extraction using higher-order spectra, local binary pattern, Laws' texture energy, and discrete wavelet transform.
Feature selection employing sequential forward, backward, plus-l-takeaway-r, individual, and branch-and-bound methods with Mahalanobis distance.
Classification using decision tree, fisher, linear discriminant, nearest mean, Parzen, and support vector machine classifiers.

Main Results:

The decision tree classifier achieved excellent performance across both datasets.
Classification accuracy reached 91% on the Digital Database for Screening Mammography and 96.8% on the Singapore Anti-Tuberculosis Association CommHealth database.
High sensitivity and specificity were demonstrated by the decision tree classifier.

Conclusions:

The proposed automated classification pipeline effectively enhances the accuracy of breast cancer mammogram interpretation.
The decision tree classifier shows superior performance compared to other evaluated methods for mammogram analysis.
Automated systems hold significant potential for standardizing and improving breast cancer screening accuracy.