Thermal Imaging for Localization of Anterior Forearm Subcutaneous Veins
The anterior forearm recognition systems are very popular identify the subcutaneous veins, mostly by devices operating real-time. Real time projection systems are carried out by handheld devices with a near infrared (NIR) camera and a laser projector to choose venipuncture sites; however what we propose in this paper is an easier and more reliable way using infrared thermal (IR-T) camera. At the forearm some veins like Cephalic vein are mostly so concealed to detect by NIR cameras, therefore we propose an alternative method for localization of the whole vein system without preprocessing. Briefly in the thermograms, the forearm is segmented from the surrounding by crisp 2- means and the vein system is reconstructed on blank images by directional curvature method. All directional curvatures are combined by addition for merging the layers and highlighting mutual veins. The preliminary results are promising since all the vein system is revealed with invisible veins without any preprocessing.
KeywordsThermal imaging Forearm veins Directional curvature Crisp 2- means Localization Segmentation
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. We are also grateful for the support of Ph.D. students of our team (Ayca Kirimtat and Pavel Blazek) in consultations regarding application aspects.
- 3.Buddharaju, P., Pavlidis, I.T.: Physiology-based face recognition in the thermal infrared spectrum. In: Medical Infrared Imaging: Principles and Practices, pp. 18.1–18.16. CRC press (2012)Google Scholar
- 5.Huang, D., Zhang, R., Yin, Y., Wang, Y., Wang, Y.: Local feature approach to dorsal hand vein recognition by centroid-based circular key-point grid and fine-grained matching. Image Vis. Comput. (2016). http://dx.doi.org/10.1016/j.imavis.2016.07.001
- 6.Joardar, S., Chatterjee, A., Bandyopadhyay, S., Maulik, U.: Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm. Eng. Appl. Artif. Intell. 60, 151–163 (2017)CrossRefGoogle Scholar
- 10.Sheetal, R.P., Goela, K.G.: Image processing in hand vein pattern recognition system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(6), 427–430 (2014)Google Scholar
- 11.Janes, R., Júnior, A.F.B.: A low cost system for Dorsal hand vein patterns recognition using curvelets. In: Proceedings of the 2014 First International Conference on Systems Informatics, Modelling and Simulation. IEEE Computer Society, pp. 47–52 (2014)Google Scholar
- 12.Sakthivel, G.: Hand vein detection using infrared light for web based account. Int. J. Comput. Appl. 112(10), 17–21 (2015)Google Scholar
- 16.Morales-Montiel, I.I., Olvera-López, J.A., Martín-Ortíz, M., Orozco-Guillén, E.E.: Hand vein infrared image segmentation for biometric recognition. Res. Comput. Sci. 80, 55–66 (2014)Google Scholar
- 18.Djerouni, A., Hamada, H., Loukil, A., Berrached, N.: Dorsal hand vein image contrast enhancement techniques. Int. J. Comput. Sci. 11(1), 137–142 (2014)Google Scholar
- 22.Wang, J., Wang, G., Li, M., Du, W., Yu, W.: Hand vein images enhancement based on local gray-level Information histogram. Int. J. Bioautom. 19(2), 245–258 (2015)Google Scholar
- 24.Buddharaju, P., Pavlidis, I.T., Tsiamyrtzis, P.: Physiology-based face recognition. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2005, pp. 354–359. IEEE (2005)Google Scholar
- 25.Alpar, O., Krejcar, O.: Dorsal hand recognition through adaptive YCbCr imaging technique. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016 Part II. LNCS (LNAI), vol. 9876, pp. 262–270. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45246-3_25CrossRefGoogle Scholar