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Methods of Endoscopic Images Enhancement and Analysis in CDSS

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Computer Vision in Advanced Control Systems-5

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 175))

Abstract

In this chapter, the methods of medical image enhancement and analysis in Clinical Decision Support Systems (CDSS) are discussed. Three general groups of tasks in CDSS development are described: medical images quality improvement, synthesis of images with increased diagnostic value, and medical images automatic analysis for differential diagnostics. For the first group, the review and analysis of noise reduction methods are presented. The new state-of-the art algorithm for virtual chromoendoscopy is proposed as an illustration of the second group. Automatic images analysis concerning to the third group is shown on example of two algorithms: for the polyps’ segmentation and bleeding detection. The algorithm for bleeding detection is based on two-stage strategy that gives the sensitivity and specificity scores of 0.85/0.97 for test set. The segmentation of polyps is based on deep learning technology and shows promising results.

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Authors and Affiliations

  1. Saint Petersburg State Electro Technical University “LETI”, Professora Popova str. 5, Saint-Petersburg, 197022, Russian Federation

    Nataliia Obukhova, Alexandr Motyko & Alexandr Pozdeev

Authors
  1. Nataliia Obukhova
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  2. Alexandr Motyko
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  3. Alexandr Pozdeev
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Corresponding author

Correspondence to Nataliia Obukhova .

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Editors and Affiliations

  1. Department of Informatics and Computer Techniques, Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Obukhova, N., Motyko, A., Pozdeev, A. (2020). Methods of Endoscopic Images Enhancement and Analysis in CDSS. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Advanced Control Systems-5. Intelligent Systems Reference Library, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-33795-7_8

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