DOI: 10.17586/1023-5086-2019-86-12-35-42
УДК: 621.397, 681.3
Method of bringing locally varying images into coincidence in video capillaroscopy
Full text «Opticheskii Zhurnal»
Full text on elibrary.ru
Publication in Journal of Optical Technology
Гуров И.П., Волков М.В., Маргарянц Н.Б., Потемкин А.В. Метод совмещения локально изменяющихся изображений в видеокапилляроскопии // Оптический журнал. 2019. Т. 86. № 12. С. 35–42. http://doi.org/10.17586/1023-5086-2019-86-12-35-42
Gurov I.P., Volkov M.V., Margaryants N.B., Potemkin A.V. Method of bringing locally varying images into coincidence in video capillaroscopy [in Russian] // Opticheskii Zhurnal. 2019. V. 86. № 12. P. 35–42. http://doi.org/10.17586/1023-5086-2019-86-12-35-42
I. P. Gurov, M. V. Volkov, N. B. Margaryants, and A. V. Potemkin, "Method of bringing locally varying images into coincidence in video capillaroscopy," Journal of Optical Technology. 86(12), 774-780 (2019). https://doi.org/10.1364/JOT.86.000774
This paper presents the results of a study of a method for estimating and compensating mutual displacements of locally varying images of a capillary network when a video sequence is being recorded in order to determine the flow rate of erythrocytes in the capillaries of a nail bed. A method is proposed for bringing images into coincidence in a video sequence, based on the use of a set of reference frames, relative to which the displacement of each image is estimated. It is shown that the proposed method reduces the (subpixel) errors in image alignment, such as local image deformations due to the nature of living biological tissue. Experimental estimates are obtained for the image-alignment error, which equalled 0.11 pixel when 30 reference frames were used. The proposed method has the advantages that it is highly noise resistant and has high computational efficiency in solving problems of video capillaroscopy.
capillar, flow rate of erythrocytes, bringing images into coincidence, video capillaroscopy
Acknowledgements:The research was supported by the Ministry of Education and Science of the Russian Federation (project No. 8.2501.2017/4.6).
OCIS codes: 100.2960, 100.3010, 170.3880
References:1. J. Allen and K. Howell, “Microvascular imaging: techniques and opportunities for clinical physiological measurements,” Physiol. Meas. 35, R91–R141 (2014).
2. S. M. Daly and M. J. Leahy, “‘Go with the flow’: a review of methods and advancements in blood-flow imaging,” J. Biophotonics 6, 217–255 (2013).
3. M. Cutolo, C. Pizzorni, M. E. Secchi, and A. Sulli, “Capillaroscopy,” Best Pract. Res. Clin. Rheumatol. 22, 1093–1108 (2008).
4. I. Gurov, M. Volkov, N. Margaryants, A. Pimenov, and A. Potemkin, “High-speed video capillaroscopy method for imaging and evaluation of moving red blood cells,” Opt. Lasers Eng. 104, 244–251 (2018).
5. S. Baker and I. Matthews, “Lucas-Kanade 20 years on: a unifying framework,” Int. J. Comput. Vision 56, 221–255 (2004).
6. B. K. Horn, Robot Vision (McGraw-Hill, New York, 1986; Mir, Moscow, 1989), chap. 12.
7. S. Negahdaripour and S. Lee, “Motion recovery from image sequences using only first-order optical flow information,” Int. J. Comput. Vision 9, 163–184 (1992).
8. J. L. Horner and P. D. Gianino, “Phase-only matched filtering,” Appl. Opt. 23, 812–816 (1984).
9. A. S. Potapov, “How mutual geometrical distortions affect the possibility of spatially combining images by the local-correlation method,” J. Opt. Technol. 71(8), 555–560 (2004) [Opt. Zh. 71(8), 74-80 (2004)].
10. M. Wernet, “Symmetric phase only filtering: a new paradigm for DPIV data processing,” Meas. Sci. Technol. 16, 601–618 (2005).
11. K. A. Karimov and M. V. Volkov, “The phase correlation algorithm for stabilization of capillary blood flow video frames,” Proc. SPIE 9528, 952810 (2015).
12. C. I. Wright, C. I. Kroner, and R. Draijer, “Non-invasive methods and stimuli for evaluating the skin’s microcirculation,” J. Pharmacolog. Toxicolog. Methods 54, 1–25 (2006).
13. J. M. Johnson, C. T. Minson, and D. L. Kellogg, “Cutaneous vasodilator and vasoconstrictor mechanisms in temperature regulation,” Compr. Physiol. 4, 3–89 (2014).
14. A. A. Sagaidachnyi, “Reactive hyperemia test: methods of analysis, mechanisms of reaction and prospects,” Reg. Blood Circ. Microcirc. 17, 5–22 (2018).
15. A. Sourice and G. Plantier, “Red blood cell velocity estimation in microvessels using the spatiotemporal autocorrelation,” Meas. Sci. Technol. 16, 2229–2239 (2005).
16. M. Watanabe, M. Matsubara, T. Sanada, H. Kuroda, M. Iribe, and M. Furue, “High-speed video capillaroscopy: nailfold capillary shape analysis and red blood cell velocity measurement,” J. Biomech. Sci. Eng. 2, 81–92 (2007).
17. M. Di Renzo, G. Mancia, G. Parati, A. Pedotti, and A. Zanchetti, eds., Frontiers of Blood Pressure and Heart Rate Analysis (IOS Press, Amsterdam, 1997).
18. M. Bra ˇci ˇc Lotri ˇc and A. Stefanovska, “Synchronization and modulation in the human cardiorespiratory system,” Physica A 283, 451–461 (2000).