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The Mammary Glands

The Mammary Glands

The female breast is a hemispheric projection of variable size positioned anterior to the pectoralis major and serratus anterior muscles. A fascia layer composed of dense, irregular connective tissue connects it to these muscles.
Each breast features a pigmented projection known as the nipple, through which milk emerges via closely spaced openings of ducts, referred to as lactiferous ducts. Surrounding the nipple is a circular pigmented area of skin named the areola, which appears rough due to...

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

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Dense-breast classification using image similarity.

Takuji Tsuchida^1,2, Toru Negishi³, Yuka Takahashi⁴

¹Department of Radiological Technology, Saitama Saiseikai Kawaguchi General Hospital, 5-11-5 Nishikawaguchi, Kawaguchi City, Saitama, 332-8558, Japan. t-tsuchida@saiseikai.gr.jp.

Radiological Physics and Technology

|May 8, 2020

Summary

This study introduces an automated mammography analysis to reduce subjective errors in breast density classification. The new image similarity method achieved higher accuracy than conventional techniques for dense and non-dense breast identification.

Keywords:

Auto analysis Dense-breast classification Mammogram NCC Similarity Template matching

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Area of Science:

Radiology
Medical Imaging Analysis
Computer-Aided Diagnosis

Background:

Subjective physician evaluation of mammography images introduces variability in breast density classification.
Accurate breast density assessment is crucial for mammography interpretation and breast cancer risk stratification.

Purpose of the Study:

To develop and validate an automated method for mammary gland analysis on mammography images.
To reduce inter-physician variability in breast density classification using pixel value and image similarity analysis.

Main Methods:

Mammography images were classified into dense and non-dense breast groups based on subjective assessment.
A novel image similarity evaluation method using zero-mean normalized cross-correlation was applied.
The proposed method was compared against the conventional variance histogram analysis method.

Main Results:

The proposed automated method achieved a concordance rate of 89.1% for breast classification.
The conventional method showed a concordance rate of 79.2% for breast classification.
The image similarity analysis successfully classified breast density, minimizing ambiguity.

Conclusions:

Automated analysis of mammography images using pixel value and pattern recognition enhances breast density classification accuracy.
The proposed image similarity method offers a more objective and reliable alternative to subjective physician evaluations.
This approach has the potential to improve the consistency and accuracy of mammographic screening.