Subcutaneous vein detection using near-infrared (NIR) imaging has recently become a subject of study.1–4 Optical absorption of human skin and muscle plummets in the NIR window allowing for light to travel to subcutaneous depths before losing coherence and directionality to diffusion.5,6 Unlike muscle and skin, blood is a strong absorber of NIR radiation7 which contrasts the subcutaneous vessels against skin and muscle in NIR imaging. As a result, automatic subcutaneous vein detection has become possible using NIR imaging with extensive application in catheter insertion at health care facilities.8 It has been shown that multispectral imaging enhances the performance of automatic vein detection as it collects multiple layers of information that are used in image postprocessing software to localize subcutaneous vasculature.4,8 However, optimal performance of multispectral methods depends on their postprocessing algorithms that require optimal parameter selection and are susceptible to artifacts from nonvein background. Additionally, multispectral methods which use postprocessing software merely detect the subcutaneous vasculature map without retaining other anatomical information. In this letter, we apply a background removal and normalization scheme to multispectral vein imaging that circumvents the limitations of other methods and allows for high-contrast subcutaneous vein detection while extracting nonvein subcutaneous physiological and anatomical structures. At visible wavelengths, images of human limbs mainly entail reflectance from skin, whereas at NIR wavelengths, images are comprised of subcutaneously penetrated light in addition to skin reflectance. To extract the subcutaneous components and remove the skin reflectance, referred to as background herein, from NIR images, we can use the visible images as reference. Visible images taken jointly with NIR images using a multispectral imager are used in a normalized subtraction algorithm to have the background removed from NIR images and bring out the subcutaneous structures. The proposed method is applied to multispectral data obtained by imaging naked arms of human subjects with different skin tone and texture. The output of the algorithm is a fused image that primarily contains subcutaneous components. Multispectral imaging in this work is performed using the Spectrocam™ Multispectral Imaging Camera (Ocean Thin Films, Golden, Colorado). Spectrocam is an imaging system based on a high speed rotating filter wheel coupled to an NIR enhanced CCD camera (a Sony ICX285 sensor) through a Carl Zeiss Distagon ZF-IR lens, as shown in Fig. 1(a). In Spectrocam, multiple wavelengths are multiplexed in the time domain, and as a consequence, the spatial resolution remains un-adulterated. The filter wheel can accept up to eight discrete filters. The camera software separates the images from each filter in real time, and presents the eight spectrally resolved images. In this work, five different filters with the custom-designed spectral bands are utilized, as shown in Fig. 1(b). Narrow-band filters at 546 and 570 nm correspond to the de-oxy and oxy forms of hemoglobin, respectively. The 475 nm broadband filter encompasses the strong absorption bands of melanin, beta-carotene, and hemoglobin. The 615 nm filter covers a region of little absorbance by skin pigments. The 850 nm filter covers the NIR band that includes absorption by lipids but excludes absorption by water.