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Advanced Transmission Methods Applied in Remote Consultation and Diagnosis Platform

  • Zhuofu Deng
  • Yen-wei Chen
  • Zhiliang Zhu
  • Yinuo Li
  • He Li
  • Yi Wang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)

Abstract

Remote consultation and diagnosis platform has been widespread in market for group diagnosis, education and etc. It provides convenience for people enjoying superior quality of medical service. However, most of platforms cannot meet increasing demands in practice. The most important reason is that they have troubles of navigating in complicated Internet condition. In general, remote rendering is preferred to support data transmission, by which server renders images locally, captures screenshot and delivers them to clients synchronously. As a result of neighborhood DICOM slices serving a high similarity, following frame is compressed according to the difference with last one. Even though necessary demand of bandwidth has been reduced a lot, remaining volume proves too large especially remote 3D volume rendering. In this paper, we proposed a novel method bit difference compression transmission. Compared with traditional algorithm, it redesigns a new data structure, which gives more significances to bit. Consequently, demands of network fall into a desirable amount. Specifically, we improved solution of 3D images sharing and collaboration. Instead of terrible screenshots, synchronization of cameras in clients has been introduced. With a minimal cost our platform realized the cumbersome process. Collected data from experiments demonstrated our proposed methods have an obvious superiority and run robust in different environment of Internet.

Notes

Acknowledgments

This research was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 16H01436, in part by the MEXT Support Program for the Strategic Research Foundation at Private Universities (2013–2017).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Zhuofu Deng
    • 1
    • 2
  • Yen-wei Chen
    • 2
  • Zhiliang Zhu
    • 1
  • Yinuo Li
    • 1
  • He Li
    • 2
  • Yi Wang
    • 2
  1. 1.College of Software EngineeringNortheastern UniversityShenyangChina
  2. 2.College of Information Science and EngineeringRitsumeikan UniversityKyotoJapan

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