Tony F. Chan

The development of ISO 14971 was not carried out in a vacuum but instead was shaped by precedent activities in the European Union (EU) to establish risk management standards quite early in the 1990s. The first step made by the EU was the publication in 1997 of a European Standard, EN (European Norm) 1441 on Risk Analysis for Medical Devices. The framework focused on using a specific set of methods called Failure Mode and Effect Analysis (FMEA), for medical device risk analysis. ISO adopted this European Standard in 1997, when it essentially duplicated its publication under the title “Medical Device—Risk Analysis,” ISO 14971-1. This standard was useful for prioritizing activities but remained incomplete because of its focus on the particular use of a single approach, 192namely FMEA. Thus, a working group was formed to incorporate risk management practices already defined in the IEC 60601 Standard Series, a set of standards focusing on medical electrical equipment, into its new international standard, designated as ISO 14971, in 2000. This standard then replaced the ISO/IEC 14971-1 standard and was recognized as both a European-harmonized standard (GHTF SG3/N15R8:2005 [1]) and an FDA-recognized standard. It emphasized a more complete risk management framework, shown in Fig. 16.1.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

Reconstructing transparent objects is a challenging problem. While producing reasonable results for quite complex objects, existing approaches require custom calibration or somewhat expensive labor to achieve high precision. When an overall shape preserving salient and fine details is sufficient, we show in this paper a significant step toward solving the problem when the object's silhouette is available and simple user interaction is allowed, by using a video of a transparent object shot under varying illumination. Specifically, we estimate the normal map of the exterior surface of a given solid transparent object, from which the surface depth can be integrated. Our technical contribution lies in relating this normal estimation problem to one of graph-cut segmentation. Unlike conventional formulations, however, our graph is dual-layered, since we can see a transparent object's foreground as well as the background behind it. Quantitative and qualitative evaluation are performed to verify the efficacy of this practical solution.