Developing advanced healthcare applications to cater to the requirements of an ever-growing population has become one of the key areas of research in engineering. One major application in this area is medical video streaming, which is often used for remote monitoring of patients. Medical video streaming helps to overcome geographical barriers and offers medical services at the convenience of the patient. However, as medical videos carry critical and time-sensitive information, retaining the quality and reducing latency during transmission is paramount for accurate medical diagnosis. This paper presents the concept of effective medical video streaming, which incorporates novel methods in video pre-processing, video compression, and transmission of medical data over optical networks.

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Automated human chromosome segmentation and feature extraction aim to improve the overall quality of genetic disorder diagnosis by addressing the limitations of tedious manual processes such as expertise dependence, time-inefficiency, observer variability and fatigue errors. Nevertheless, significant differences caused by staining methods, chromosome damage which may occur during imaging, cell and staining debris, inhomogeneity, weak boundaries, morphological variations, premature sister chromatid separation, as well as the presence of overlapping, touching, di-centric and bent chromosomes pose challenges in automated human chromosome segmentation and feature extraction. This review paper extensively discusses how the approaches presented in literature have addressed these challenges, and their strengths and limitations. Human chromosome segmentation algorithms are presented under four broad categories; thresholding, clustering, active contours and convex-concave points-based methods. Chromosome feature extraction methods are discussed under two main categories based on banding-pattern and geometry. In addition, new insights for the improvement of fully automated karyotyping are provided.