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Related Experiment Video

Updated: Dec 26, 2025

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

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.

Annarita Fanizzi¹, Teresa M A Basile^2,3, Liliana Losurdo⁴

¹I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", viale O. Flacco 65, Bari, Italy.

BMC Bioinformatics

|March 14, 2020

Summary

This study introduces an automatic model to help radiologists detect breast cancer in mammograms. The model effectively distinguishes between normal/abnormal and benign/malignant tissues, improving early cancer diagnosis.

Keywords:

Computer-aided diagnosis Digital mammograms Feature selection Haar wavelet transform Microcalcifications Minimum eigenvalue algorithm Random forest SURF

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Last Updated: Dec 26, 2025

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

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Published on: August 30, 2013

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

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Published on: July 26, 2014

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Mammography is crucial for early breast cancer detection.
Radiologists face challenges in diagnosing microcalcifications.
An automated tool can support radiologists in mammogram interpretation.

Purpose of the Study:

To develop an automatic binary model for tissue discrimination in digital mammograms.
To compare feature selection methods for improved learning performance.
To enhance diagnostic support for radiologists.

Main Methods:

Extracted textural features using Haar wavelet decompositions.
Utilized Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg) for interest point detection.
Trained a Random Forest classifier using filter and embedded feature selection techniques.

Main Results:

Tested on 260 ROIs from the BCDR public database.
Achieved a median AUC of 98.16% for normal/abnormal classification.
Reached a median AUC of 92.08% for benign/malignant classification with 97.31% and 88.46% accuracy, respectively.
SURF and MinEigen algorithms proved informative for microcalcification characterization.

Conclusions:

The embedded feature selection method was more parsimonious than the filter method.
The developed model shows promising performance comparable to existing methods.
The automated system can aid radiologists in diagnosing challenging lesions like microcalcifications.