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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5107–5135 | Cite as

A novel registration and super-resolution jointed paradigm for medical images under internet of thing environment

  • Yu Liu
  • Jie Yang
  • Jing Mi
  • Jingjing Yang
  • Xiao ZhangEmail author
Article
  • 277 Downloads

Abstract

This paper proposes a novel registration and super-resolution jointed paradigm for medical images under the Internet of thing environment. In the medical image processing, the matching issue is one catches wide attention with the domain of research. Image registration technique can be divided into similarity measure, optimization, geometric transformation, and interpolation, etc. As the first essential clue of our model, we propose the novel registration algorithm based on energy feature extraction. Generally, the matching energy function by the similarity measurement and a penalty constitution is called the external force and endogenic force separately. The matching is an external force and endogenic force mutual competition, eventually achieves the balanced process. Furtherly, we integrate the game analysis and area feature selection to achieve the better image super-resolution mode through the pretreatment of the image to change the initial value, so as to achieve the purpose of improving the performance. Besides the algorithm level innovation, we integrate the GPU and the IOT to construct the hardware based implementation of the proposed medical image processing system. The latency of registers to read and write data across a GPU’s entire storage system is minimal, it is private to each thread, and can only be accessed by its owning thread. For each thread, the local memory is also private and it is often used to deal with the problem of overflow register, reducing the buffer overflow caused by the entire application of a substantial decline in the possibility and shared memory is visible to all threads within the thread block. We then achieve the optimal integration of IOT and GPU. The experimental result proves the robustness of the method.

Keywords

Medical image Image registration Image super-resolution Internet of things Data storage Algorithm efficiency Jointed paradigm 

Notes

Acknowledgements

The authors thank Prof. Xiao Zhang and Prof. Wei Peng for the valuable discussion and recommendation.

This project was supported partially by the Population Health Informatization in Hebei Province Engineering Technology Research Center, Medical Informatics in Hebei Universities Application Technology Research and Development Center, Hebei province department of science and technology project(15217747D), Hebei province department of health project(20160029), Zhangjiakou department of science and technology project (1421012B), and Youth Foundation of the Education Department of Hebei Province (QN2016190).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yu Liu
    • 1
  • Jie Yang
    • 1
  • Jing Mi
    • 1
  • Jingjing Yang
    • 1
  • Xiao Zhang
    • 1
    Email author
  1. 1.Hebei North UniversityZhangjiakouChina

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