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This paper reports on the clinical application of a system for recovering the time-varying three-dimensional (3-D) left-ventricular (LV) shape from multiview X-ray cineangiocardiograms. Considering that X-ray cineangiocardiography is still commonly employed in clinical cardiology and computational costs for 3-D recovery and visualization are rapidly decreasing, it is meaningful to develop a clinically applicable system for 3-D LV shape recovery from X-ray cineangiocardiograms. The system is based on a previously reported closed-surface method of shape recovery from two-dimensional occluding contours with multiple views. To apply the method to "real" LV cineangiocardiograms, user-interactive systems were implemented for preprocessing, including detection of LV contours, calibration of the imaging geometry, and setting of the LV model coordinate system. The results for three real LV angiographic image sequences are presented, two with fixed multiple views (using supplementary angiography) and one with rotating views. 3-D reconstructions utilizing different numbers of views were compared and evaluated in terms of contours manually traced by an experienced radiologist. The performance of the preprocesses was also evaluated, and the effects of variations in user-specified parameters on the final 3-D reconstruction results were shown to be sufficiently small. These experimental results demonstrate the potential usefulness of combining multiple views for 3-D recovery from "real" LV cineangiocardiograms.

Recently, recommendation systems and user reviews have attracted attention as aids in searching for desired products by using web information on the products. However, existing recommendation systems require analyzing a user's activity on the web and collecting information from many reviews. Therefore, in this study, we aimed to develop a product recommendation system that does not have these requirements. We used digital cameras and earphones as case studies and, on the basis of perception-of-value information obtained from questionnaires to users, we applied an analytic hierarchy process (AHP) to an analysis and calculated the degree-of-importance values of the evaluation items for each product. We then calculated the product evaluation values of each evaluation item by using user evaluations of products acquired from Kakaku.com Application Program Interface (API). We developed a system that decides the recommendation order of each product by multiplying the degree-of-importance values and evaluation values together by Fuzzy AHP.