TY - GEN
T1 - A vein image enhancement algorithm for the multi-spectral illumination
AU - Wu, Zhaoguo
AU - Zhou, Ya
AU - Hu, Xiaoming
AU - Zhou, Muqing
AU - Dai, Xiaobin
AU - Li, Xinzhou
AU - Wang, Danting
PY - 2013
Y1 - 2013
N2 - Subcutaneous vein is invisible to naked eyes, but can be easily identified under NIR (near-infrared) light, which is widely used in the catheter insertion and biometric identification. However, the quality of raw NIR image usually suffers from low contrast due to uneven illumination and individual difference. Most of current methods used to enhance the contrast between veins and surrounding tissue are based on single wavelength NIR LED. In the meaning the difference of individual NIR absorption is ignored, which results in diverse performance among different individuals. In this work, a training-based contrast enhancement algorithm is applied to hand vein images. NiBlack segmentation method is also used in the NIR system in order to get a high contrast binary image. An enhanced NIR image is generated from processing six images acquired under six mono-wavelength of light (730nm, 830nm, 850nm, 880nm, 890nm and 940nm). The experiment shows that the contrast of the enhanced NIR image increased by 4∼10 times.
AB - Subcutaneous vein is invisible to naked eyes, but can be easily identified under NIR (near-infrared) light, which is widely used in the catheter insertion and biometric identification. However, the quality of raw NIR image usually suffers from low contrast due to uneven illumination and individual difference. Most of current methods used to enhance the contrast between veins and surrounding tissue are based on single wavelength NIR LED. In the meaning the difference of individual NIR absorption is ignored, which results in diverse performance among different individuals. In this work, a training-based contrast enhancement algorithm is applied to hand vein images. NiBlack segmentation method is also used in the NIR system in order to get a high contrast binary image. An enhanced NIR image is generated from processing six images acquired under six mono-wavelength of light (730nm, 830nm, 850nm, 880nm, 890nm and 940nm). The experiment shows that the contrast of the enhanced NIR image increased by 4∼10 times.
KW - NIR
KW - hand vein
KW - image enhancement
KW - multi-spectral
UR - http://www.scopus.com/inward/record.url?scp=84894487602&partnerID=8YFLogxK
U2 - 10.1109/IST.2013.6729716
DO - 10.1109/IST.2013.6729716
M3 - Conference contribution
AN - SCOPUS:84894487602
SN - 9781467357906
T3 - IST 2013 - 2013 IEEE International Conference on Imaging Systems and Techniques, Proceedings
SP - 332
EP - 336
BT - IST 2013 - 2013 IEEE International Conference on Imaging Systems and Techniques, Proceedings
T2 - 2013 IEEE International Conference on Imaging Systems and Techniques, IST 2013
Y2 - 22 October 2013 through 23 October 2013
ER -