2021 Volume 39 Issue 2 Pages 77-89
It is not easy to acquire case image data, which is important for checking and improving the performance of computer-aided diagnosis (CAD) systems, and various attempts have been made to artificially generate case images. In this study, Elliptic Fourier descriptor (EFD), one of the quantitative evaluation methods for contour information, was used to analyze the contours of calcification distribution shapes and to investigate a method to apply it to the generation of artificial mammograms with calcifications. The shape information of the calcification distribution was converted to an elliptical Fourier descriptor, and principal component analysis was performed. The first to fourth principal components and the area obtained were used as features and discriminated using the SVM (Support Vector Machine), It was confirmed that three relatively high malignancy categories (clustered, linear, and segmental) among the five calcification distributions in the BI-RADS (Breast Imaging Reporting and Data System) could be identified with 90.4％ accuracy. We also developed a method to artificially generate mammograms with various calcification distributions by randomly arranging calcifications extracted in advance, within the contours of the generated calcification distribution shapes and embedding them in other mammograms. Seven radiologists evaluated the generated 15 artificial mammograms and 15 real mammograms by the index of "reality" of the calcifications (0: fake to 100: real). A two-tailed t-test showed that there was no statistically significant difference in the rating of “reality” between the artificial mammograms and the real mammograms. Averaged AUC of ROC analysis was 0.466. It has been confirmed that the method developed in this study can generate artificial mammograms with calcifications with various distribution shapes at a level that is indistinguishable from actual case images. In the future, it is expected to be used to generate image data for performance evaluation of CAD systems and as a data argumentation for deep learning.
