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.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref]

2015 (3)

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

2014 (2)

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

2013 (4)

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

X. Hu, Y. Zhou, and Z. Wu, “A 2.5 dimensional vein imaging system for venipuncture,” Proc. SPIE 8668, 86685A (2013).
[Crossref]

2012 (1)

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

2011 (2)

M.M. Chen, S.X. Guo, and X.H. Qian, “Finger vein image segmentation based on an improved LBF active contour model,” J. Jilin University 41, 1171–1176 (2011).

E. Nakamachi, Y. Morita, and Y. Mizuno, “Development of Automatic 3D Blood Vessel Search and Automatic Blood Sampling System by Using Hybrid Stereo-Autofocus Method,” International J. Opt. 2012, 258626 (2011).

2010 (2)

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

2009 (2)

W. Kang and F. Deng, “Vein Image Segmentation Based on Distribution Ratio of Directional Fields,” Acta Automatica Sinica 35, 1496–1502 (2009).
[Crossref]

V.C. Paquit, K.W. Tobin, J.R. Price, and F. Mriaudeau, “3D and multispectral imaging for subcutaneous veins detection,” Opt. Express 17(14), 11360–11365 (2009).
[Crossref] [PubMed]

2007 (1)

M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med 37, 262–267 (2007).
[Crossref]

2004 (2)

H.D. Zeman, G. Lovhoiden, and C. Vrancken, “Prototype vein contrast enhancer,” Proc. SPIE 5318, 39–49 (2004).
[Crossref]

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl. 15(4), 194–203 (2004).
[Crossref]

2000 (1)

Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Trans. Pattern Anal. 22(11), 1330–1334 (2000).
[Crossref]

1997 (1)

D. Delpy, “Quantification in tissue near-infrared spectroscopy,” Philos Trans R Soc Lond B. 352(1354), 649–659 (1997).
[Crossref]

1994 (1)

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

Ai, D.N.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

Al-Rawi, M.

M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med 37, 262–267 (2007).
[Crossref]

Arrar, M.

M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med 37, 262–267 (2007).
[Crossref]

Bankhead, P.

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

Bay, H.

H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” 9th European Conference on Computer Vision (2006), pp. 404–417.

Bennett, H.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Brewer, R.D.

R.D. Brewer and J.K. Salisbury, “Visual Vein-Finding for Robotic IV Insertion,” IEEE International Conference on Robotics and Automation, (2010), pp. 4597–4602.

Chen, A.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

Chen, D.

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

Chen, K.

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Chen, M.M.

M.M. Chen, S.X. Guo, and X.H. Qian, “Finger vein image segmentation based on an improved LBF active contour model,” J. Jilin University 41, 1171–1176 (2011).

Chen, Y.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

Chiao, F.B.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Chokreff, J.M.

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

Cong, W.J.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

Cuper, N.J.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Curtis, T. M.

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

de Graaff, J.C.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

de Roode, R.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Deepa, P.

P. Deepa, K. Mohanavelu, B.S. Sundersheshu, and V.C. Padaki, “Vein Identification and Localization for Automated Intravenous Drug Delivery System,” Volume 292 of the series Communications in Computer and Information Science, (2012), pp. 270–281.

Delpy, D.

D. Delpy, “Quantification in tissue near-infrared spectroscopy,” Philos Trans R Soc Lond B. 352(1354), 649–659 (1997).
[Crossref]

Deng, F.

W. Kang and F. Deng, “Vein Image Segmentation Based on Distribution Ratio of Directional Fields,” Acta Automatica Sinica 35, 1496–1502 (2009).
[Crossref]

Dermatas, E.

M. Vlachos and E. Dermatas, “Vein segmentation in infrared images using compound enhancing and crisp clustering,” Volume 5008 of the series Lecture Notes in Computer Science, (2008) pp. 393–402.

M. Vlachos and E. 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. 741–744.

Fan, J.F.

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

Friman, O.

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Ganz, A.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Guo, S.X.

M.M. Chen, S.X. Guo, and X.H. Qian, “Finger vein image segmentation based on an improved LBF active contour model,” J. Jilin University 41, 1171–1176 (2011).

