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Updated: Dec 12, 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

Spatially localized sparse representations for breast lesion characterization.

Keni Zheng¹, Chelsea Harris¹, Predrag Bakic²

¹Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, 1200 N. DuPont Hwy, Dover, DE, 19901-2277, USA.

Computers in Biology and Medicine

|August 10, 2020

Summary

This summary is machine-generated.

This study introduces sparse representation methods for classifying breast lesions from mammograms. The novel approach achieved 89.1% AUC, improving diagnostic accuracy for benign versus malignant states.

Keywords:

Breast lesion characterization CAD/CADx Sparse analysis

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Last Updated: Dec 12, 2025

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Area of Science:

Medical Imaging
Computational Biology
Machine Learning

Background:

Sparse representation in high-dimensional spaces is a growing research area.
Accurate classification of breast lesions is crucial for patient outcomes.

Purpose of the Study:

To develop and evaluate sparse representation-based methods for classifying breast lesions in mammograms.
To improve the accuracy of distinguishing benign from malignant breast lesions.

Main Methods:

Proposed a spatial block decomposition method for sparse analysis.
Developed two classification strategies: maximum a posteriori probability (BBMAP-S) and log likelihood function (BBLL-S).
Utilized cross-validation on mammogram datasets.

Main Results:

Achieved an Area Under the Receiver Operating Curve (AUC) of 89.1% using 30-fold cross-validation.
Demonstrated that the integrative sparse analysis addresses ill-posed approximation problems.
The approach is effective for classifying breast lesions into benign and malignant categories.

Conclusions:

The proposed sparse analysis method effectively classifies breast lesions.
The BBLL-S decision function shows potential for higher accuracy than BBMAP-S due to bias consideration.