Segmentation-based compression: New frontiers of telemedicine in telecommunication

Different Roles of Telehealth and Telemedicine on Medical Tourism: An Empirical Study from Azerbaijan

With the rapid progress in mobile healthcare and Internet medicine, the impact of telehealth and telemedicine on the satisfaction of patients and their willingness to travel has become a focus of the academic research community. This study analyses the differences between telehealth and telemedicine and their role in medical tourism. We examine how the information quality and communication quality of telehealth and telemedicine influence patient satisfaction, and their effects on patients' willingness to undertake medical travel and on their medical travel behaviours. We conducted an empirical study on the use of telehealth and telemedicine and on medical travel behaviour in Azerbaijan using a survey for data collection. A total of 500 results were collected and analysed using SmartPLS 3.0. Results show that (1) the communication quality and information quality of telehealth and telemedicine and their effects on satisfaction have significantly positive influences on willingness to undertake medical travel; (2) the psychological expectations of value and cost (perceived value and perceived cost) have a positive influence on medical travel; and (3) willingness to participate in medical travel positively influences medical travel behaviour. Moreover, results of this study have implications for research on, and the practice of, using telehealth and telemedicine as they relate to medical tourism. This research may help improve knowledge about telehealth and telemedicine and understand the differences between them in detail. This empirical research model may also be useful for researchers from other countries who wish to measure medical travel behaviour.

An Overview of Watershed Algorithm Implementations in Open Source Libraries

Watershed is a widespread technique for image segmentation. Many researchers apply the method implemented in open source libraries without a deep understanding of its characteristics and limitations. In the review, we describe benchmarking outcomes of six open-source marker-controlled watershed implementations for the segmentation of 2D and 3D images. Even though the considered solutions are based on the same algorithm by flooding having O(n)computational complexity, these implementations have significantly different performance. In addition, building of watershed lines grows processing time. High memory consumption is one more bottleneck for dealing with huge volumetric images. Sometimes, the usage of more optimal software is capable of mitigating the issues with the long processing time and insufficient memory space. We assume parallel processing is capable of overcoming the current limitations. However, the development of concurrent approaches for the watershed segmentation remains a challenging problem.

Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.