Image mosalcmg is a common method in Medical image processing. In this paper, a new method is proposed for image mosaicing based on Speeded-Up Robust Features (SURF), which can be used for X-ray images of spine. First, feature descriptor of the image is obtained by using SURF;secondly, matching pairs are found, check the neighbors, and remove the mismatch couples by RANSAC (Random Sample Consensus); then, adjust the images and estimate the accurate homo-graphy matrix; lastly, stitch image by weighted average method. The experimental results show that the method can be effectively applied for prototype panoramic X-rtU' imaging.
The issue of identity authentication for online medical services has been one of the key focuses of the healthcare industry in recent years. Most healthcare organizations use centralized identity management systems (IDMs), which not only limit the interoperability of patient identities between institutions of healthcare, but also create isolation between data islands. The more important matter is that centralized IDMs may lead to privacy disclosure. Therefore, we propose Health-zkIDM, a decentralized identity authentication system based on zero-knowledge proof and blockchain technology, which allows patients to identify and verify their identities transparently and safely in different health fields and promotes the interaction between IDM providers and patients. The users in Health-zkIDM are uniquely identified by one ID registered. The zero-knowledge proof technology is deployed on the client, which provides the user with a proof of identity information and automatically verifies the user’s identity after registration. We implemented chaincodes on the Fabric, including the upload of proof of identity information, identification, and verification functions. The experiences show that the performance of the Health-zkIDM system can achieve throughputs higher than 400 TPS in Caliper.
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