Parametric Variations of Anisotropic Diffusion and Gaussian High-Pass Filter for NIR Image Preprocessing in Vein Identification

  • Ayca Kirimtat
  • Ondrej KrejcarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


Near infrared (NIR) imaging is one of the promising methods for identification of superficial veins and widely researched and used in clinical medicine and biomedical studies. However, just like imaging in visible spectrum, NIR imaging is not adequate for exact recognition of the vein system as it is, therefore nearly every research starts with preprocessing to prepare the images for identification. Two major filtering methods are anisotropic diffusion and Gaussian high-pass filter which both consist of mandatory parametric adjustments for better visualization of the images and for revealing the vein system. Therefore in this paper we deal with parametric variations of these two methods on a NIR image to give ideas for choosing proper preprocessing techniques and parameters, excluding edge detection and vein detection methodologies.


Near infrared Vein recognition Preprocessing 



The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2018).


  1. 1.
    Fredembach, C.: Near-infrared imaging. Accessed 14 Jan 2018
  2. 2.
    Cope, M., Delpy, D.T.: System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination. Med. Biol. Eng. Comput. 26(3), 289–294 (1988)CrossRefGoogle Scholar
  3. 3.
    Xueyan, L., Shuxu, G.: The fourth biometric - vein recognition. In: Yin, P.-Y. (ed.) Pattern Recognition Techniques, Technology and Applications (2008)Google Scholar
  4. 4.
    Bouzida, N., Bendada, A.H., Maldague, X.P.: Near-infrared image formation and processing for the extraction of hand veins. J. Mod. Opt. 57(18), 1731–1737 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Wang, L., Leedham, G., Cho, S.Y.: Infrared imaging of hand vein patterns for biometric purposes. IET Comput. Vis. 1(3), 113–122 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Van Tien, T., Mien, P.T.H., Dung, P.T., Linh, H.Q.: Using near-infrared technique for vein imaging. In: Toi, V.V., Lien Phuong, T.H. (eds.) 5th International Conference on Biomedical Engineering in Vietnam. IP, vol. 46, pp. 190–193. Springer, Cham (2015). Scholar
  7. 7.
    Paquit, V., Price, J.R., Seulin, R., Mériaudeau, F., Farahi, R.H., Tobin, K.W., Ferrell, T.L.: Near-infrared imaging and structured light ranging for automatic catheter insertion. In: Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display (2006)Google Scholar
  8. 8.
    Alpar, O., Krejcar, O.: Superficial dorsal hand vein estimation. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 408–418. Springer, Cham (2017). Scholar
  9. 9.
    Alpar, O., Krejcar, O.: Quantization and equalization of pseudocolor images in hand thermography. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 397–407. Springer, Cham (2017). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Informatics and Management, Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec KraloveCzech Republic

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