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Elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain

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Information Technologies and Intelligent Decision Making Systems (ITIDMS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2112))

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Abstract

An algorithm is proposed for eliminating refractive distortions caused by the oscillating surface of a liquid when studying the brain of a live mouse. Studies like this in mice allow monitoring neuronal activity in a living organism, and the fluid is needed to ensure that the brain remains in its natural environment. However, the presence of fluid flow causes distortions that significantly complicate tracking waves of neuronal activity. The goal of the present work is to remove effects that displace and distort images of individual parts of the brain, and, in fact, bring the entire image to a static picture. The proposed algorithm, based on tracking individual parts of images, gives a 10% improvement in approximation to a static picture compared to the original recording.

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References

  1. Shumkova, V., Sitdikova, V., Rechapov, I., Leukhin, A., Minlebaev, M.: Effects of urethane and isoflurane on the sensory evoked response and local blood flow in the early postnatal rat somatosensory cortex. Sci. Rep. 11, 9567 (2021)

    Article  Google Scholar 

  2. Tuliabaeva, D., Tumakov, D, Elshin, L.: On the recognition of weakly blurred, highly contrasting objects by neural networks. In: Proceedings SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), p. 1306507 (2024)

    Google Scholar 

  3. Suchkov, D., Shumkova, V., Sitdikova, V., Minlebaev M.: Simple and efficient 3D-printed superfusion chamber for electrophysiological and neuroimaging recordings in vivo. eNeuro 9(5) (2022)

    Google Scholar 

  4. Jung, Y.-H., Kim, G., Yoo, W.S.: Off-site distortion and color compensation of underwater archaeological images photographed in the very turbid yellow sea. J. Cons. Sci. 38(1), 14–32 (2022)

    Article  Google Scholar 

  5. Haitao, L., et al.: 3D point cloud capture method for underwater structures in turbid environment. Meas. Sci. Technol. 32(2), 025106 (2021)

    Article  Google Scholar 

  6. Thapa, S., Li, N., Ye, J.: Learning to remove refractive distortions from underwater images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5007–5016 (2021)

    Google Scholar 

  7. Li, T., Yang, Q., Rong, S., Chen, L., He, B.: Distorted underwater image reconstruction for an autonomous underwater vehicle based on a self-attention generative adversarial network. Appl. Opt. 59(32), 10049–10060 (2020)

    Article  Google Scholar 

  8. Li, N., Thapa, S., Whyte, C., Reed, A. W., Jayasuriya, S., Ye, J.: Unsupervised non-rigid image distortion removal via grid deformation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2522–2532 (2021)

    Google Scholar 

  9. Sun, H., Du, H., Li, M., Sun, H., Zhang, X.: Underwater image matching with efficient refractive-geometry estimation for measurement in glass-flume experiments. Measurement 152, 107391 (2020)

    Article  Google Scholar 

  10. Dutta, A., Mondal, A., Dey, N., et al.: Vision tracking: a survey of the state-of-the-art. SN Comput. Sci. 1(1), 57 (2020)

    Article  Google Scholar 

  11. Yedjour, H.: Optical flow based on Lucas-Kanade method for motion estimation. Artif. Intell. Renew. Towards Energy Transit. 4, 937–945 (2020)

    Google Scholar 

  12. Zhong, L., Meng, L., Hou, W., Huang, L.: An improved visual odometer based on Lucas-Kanade optical flow and ORB feature. IEEE Access 11, 47179–47186 (2023)

    Article  Google Scholar 

  13. Du, K., Bobkov, A.: An overview of object detection and tracking algorithms. Eng. Proc. 33(1), 22 (2023)

    Google Scholar 

  14. Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Kim, T.-K.: Multiple object tracking: a literature review. Artif. Intell. 293, 103448 (2021)

    Article  MathSciNet  Google Scholar 

  15. Eelbode, T., et al.: Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index. IEEE Trans. Med. Imaging 39(11), 3679–3690 (2020)

    Article  Google Scholar 

  16. Nilsson, J., Akenine-Möller, T.: Understanding SSIM. arXiv preprint arXiv 2006.13846 (2020)

    Google Scholar 

  17. Starovoytov, V., Eldarova, E., Iskakov, K.: Comparative analysis of the SSIM index and the pearson coefficient as a criterion for image similarity. Eurasian J. Math. Comput. Appl. 8(1), 76–90 (2020)

    Google Scholar 

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Acknowledgment

This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program.

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Authors and Affiliations

  1. Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kazan, Russia

    Timur Bikbulatov & Dmitrii Tumakov

  2. Laboratory ‘‘New Engineering Solutions for Modern Laboratory Research’’, Kazan Federal University, Kazan, Russia

    Violetta Sitdikova

Authors
  1. Timur Bikbulatov
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  2. Violetta Sitdikova
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  3. Dmitrii Tumakov
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Corresponding author

Correspondence to Dmitrii Tumakov .

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Editors and Affiliations

  1. National Research University "MPEI", Moscow, Russia

    Arthur Gibadullin

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Bikbulatov, T., Sitdikova, V., Tumakov, D. (2024). Elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain. In: Gibadullin, A. (eds) Information Technologies and Intelligent Decision Making Systems. ITIDMS 2023. Communications in Computer and Information Science, vol 2112. Springer, Cham. https://doi.org/10.1007/978-3-031-60318-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-60318-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60317-4

  • Online ISBN: 978-3-031-60318-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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