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Updated: May 1, 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

Spatial recurrence analysis: a sensitive and fast detection tool in digital mammography.

T L Prado¹, P P Galuzio¹, S R Lopes¹

¹Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.

Chaos (Woodbury, N.Y.)

|April 5, 2014

Summary

This study introduces a novel spatial recurrence quantification analysis for faster digital mammographic image processing. The method effectively detects breast lesions, improving diagnostic accuracy and reducing image artifacts.

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Area of Science:

Medical Imaging
Biomedical Engineering
Computational Pathology

Background:

Accurate breast cancer diagnostics rely on efficient digital mammographic image processing.
Subtle breast lesions, both benign and malignant, often evade untrained visual inspection, necessitating advanced image analysis techniques.
Existing image processing methods face challenges with spurious image fragments, potentially impacting diagnostic reliability.

Purpose of the Study:

To introduce a novel digital mammographic image analysis method for efficient and accurate breast lesion detection.
To leverage spatial recurrence quantification analysis as an extension of time recurrence analysis for image processing.
To improve the detection of subtle breast lesions while enhancing control over image artifacts.

Main Methods:

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Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

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Last Updated: May 1, 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

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

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Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

The proposed method utilizes the concept of spatial recurrence.
It employs spatial recurrence quantification analysis (SRQA), an adaptation of time recurrence analysis to image data.
SRQA quantifies spatial patterns within digital mammograms.

Main Results:

The recurrence-based quantifiers demonstrate efficacy in evidencing breast lesions.
The method performs comparably to the best-established standard image processing techniques.
SRQA offers superior control over spurious fragments in mammographic images compared to existing methods.

Conclusions:

Spatial recurrence quantification analysis presents a promising approach for digital mammographic image analysis.
The method enhances the detection of breast lesions, contributing to more efficient and reliable breast cancer diagnostics.
SRQA provides a robust alternative for image processing, minimizing the impact of image noise and artifacts.