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ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 1. Vol. 30. 2024

DOI: 10.17587/it.30.42-49

I. V. Shcherban, Dr. Sc., Professor, V. S. Fedotova, Junior Researcher, N. E. Kirilenko, Junior Researcher,
A. E. Matukhno, Cand. Sc., Senior Researcher, O. G. Shcherban, Cand. Sc., Assistant Professor, L. V. Lysenko, Cand. Sc., Leading Researcher,
Southern Federal University, Rostov-on-Don, Russian Federation

Method of Localization of Spatiotemporal Patterns on the Time Sequence of Biomedical Images

A method has been developed for localizing spatiotemporal patterns observed in sequentially recorded biomedical images of laser scanning microscopy and reflecting the dynamics of the biological structures under study. By means of interpolation by radial basis functions of each individual image, a compact mathematical model of the space-time dynamics of the brightness function on a sequence of images is obtained. The subsequent localization of the structures of the sought-for dynamic patterns is carried out by means of the mathematical apparatus of singular spectral analysis. The results of experiments on optical visualization of the activity patterns of the olfactory bulb of a macrosmatic (rat) confirmed the efficiency of the developed method for localizing the reaction to biomarkers of human oncological diseases.
Keywords: biomedical imaging, laser scanning microscopy, dynamic pat-terns, neural network, gaussian radial basis function, singular spectral analysis

P. 42-49

Acknowlegements: This work was supported by the Russian Science Foundation, project น 22-25-00683 (2022—2023).

