Hybrid constraint optimization for 3D subcutaneous vein reconstruction by near-infrared images. (September 2018)

• Record Type: Journal Article
• Authors: Wu, Chan; Yang, Jian; Zhu, Jianjun; Cong, Weijian; Ai, Danni; Song, Hong; Liang, Xiaohui; Wang, Yongtian
• Is Part Of: Computer methods and programs in biomedicine. Volume 163(2018)
• Page Start: 123
• Page End: 133
• Publication Date: 2018-09
• Subjects: Near-infrared -- Subcutaneous vein -- Matching -- Stereo reconstruction; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.06.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A hybrid constraint optimization (HCO) bidirectional matching algorithm is established using the homographic constraint and the epipolar constraint. Homographic matrix is calculated using the SURF features as the subcutaneous veins are distributed on a plane approximately. To validate the performance of the proposed matching method, we designed a series of experiments to evaluate the effectiveness of the hybrid constraint optimization method. The experiments were performed on simulated and real datasets. The algorithm is validated in terms of homographic optimization, matching efficiency, and simulated vascular reconstruction error. The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed algorithm. Abstract: Background and objective: The development of biometric identification technology and intelligent medication has enabled researchers to analyze subcutaneous veins from near-infrared images. However, the stereo reconstruction of subcutaneous veins has not been well studied, and the results are difficult to utilize in clinical practice. Methods: We present a hybrid constraint optimization (HCO) matching algorithm for vein reconstruction to solve the matching failure problems caused by the incomplete segmentation of vein structures captured from different views. This algorithm initially introduces the existence of the epipolar and homography constraints in the subcutaneous vein matching. Then, the HCO matching algorithm of the vascular centerline is established by homography point-to-point matching, homography matrix optimization, and vascular section matching. Finally, the 3D subcutaneous vein is reconstructed on the basis of the principle of triangulation and system calibration parameters. Results: To validate the performance of the proposed matching method, we designed a series of experiments to evaluate the effectiveness of the hybrid constraint optimization method. The experiments were performed on simulated and real datasets. 42 real vascular images were analyzed on the basis of different matching strategies. Experimental result shows that the matching accuracy increased significantly with the proposed optimization matching method. To calculate the reconstruction accuracy, we reconstructed seven simulated cardboards and measured 10 vascular distances in each simulated cardboard. The average vascular distance error of each simulated image was within 1.0 mm, and the distance errors of 75% feature points were less than 1.5 mm. Also, we printed a 3D simulated vein model to improve the illustration of this system. The reconstruction error extends from −3.58 mm to 1.94 mm with a standard deviation of 0.68 mm and a mean of 0.07 mm. Conclusions: The algorithm is validated in terms of homography optimization, matching efficiency, and simulated vascular reconstruction error. The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed algorithm.