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Protection of Other Multimedia Objects Using Image-Based Security Techniques
Published in Shivendra Shivani, Suneeta Agarwal, Jasjit S. Suri, Handbook of Image-Based Security Techniques, 2018
Shivendra Shivani, Suneeta Agarwal, Jasjit S. Suri
Biometric and telemedicine are the two latest areas in which we can apply image-based security approaches to enhance security features and efficiency. We know that nowadays, a great deal of authentication is carried out only by biometric data either by fingerprint or face or iris, etc. Therefore we can understand the importance of this biometric data. This data is responsible for proving our identity in many places. We can protect it by digital watermarking techniques. Telemedicine provides health care access to rural and remote locations by enabling practitioners to evaluate, diagnose and treat patients remotely using the latest telecommunications technology. It facilitates patients receiving expert medical suggestions without having to travel. Rural health care practitioners can use telemedicine products to capture and transmit medical data and images. So in the case of telemedicine, we see that a secret medical image is being transmitted from patient to medical expert and this requires security. Hence here we can secure the transmission of medical images using a multitone visual cryptography method. Here we stipulate multitone VC because in cases of medical data, it is very difficult to explain the smallest details of a medical image using only two intensities (halftone VC). Securing medical data during the transmission is very important because even a little modification in a medical images may lead to wrong diagnosis.
Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective
Published in International Journal of Production Research, 2019
Jinxin Pan, Shuai Ding, Desheng Wu, Shanlin Yang, Jun Yang
The health care systems have undergone several evolutions before the concept of smart healthcare emerged (Raskovic et al. 2008). Conventional health care systems are designed to react on illness and are optimised to manage illness. Communication and information technologies have facilitated the delivery of medical services at a distance, which is known as telemedicine (Qiao and Koutsakis 2011). Telemedicine, ranging from teleconferencing to rural health monitoring, has extended the reach of medical services from high-quality medical centres to understaffed remote villages. The introduction of electronic medical records has turned the health care systems into a new paradigm, namely eHealth (Plamondon et al. 2018). Then, developments in sensors and wearable devices create opportunities to provide clinicians and users with tools and environments to gather physiological data over extended periods of time (Garawi, Istepanian, and Abu-Rgheff 2006; Miao et al. 2017). This emerging concept is known as mHealth. It represents the evolution of eHealth systems from traditional desktop telemedicine platforms to wireless and mobile platform. Recent advances in computational and storage capacity, dramatic increase in the wireless bandwidth, and advances in AI and big data analysis have made it possible to turn existing healthcare systems (e.g. m-health) into a new and ubiquitous concept called smart healthcare (Pramanik et al. 2017). Istepanian and Al-Anzi (2018) also defined smart healthcare as mHealth 2.0, and they believe that smart healthcare is an extension of mHealth based on AI and machine learning in the era of big data.