References

  1. Fedorov A. A., Kuznetsova M. A. Scanning electron microscopy in medicine, biology, microbiology, Analyt'cs, 2018, vol. 8, no. 3, pp. 254—257.
  2. Kravchuk D. A., Voronina K. A. Studies of red blood cell aggregation and blood oxygenation on the basis of the optoacoustic effect in biological media, Journal of Biomedical Photonics & Eng'neer'ng, 2020, vol. 6, no. 1, pp. 10307.
  3. Kravchuk D. A. Results of experimental studies of opto-acoustic response in biological tis-sues and their models, Applied Physics, 2022, no. 3, pp. 63—66.
  4. Pawley J. (ed.). Handbook of biological confocal microscopy, Springer Science & Business Media, 2006, vol. 236.
  5. Jezierski T. et al. Study of the art: canine olfaction used for cancer detection on the basis of breath odour. Perspectives and limitations, Journal of breath research, 2015, vol. 9, no. 2, pp. 027001.
  6. Rodionova E. I. et al. Detection of volatile organic compounds associated with hepatocellular carcinoma by macrosmatic animals: Approaches to the search for new tumor markers, Biology Bulletin, 2015, vol. 42, pp. 239—245.
  7. Lippi G., Heaney L. M. The "olfactory fingerprint": can diagnostics be improved by combining canine and digital noses?, Clinical Chemistry and Laboratory Medicine, 2020, vol. 58, no. 6, pp. 958—967.
  8. Feil C. et al. Sniffer dogs can identify lung cancer patients from breath and urine samples, BMC cancer, 2021, vol. 21, no. 1, pp. 1—12.
  9. Shcherban I. V. et al. Method of automatic search for odor-induced patterns in bioelectric activity of a rat olfactory bulb, Information and Control Systems, 2020, vol. 5, pp. 62—69.
  10. Shor E. et al. Sensitive and robust chemical detection using an olfactory brain-computer interface, Biosensors and Bioelectronics, 2022, vol. 195, pp. 113664.
  11. Zhu P. et al. In Vivo Bioelectronic Nose Based on a Bioengineered Rat Realizes the Detection and Classification of Multiodorants, ACS Chemical Neuroscience, 2022, vol. 13, no. 12, pp. 1727—1737.
  12. Kiroiy V. N. et al. Biohybrid technology for detecting ultra-low concentrations of trinitrotoluene in air, Journal of Anaญlytical Chemistry, 2023, vol. 78, no 8. pp. 1—9 (in Russian)
  13. Barkai E., Wilson D. A. Odor memory and perception. Elsevier, Oxford, 2014.
  14. Oka Y. et al. Odorant receptor map in the mouse olfactory bulb: in vivo sensitivity and specificity of receptor-defined glomeruli, Neuron, 2006, vol. 52, no. 5, pp. 857—869.
  15. Burton S. D. et al. Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb, Elife., 2022, vol. 11, pp. e80470.
  16. Johnson B. A., Leon M. Odorant molecular length: one aspect of the olfactory code, Journal of Comparative Neurology, 2000, vol. 426, no. 2, pp. 330—338.
  17. Johnson B. A., Leon M. Chemotopic odorant coding in a mammalian olfactory system, Journal of Comparative Neurology, 2007, vol. 503, no. 1, pp. 1—34.
  18. Homma R. et al. In vivo functional properties of juxta glomerular neurons in the mouse olfactory bulb, Front. Neural Circuits, 2013, vol. 7, pp. 23.
  19. Yuan Q. et al. Optical imaging of odor preference memory in the rat olfactory bulb, Journal of Neurophysiology, 2002, pp. 87, no. 6, pp. 3156—3159.
  20. Wu H. et al. Multi-Level Object-Aware Guidance Network for Biomedical Image Segmentation, IEEE Transactions on Automation Science and Engineering, 2023, pp. 1—14.
  21. Volkov V. Yu., Markelov O. A., Bogachev M. I. Image segmentation and selection of objects based on multipore processing, News of Russian universities. Radio electronics, 2019, no. 3, pp. 24—34 (in Russian)
  22. Yeung M. et al. Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation, arXiv preprint arXiv:2111.00533. 2021.
  23. Tuan T. A., Dung C. T., Khanh T. V. Biomedical Image Segmentation and Analysis in Deep Learning, TTU Review, 2021, vol. 2, no. 1, pp. 19—23.
  24. Spors H., Grinvald A. Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb, Neuron, 2002, vol. 34, no. 2, pp. 301—315.
  25. Spors H. et al. Temporal dynamics and latency patterns of receptor neuron input to the olfactory bulb, Journal of Neuroscience, 2006, vol. 26, no. 4, pp. 1247—1259.
  26. Fantana A. L., Soucy E. R., Meister M. Rat olfactory bulb mitral cells receive sparse glomerular inputs, Neuron, 2008, vol. 59, no. 5, pp. 802—814.
  27. Chae H. et al. Mosaic representations of odors in the input and output layers of the mouse olfactory bulb, Nature neuroscience, 2019, vol. 22, no. 8, pp. 1306—1317.
  28. Franke R. Scattered data interpolation: tests of some methods, Mathematics of computation, 1982, vol. 38, no. 157, pp. 181—200.
  29. Wu Y. et al. Using radial basis function networks for function approximation and classification, International Scholarly Research Notices, 2012, vol. 2012.
  30. Du Toit W. Radial basis function interpolation, Diss. Stellenbosch, Stellenbosch University, 2008.
  31. Hu H. et al. An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography, PeerJ, 2017, vol. 5, pp. e3474.
  32. De Sanctis S. et al. Singular spectrum analysis for an automated solvent artifact removal and baseline correction of 1D NMR spectra, Journal of Magnetic Resonance, 2011, vol. 210, no. 2, pp. 177—183.
  33. Ghaderi F., Mohseni H. R., Sanei S. Localizing heart sounds in respiratory signals using singular spectrum analysis, IEEE Transactions on Biomedical Engineering, 2011, vol. 58, no. 12, p. 3360—3367.
  34. Maddirala A. K., Shaik R. A. Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis, Biomedical Signal Processing and Control, 2016, vol. 30, pp. 79—85.
  35. Scherban O. G., Scherban I. V., Lobzenko P. V. A method for searching for high-frequency patterns in noisy multidimensional signals in real time, Information technology, 2020, vol. 26, no. 9, pp. 507—514 (in Russian).
  36. Golyandina N., Nekrutkin V., Zhigljavsky A. A. Analysis of time series structure: SSA and related techniques, CRC press, 2001.
  37. Danilov D. L., ZHiglyavskij A. A. Principal components of time series structure: SSA. St. Petersburg, Presskom Publ., 1997, 308 p. (in Russian).
  38. Arefev R. A. et al. Methods for calculating the stereotaxic coordinates of rat brain structures by pixel coordinates of the image obtained by confocal and two-photon laser scanning microsญcopy, Journal of Neuroscience Methods, 2021, vol. 361, pp. 109273.


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