Harding, S.P.

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

Hindennach, M.

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Hu, X.

X. Hu, Y. Zhou, and Z. Wu, “A 2.5 dimensional vein imaging system for venipuncture,” Proc. SPIE 8668, 86685A (2013).
[Crossref]

Jaspers, J.E.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Jian, Y.

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

Kang, W.

W. Kang and F. Deng, “Vein Image Segmentation Based on Distribution Ratio of Directional Fields,” Acta Automatica Sinica 35, 1496–1502 (2009).
[Crossref]

Khnel, C.

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Klaessens, J.H.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Lesser, J.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Liu, Y.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

Lovhoiden, G.

H.D. Zeman, G. Lovhoiden, and C. Vrancken, “Prototype vein contrast enhancer,” Proc. SPIE 5318, 39–49 (2004).
[Crossref]

Maguire, T.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

McGeown, J. G.

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

Meriaudeau, F.

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

V.C. Paquit, F. 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.

Miura, N.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl. 15(4), 194–203 (2004).
[Crossref]

Miyatake, T.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl. 15(4), 194–203 (2004).
[Crossref]

Mizuno, Y.

E. Nakamachi, Y. Morita, and Y. Mizuno, “Development of Automatic 3D Blood Vessel Search and Automatic Blood Sampling System by Using Hybrid Stereo-Autofocus Method,” International J. Opt. 2012, 258626 (2011).

Mohanavelu, K.

P. Deepa, K. Mohanavelu, B.S. Sundersheshu, and V.C. Padaki, “Vein Identification and Localization for Automated Intravenous Drug Delivery System,” Volume 292 of the series Communications in Computer and Information Science, (2012), pp. 270–281.

Morita, Y.

E. Nakamachi, Y. Morita, and Y. Mizuno, “Development of Automatic 3D Blood Vessel Search and Automatic Blood Sampling System by Using Hybrid Stereo-Autofocus Method,” International J. Opt. 2012, 258626 (2011).

Mriaudeau, F.

Nagasaka, A.

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl. 15(4), 194–203 (2004).
[Crossref]

Nakamachi, E.

E. Nakamachi, Y. Morita, and Y. Mizuno, “Development of Automatic 3D Blood Vessel Search and Automatic Blood Sampling System by Using Hybrid Stereo-Autofocus Method,” International J. Opt. 2012, 258626 (2011).

Naufal Mohamad Saad, M.

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

Ng, J.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Nikitczuk, J.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

Nikitczuk, K.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

Noordmans, H.J.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Padaki, V.C.

P. Deepa, K. Mohanavelu, B.S. Sundersheshu, and V.C. Padaki, “Vein Identification and Localization for Automated Intravenous Drug Delivery System,” Volume 292 of the series Communications in Computer and Information Science, (2012), pp. 270–281.

Paquit, V.C.

V.C. Paquit, K.W. Tobin, J.R. Price, and F. Mriaudeau, “3D and multispectral imaging for subcutaneous veins detection,” Opt. Express 17(14), 11360–11365 (2009).
[Crossref] [PubMed]

V.C. Paquit, F. 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.

Peitgen, H. O.

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Perkins, C.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Pino-Luey, D.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Price, J.R.

V.C. Paquit, K.W. Tobin, J.R. Price, and F. Mriaudeau, “3D and multispectral imaging for subcutaneous veins detection,” Opt. Express 17(14), 11360–11365 (2009).
[Crossref] [PubMed]

V.C. Paquit, F. 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.

Qian, X.H.

M.M. Chen, S.X. Guo, and X.H. Qian, “Finger vein image segmentation based on an improved LBF active contour model,” J. Jilin University 41, 1171–1176 (2011).

Qutaishat, M.

M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med 37, 262–267 (2007).
[Crossref]

Rada, L.

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Resta-Flarer, F.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Rzeszotarski, M.S.

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

Saeed Malik, A.

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

Salisbury, J.K.

