A vein image enhancement algorithm for the multi-spectral illumination

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.