Abstract
A novel 3D reconstruction and fast imaging system for subcutaneous veins by augmented reality is presented. The study was performed to reduce the failure rate and time required in intravenous injection by providing augmented vein structures that back-project superimposed veins on the skin surface of the hand. Images of the subcutaneous vein are captured by two industrial cameras with extra reflective near-infrared lights. The veins are then segmented by a multiple-feature clustering method. Vein structures captured by the two cameras are matched and reconstructed based on the epipolar constraint and homographic property. The skin surface is reconstructed by active structured light with spatial encoding values and fusion displayed with the reconstructed vein. The vein and skin surface are both reconstructed in the 3D space. Results show that the structures can be precisely back-projected to the back of the hand for further augmented display and visualization. The overall system performance is evaluated in terms of vein segmentation, accuracy of vein matching, feature points distance error, duration times, accuracy of skin reconstruction, and augmented display. All experiments are validated with sets of real vein data. The imaging and augmented system produces good imaging and augmented reality results with high speed.
Highlights
Intravenous injection is a basic and rapid medical method in bedside care
The following methods are proposed for image processing: 1) a vein segmentation method by combining multi-scale information and structural information; 2) 3D reconstruction of subcutaneous veins according to epipolar constraints and hierarchical optimization, and back-projection on the skin surface for augmented reality; and 3) skin surface reconstruction using active structured light with spatial encoding values, and fusion displayed with the reconstructed vein with indicative vein depth
We have presented a novel 3D reconstruction and fast imaging system for subcutaneous veins by augmented reality
Summary
The only handheld, non-contact vein illumination device, http://medidyne.dk/wpcontent/uploads/ENG-accuvein-data-sheet.pdf. “3D and multispectral imaging for subcutaneous veins detection,” Opt. Express 17(14), 11360–11365 (2009). “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. Dermatas, “Supervised and unsupervised finger vein segmentation in infrared images using KNN and NNCA clustering algorithms,” XII Mediterranean Conference on Medical and Biological Engineering and Computing (2010), pp. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Meriaudeau, J.R. Price, and K.W. Tobin, “Simulation of skin reflectance images using 3D tissue modeling and multispectral Monte Carlo light propagation,” 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2008), 447–450.
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