R.D. Brewer and J.K. Salisbury, “Visual Vein-Finding for Robotic IV Insertion,” IEEE International Conference on Robotics and Automation, (2010), pp. 4597–4602.

Scholfield, C. N.

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

Shahzad, A.

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

Shoujun, Z.

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

Singerman, L.J.

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

Sundersheshu, B.S.

P. Deepa, K. Mohanavelu, B.S. Sundersheshu, and V.C. Padaki, “Vein Identification and Localization for Automated Intravenous Drug Delivery System,” Volume 292 of the series Communications in Computer and Information Science, (2012), pp. 270–281.

Tobin, K.W.

V.C. Paquit, K.W. Tobin, J.R. Price, and F. Mriaudeau, “3D and multispectral imaging for subcutaneous veins detection,” Opt. Express 17(14), 11360–11365 (2009).
[Crossref] [PubMed]

V.C. Paquit, F. 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.

Tuytelaars, T.

H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” 9th European Conference on Computer Vision (2006), pp. 404–417.

Van Gool, L.

H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” 9th European Conference on Computer Vision (2006), pp. 404–417.

Verdaasdonk, R.M.

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Vlachos, M.

M. Vlachos and E. Dermatas, “Vein segmentation in infrared images using compound enhancing and crisp clustering,” Volume 5008 of the series Lecture Notes in Computer Science, (2008) pp. 393–402.

M. Vlachos and E. 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. 741–744.

Vrancken, C.

H.D. Zeman, G. Lovhoiden, and C. Vrancken, “Prototype vein contrast enhancer,” Proc. SPIE 5318, 39–49 (2004).
[Crossref]

Walter, N.

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

Wang, Y.T.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

Witek, B.

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Wu, X.

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

Wu, Z.

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

X. Hu, Y. Zhou, and Z. Wu, “A 2.5 dimensional vein imaging system for venipuncture,” Proc. SPIE 8668, 86685A (2013).
[Crossref]

Wufan, C.

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

Yang, J.

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

J. Yang and J. Yang, “Multi-channel gabor filter design for finger-vein image enhancement,” Fifth International Conference on Image and Graphics (2009), pp. 87–91.

J. Yang and J. Yang, “Multi-channel gabor filter design for finger-vein image enhancement,” Fifth International Conference on Image and Graphics (2009), pp. 87–91.

Yarmush, M.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

Yongtian, W.

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

Zeman, H.D.

H.D. Zeman, G. Lovhoiden, and C. Vrancken, “Prototype vein contrast enhancer,” Proc. SPIE 5318, 39–49 (2004).
[Crossref]

Zhang, Z.

Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Trans. Pattern Anal. 22(11), 1330–1334 (2000).
[Crossref]

Zhao, Y.

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

Zheng, Y.

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

Zhou, L.

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

Zhou, M.

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

Zhou, Y.

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

X. Hu, Y. Zhou, and Z. Wu, “A 2.5 dimensional vein imaging system for venipuncture,” Proc. SPIE 8668, 86685A (2013).
[Crossref]

Acta Automatica Sinica (1)

W. Kang and F. Deng, “Vein Image Segmentation Based on Distribution Ratio of Directional Fields,” Acta Automatica Sinica 35, 1496–1502 (2009).
[Crossref]

Biomed. Eng. Online (1)

Z. Shoujun, Y. Jian, W. Yongtian, and C. Wufan, “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking,” Biomed. Eng. Online 9, 40 (2010).
[Crossref] [PubMed]

Brit. J. Anaesth (1)

F.B. Chiao, F. Resta-Flarer, J. Lesser, J. Ng, A. Ganz, D. Pino-Luey, H. Bennett, C. Perkins, and B. Witek, “Vein visualization: patient characteristic factors and efficacy of a new infrared vein finder technology,” Brit. J. Anaesth 110(6), 966–971 (2013).
[Crossref] [PubMed]

Comput. Biol. Med (1)

M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med 37, 262–267 (2007).
[Crossref]

Curr. Med. Imaging Rev (1)

A. Shahzad, M. Naufal Mohamad Saad, N. Walter, A. Saeed Malik, and F. Meriaudeau, “A review on subcutaneous veins localization using imaging techniques,” Curr. Med. Imaging Rev 10(2), 125–133 (2014).
[Crossref]

