A Digital Mammogram Auto Classification Method Based on Fibroglandular Breast Tissue Density Evaluation by Image Similarity

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Author Info

Takuji Tsuchida Toru Negishi Toshihiro Kai

Corresponding Author

Takuji Tsuchida
Department of Radiological Technology, Saitama Saiseikai Kawaguchi General Hospital, Nishikawaguchi, Kawaguchi City, Saitama, Japan

A B S T R A C T

It is crucial to assess the fibroglandular breast tissue density to define the degree of the risk that the healthy breast tissue will obscure the lesions. Subjective assessment criteria, proceed by the reading physicians by using the mammary gland concentrations on mammograms, are defined as the breast classification method. However, due to the existence of between observer’s variability, a computer-based quantitative classification method is required. The conventional method classifies according to the ratio of the Dmg region (mammary gland region) to the Dc region (fibroglandular breast tissue region). However, this does not include subjective evaluation elements. The purpose of this study is to improve the concordance rate with the subjective assessment by performing an automated classification based on image similarity. First, 130 cases of right MLO (Medio-Lateral Oblique) images, subjectively classified as fatty tissue, mammary gland diffuseness, non-uniform high density, and high density, were reclassified to two groups; fatty tissue and mammary gland diffuseness as Non-Dense breast, and non-uniform high density and high density as Dense breast. Next, as for evaluation images, 33 cases of both sides MLO images taken by different mammography devices were used. Finally, the image similarity analysis result using Normalized Cross-Correlation between the search image and the evaluation image was derived, and the degree of coincidence of subjective breast classification was calculated. As a result, the concordance rate between the conventional method and the subjective evaluation results of this method improved from 73 % to 91 %, and the kappa coefficient improved from 0.49 to 0.81. This result indicates that our approach is more useful for the automated classification of mammograms based on fibroglandular breast tissue density.

Article Info

Article Type

Research Article

Publication history

Received: Wed 05, Feb 2020
Accepted: Wed 19, Feb 2020
Published: Tue 03, Mar 2020

Copyright

© 2023 Takuji Tsuchida. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hosting by Science Repository.
DOI: 10.31487/j.RDI.2020.01.02