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Related Experiment Video

Updated: Jun 10, 2026

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

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

Published on: December 15, 2014

Automatic breast parenchymal density classification integrated into a CADe system.

G Bueno¹, N Vállez, O Déniz

¹Universidad de Castilla-La Mancha, E.T.S. Ingenieros Industriales, Avda. Camilo José Cela, 3, 13071, Ciudad Real, Spain. gloria.bueno@uclm.es

International Journal of Computer Assisted Radiology and Surgery

|August 6, 2010

Summary

This summary is machine-generated.

Automated breast density classification using machine learning techniques improves mammogram interpretation in dense breast tissue. This aids in the detection and analysis of breast lesions, enhancing diagnostic accuracy.

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

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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Breast parenchymal density is a significant risk factor for breast cancer.
Dense breast tissue complicates mammogram interpretation and lesion detection.

Purpose of the Study:

To develop and evaluate automated breast density classification methods.
To enhance breast lesion detection and analysis in mammography, particularly in dense tissue.

Main Methods:

Utilized k-NN, SVM, and LBN with principal component analysis for texture-based breast density classification.
Integrated classification techniques into a Computer-Aided Detection (CADe) system.
Classified mammograms according to Breast Imaging Reporting and Data System (BIRADS) categories.

Main Results:

Achieved up to 84% correct classification rate on a dataset of 322 mammograms.
Demonstrated enhanced lesion detectability within the developed CADe system.
Validated the ability to distinguish local attenuation without local tissue density constraints.

Conclusions:

Automated breast density classification tools can improve lesion detection in dense and heterogeneous breast tissue.
The developed system aids in analyzing lesion characteristics irrespective of surrounding tissue density.
These advancements support more accurate breast cancer screening and diagnosis.