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Journal of Medical Systems

, 42:138 | Cite as

An Augmented Reality Endoscope System for Ureter Position Detection

  • Feng Yu
  • Enmin Song
  • Hong LiuEmail author
  • Yunlong Li
  • Jun Zhu
  • Chih-Cheng Hung
Image & Signal Processing
Part of the following topical collections:
  1. Advanced Computational Intelligence and Soft Computing in Medical Imaging

Abstract

Iatrogenic injury of ureter in the clinical operation may cause the serious complication and kidney damage. To avoid such a medical accident, it is necessary to provide the ureter position information to the doctor. For the detection of ureter position, an ureter position detection and display system with the augmented ris proposed to detect the ureter that is covered by human tissue. There are two key issues which should be considered in this new system. One is how to detect the covered ureter that cannot be captured by the electronic endoscope and the other is how to display the ureter position that provides stable and high-quality images. Simultaneously, any delayed processing of the system should disturb the surgery. The aided hardware detection method and target detection algorithms are proposed in this system. To mark the ureter position, a surface-lighting plastic optical fiber (POF) with the encoded light-emitting diode (LED) light is used to indicate the ureter position. The monochrome channel filtering algorithm (MCFA) is proposed to locate the ureter region more precisely. The ureter position is extracted using the proposed automatic region growing algorithm (ARGA) that utilizes the statistical information of the monochrome channel for the selection of growing seed point. In addition, according to the pulse signal of encoded light, the recognition of bright and dark frames based on the aided hardware (BDAH) is proposed to expedite the processing speed. Experimental results demonstrate that the proposed endoscope system can identify 92.04% ureter region in average.

Keywords

Ureter injury Endoscope system Ureter position detection Augmented reality Automatic region growing algorithm (ARGA

Notes

Acknowledgements

This work was supported by National Key R & D Program of China, No. 2017YFC0112804, National Natural Science Foundation of China under grant project No.61370179, the Fundamental Research Funds for the Central Universities, HUST: 2016YXZD018 and HUST: 2017JYCX038, Medical Clinical Science and Technology Development Fund of Jiangsu University, No. JLY20140051C, and Clinical Medicine Science and Technology Projects in Jiangsu province, No. BL2014056.

Compliance with Ethical Standards

Conflict of interests

The authors declare no conflict of interest.

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of UrologyAffiliated Kunsan Hospital of Jiangsu UniversityKunshanChina
  3. 3.Center for Machine Vision and Security ResearchKennesaw State UniversityKennesawUSA

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