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Updated: Jun 15, 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

MammoSys: A content-based image retrieval system using breast density patterns.

Júlia E E de Oliveira¹, Alexei M C Machado, Guillermo C Chavez

¹Universidade Federal de Minas Gerais, Departamento de Ciência da Computação, Av. Antônio Carlos, 6627, 31270-901, Belo Horizonte, MG, Brazil. julia@dcc.ufmg.br

Computer Methods and Programs in Biomedicine

|March 9, 2010

Summary

This summary is machine-generated.

This study introduces a content-based image retrieval system for mammography, utilizing breast density and advanced analysis. The system achieves high precision, showing promise for computer-aided diagnosis in medical imaging.

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Area of Science:

Radiology and Medical Imaging
Computer Science and Artificial Intelligence
Biomedical Engineering

Background:

Mammography is crucial for breast cancer screening.
Efficient retrieval of mammograms from large databases is challenging.
Accurate breast density assessment is vital for diagnostic interpretation.

Purpose of the Study:

To develop a content-based image retrieval (CBIR) system for mammograms.
To leverage breast density classification for image retrieval.
To integrate the system with the Image Retrieval in Medical Applications (IRMA) database.

Main Methods:

Utilized breast density categories defined by the American College of Radiology.
Employed two-dimensional principal component analysis (2D-PCA) for texture characterization and dimensionality reduction.
Implemented a support vector machine (SVM) for the retrieval process.

Main Results:

Achieved average precision rates between 83% and 97%.
Tested on a dataset comprising 5024 mammographic images.
Demonstrated effective texture representation and dimensionality reduction.

Conclusions:

The developed CBIR system shows high accuracy in retrieving mammograms.
The system has significant potential as a preliminary step in computer-aided diagnosis (CAD) frameworks.
Breast density-based retrieval is a viable approach for medical image databases.