TY - JOUR
AR - RDI-2020-1-102
TI - A Digital Mammogram Auto Classification Method Based on Fibroglandular Breast Tissue Density Evaluation by Image Similarity
AU - Takuji, Tsuchida
AU - Toru, Negishi
AU - Toshihiro Kai, 
JO - Radiology and Medical Diagnostic Imaging
PY - 2020
DA - Tue 03, Mar 2020
SN - 2613-7836
DO - http://dx.doi.org/10.31487/j.RDI.2020.01.02
UR - https://www.sciencerepository.org/a-digital-mammogram-auto-classification-method_RDI-2020-1-102 
KW - Digital mammogram, image analysis, automated classification, image similarity, fibroglandular breast tissue density
AB - 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.