The development of a tele-monitoring system for physiological parameters based on the B/Smodel

Shuicai, Wu; Peijie, Jiang; Yang, Ce; Li, Haomin; Bai, Yanping

doi:10.1016/j.compbiomed.2010.09.009

Cited by 10 publications

(1 citation statement)

References 2 publications

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“…As a new generation information technology, Internet of Things (IoT) brings telemedicine a new chance, which applies sensors and network to traditional medical devices, and therefore is able to assign the intelligence to them and implement deeper communication and interaction between patients and remote specialists [7][8][9][10]. Besides patients' benefit, IoT even helps entire health industry, in which wide scope of medical devices are connected to existing health network, patient crucial life signal is captured by sensors and transmitted to remote medical center, and the doctor is able to remotely monitor patient condition and provide medical suggestion and aiding [11,12].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT

Chae

Moon

Lee

et al. 2014

Journal of Applied Mathematics

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Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.

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