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Updated: May 24, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Computerized scheme for evaluating mammographic phantom images.

Masaki Asahara¹, Yoshie Kodera

¹Graduate School of Medical Sciences, Nagoya University, Nagoya, Aichi, Japan. asahara@med.kagawa-u.ac.jp

Medical Physics

|March 3, 2012

Summary

A new computer algorithm automatically evaluates mammography phantom images, showing strong correlation with human expert assessments. This tool enhances quality control for mammography systems.

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Area of Science:

Medical Imaging
Radiology
Image Analysis

Background:

Mammography quality control is crucial for accurate diagnoses.
The American College of Radiology (ACR) phantom is a standard tool for assessing mammography system performance.
Manual evaluation of ACR phantom images can be subjective and time-consuming.

Purpose of the Study:

To develop and validate a computer algorithm for automated evaluation of ACR mammography accreditation phantom images.
To compare the algorithm's performance against human observer studies.

Main Methods:

The algorithm utilizes edge detection, nonuniformity correction, and magnification correction.
Image matching techniques calculate cross-correlation coefficients.
It assesses target shape, contrast, and circularity for specific phantom features.

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Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

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Main Results:

The algorithm's outputs showed high correlation coefficients with skilled observer results (ranging from 0.83 to 0.90).
Specifically, cross-correlation coefficients for template matching and target shape, contrast, and circularity indices demonstrated strong agreement.
The algorithm effectively indicates the quality of mammography phantom images.

Conclusions:

Automated image evaluation using this algorithm is a valuable tool for mammography quality control.
The algorithm provides objective and reproducible assessments, complementing traditional observer studies.
This technology can improve the efficiency and reliability of mammography unit quality assurance.