(46-6) 07 * << * >> * Russian * English * Content * All Issues

Spectral lenses to highlight blood vessels in the skin
M.M. Hamza 1, V.A. Blank 1,2, V.V. Podlipnov 1,2, L.L. Doskolovich 1,2, R.V. Skidanov 1,2, B. Fan 3

1 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151;
2 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34;
3 Institute of Optics and Electronics, Chinese Academy of Science, Chengdu 610209, China

 PDF, 1443 kB

DOI: 10.18287/2412-6179-CO-1155

Pages: 899-904.

Full text of article: Russian language.

Abstract:
A device for visualization of blood vessels in the human skin is presented. A diffractive optical element is used to locate blood vessels in the skin image. It has been shown that visualization of blood vessels is most effective in the form of obtaining an index image at wavelengths of 735 nm and 835 nm. The index is calculated using a formula similar to the NDVI formula. The work also uses an application software that is used to solve the problems of spectral analysis.

Keywords:
hyperspectrometer, hypercube, spectral analysis, blood vessels on the skin, spectral diffractive lens.

Citation:
Hamza MM, Blank VA, Podlipnov VV, Doskolovich LL, Skidanov RV, Fan B. Spectral lenses to highlight blood vessels in the skin. Computer Optics 2022; 46(6): 899-904. DOI: 10.18287/2412-6179-CO-1155.

Acknowledgements:
This work was supported by the Russian Science Foundation under RSF grant No. 20-69-47110.

References:
  1. Frey A. Success rates for peripheral i.v. insertion in a children's hospital. Financial implications. J Infus Nurs 1998; 21(3): 160-165.
  2. Reigart JR, Chamberlain KH, Eldridge D, O’Brien ES, Freeland KD, Larsen P, Hartzog TH. Peripheral intravenous access in pediatric inpatients. Clin Pediatr 2012; 51(5): 468-472. DOI: 10.1177/0009922811435164.
  3. Munshey F, Parra DA, McDonnell C, Matava C. Ultrasound guided techniques for peripheral intravenous placement in children with difficult venous access. Paediatr Anaesth 2020; 30(2): 108-115. DOI: 10.1111/pan.13780.
  4. Atalay H, Erbay H, Tomatir E, Serin S, Oner O. The use of transillumination for peripheral venous access in paediatric anaesthesia. Eur J Anaesthesiol 2005: 22(4): 317-318. DOI: 10.1017/s026502150524053x.
  5. Bachir W, Abo Dargham F. Feasibility of 830 nm laser imaging for vein localization in dark skin tissue-mimicking phantoms. Phys Eng Sci Med 2022: 45(1): 135-142. DOI: 10.1007/s13246-021-01096-x.
  6. Lin X, Zhuang B, Su X, Zhou Y. Measurement and matching of human vein pattern characteristics. Journal of Tsinghua University 2003: 43(2): 164-167.
  7. Zhang J-Y, Sun M-H. Study on algorithm for skeleton features extraction of hand vein image. J Comput Appl 2007: 27(1): 152-154.
  8. Wang K, Zhang Y, Yuan Z, Zhuang D. Hand vein recognition based on multi supplemental features of multi-classifier fusion decision. 2006 International Conference on Mechatronics and Automation 2006: 1790-1795. DOI: 10.1109/ICMA.2006.257486.
  9. Li W, Yuan W. Imaging quality analysis on palm vein under different wavelengths near-IR. Computer Engineering and Applications 2011; 47(30): 15-18.
  10. Zharov V, Ferguson S, Eidt J, Howard P, Fink L, Waner M. Infrared imaging of subcutaneous veins. Lasers Surg Med 2004; 34(1): 56-61. DOI: 10.1002/lsm.10248.
  11. Madrid García A, Horche PR. Light source optimizing in a biphotonic vein finder device: Experimental and theoretical analysis. Results Phys 2018; 11: 975-983. DOI: 10.1016/j.rinp.2018.10.033.
  12. Pan C-T, Francisco MD, Yen C-K, Wang S-Y, Shiue Y-L. Vein pattern locating technology for cannulation: a review of the low-cost vein finder prototypes utilizing near infrared (NIR) light to improve peripheral subcutaneous vein selection for phlebotomy. Sensors 2019; 19(16): 3573. DOI: 10.3390/s19163573.
  13. Ayoub Y, Serhal S, Farhat B, Ali A, Amatoury A, Nasser H, Ali MA. Diagnostic superficial vein scanner. 2018 Int Conf on Computer and Applications (ICCA) 2018: 321-325. DOI: 10.1109/COMAPP.2018.8460229.
  14. Uhl A, ed. Handbook of vascular biometrics. Springer; 2020.
  15. 8 best devices for finding veins. Source: <https://evercare.ru/news/8-luchshikh-ustroystv-dlya-poiska-ven>.
  16. Spectral Indices. Source: <http://www.exelisvis.com/docs/SpectralIndices.html>.
  17. Soifer VA, ed. Methods for computer design of diffractive optical elements. New York: John Willey & Sons Inc; 2002. ISBN: 978-0-471-09533-0.
  18. Skidanov RV, Doskolovich LL, Ganchevskaya SV, Blank VA, Podlipnov VV, Kazanskiy NL. Experiment with a diffractive lens with a fixed focus position at several given wavelengths. Computer Optics 2020; 44(1): 22-28. DOI: 10.18287/2412-6179-CO-646.
  19. Blank V, Skidanov R, Doskolovich L, Kazanskiy N. Spectral diffractive lenses for measuring a modified red edge simple ratio index and a water band index. Sensors 2021; 21(22): 7694. DOI: 10.3390/s21227694.
  20. Firsov NA, Podlipnov VV, Ivliev NA, Nikolaev PP, Mashkov SV, Ishkin PA, Skidanov RV, Nikonorov AV. Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index. Computer Optics 2021; 45(6): 887-896. DOI: 10.18287/2412-6179-CO-1038.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20