Coronary artery disease, one of the major causes of death in Japan, is said to be related to coronary artery calcification. However, health care workers are essential to examine only the heart when investigating coronary artery disease. Also, it is very difficult to extract only calcification of the heart from Non-Contrast CT. To our knowledge, 3D cardiac extraction in non-enhanced CT cardiac images has not been performed in previous studies. In addition, the estimation of calcification score from heart extraction has not been done in previous studies. Therefore, we performed calcium score classification from cardiac CT using 3D-ResNet, which is a 3D extension of ResNet, which is highly accurate in image classification. Therefore, in this study, we propose a cardiac detection method using deep learning to improve the accuracy of calcium scoring. We conducted experiments using a dataset for cardiac detection from chest CT images and a dataset for calcium scoring from chest CT scans. The results show that the proposed method improves the accuracy of calcium scoring compared to the method without cardiac extraction. As a result, we proposed a state-of-the-art highly accurate heart detection method that has never existed before.

This paper proposes a lossless image processing method for color images that simultaneously enhances brightness contrast and saturation. Most of the previous studies focus on lossless contrast enhancement (CE) for grayscale images, where the ideas of reversible data hiding (RDH) are introduced. Meanwhile, a lossless CE method for color images has been proposed. This method, however, never ensures perfect reversibility, while the quality of the restored images is adequately high. In contrast, our method can fully restore the original images. It enhances not only brightness contrast but also saturation. Additionally, the enhancement level is variable, and thus, we can flexibly adjust the visual effect. The experimental results demonstrate that our method controls both brightness contrast and saturation and guarantees reversibility.

Over the last decade, interpersonal communication has attracted more attention from researchers than before. Although the volume of data generated through various communication devices and tools could be enormous, the recent decrease in storage cost enables us to record and store it. The analysis of interpersonal communication is useful to estimate influence in social relationships among people, to detect communities, and to recommend potential friends for users on social networking services. A network graph, which is a mathematical model that represents people as nodes and past opportunities of interpersonal communication as edges, works in such analysis. However, when the capacity of the number of edges recordable in a graph database is limited, or when only a limited number of edges is used for high-speed analysis, it is still unclear which edges should be prioritized and utilized in the analysis. Previous studies suggested that edges in network graphs can be weighted on the basis of the aggregated duration of connections, the number of connections, or the connection time . However, temporal regularity in interpersonal communication has not been well considered in the previous studies. Therefore, in this paper, we propose an edge weighting method for network graphs from interpersonal communication that determines edge weighs on the basis of the scores obtained from the spectral analysis technique. The spectral analysis technique is utilized to numerically deal with temporal regularity and frequency of interpersonal communication. An examination using real records verifies that by using our edge weighting method, link prediction works better under a condition of the limited number of edges usable for the analysis. We also deeply analyze and present the distributions of the frequencies that characterize interpersonal communication.