IEEE Trans. Biomed. Eng. (1)

W.J. Cong, J. Yang, D.N. Ai, Y. Chen, Y. Liu, and Y.T. Wang, “Quantitative Analysis of Deformable Model-Based 3-D Reconstruction of Coronary Artery From Multiple Angiograms,” IEEE Trans. Biomed. Eng. 62(8), 2079–2090 (2015).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

Y. Zhao, L. Rada, K. Chen, S.P. Harding, and Y. Zheng, “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retina Images,” IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015).
[Crossref] [PubMed]

L. Zhou, M.S. Rzeszotarski, L.J. Singerman, and J.M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imaging 13(14), 619–626, (1994).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. (1)

Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Trans. Pattern Anal. 22(11), 1330–1334 (2000).
[Crossref]

International J. Opt. (1)

E. Nakamachi, Y. Morita, and Y. Mizuno, “Development of Automatic 3D Blood Vessel Search and Automatic Blood Sampling System by Using Hybrid Stereo-Autofocus Method,” International J. Opt. 2012, 258626 (2011).

J. Jilin University (1)

M.M. Chen, S.X. Guo, and X.H. Qian, “Finger vein image segmentation based on an improved LBF active contour model,” J. Jilin University 41, 1171–1176 (2011).

Mach. Vision Appl. (1)

N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl. 15(4), 194–203 (2004).
[Crossref]

Med. Eng. Phys (1)

N.J. Cuper, J.H. Klaessens, J.E. Jaspers, R. de Roode, H.J. Noordmans, J.C. de Graaff, and R.M. Verdaasdonk, “The use of near-infrared light for safe and effective visualization of subsurface blood vessels to facilitate blood withdrawal in children,” Med. Eng. Phys 35(4), 433–440 (2013).
[Crossref]

Med. Image Anal. (1)

O. Friman, M. Hindennach, C. Khnel, and H. O. Peitgen, “Multiple hypothesis template tracking of small 3D vessel structures,” Med. Image Anal. 14(2), 160–171 (2010).
[Crossref] [PubMed]

Opt. Express (1)

Philos Trans R Soc Lond B. (1)

D. Delpy, “Quantification in tissue near-infrared spectroscopy,” Philos Trans R Soc Lond B. 352(1354), 649–659 (1997).
[Crossref]

Phys. Med. Biol. (1)

J. Yang, W.J. Cong, Y. Chen, J.F. Fan, Y. Liu, and Y.T. Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Phys. Med. Biol. 59(4), 975–1003 (2014).
[Crossref] [PubMed]

PLoS ONE (1)

Y. Zhao, Y. Liu, X. Wu, S.P. Harding, and Y. Zheng, “Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase,” PLoS ONE 10(4), e0122332 (2015).
[Crossref] [PubMed]

P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, “Fast retinal vessel detection and measurement using wavelets and edge location refinement,” PloS One 7, e32435 (2012).
[Crossref] [PubMed]

Proc. SPIE (3)

M. Zhou, Z. Wu, D. Chen, and Y. Zhou, “An improved vein image segmentation algorithm based on SLIC and Niblack threshold method,” Proc. SPIE 9045, 90450D (2013).
[Crossref]

X. Hu, Y. Zhou, and Z. Wu, “A 2.5 dimensional vein imaging system for venipuncture,” Proc. SPIE 8668, 86685A (2013).
[Crossref]

H.D. Zeman, G. Lovhoiden, and C. Vrancken, “Prototype vein contrast enhancer,” Proc. SPIE 5318, 39–49 (2004).
[Crossref]

Other (12)

https://www.christiemed.com/products/veinviewer-models/veinviewer-vision

AccuVein AV400: The only handheld, non-contact vein illumination device, http://medidyne.dk/wp-content/uploads/ENG-accuvein-data-sheet.pdf

T.S. Perry, “Veebot: Making a robot that can draw blood faster and more safely than a human can,” http://spectrum.ieee.org/robotics/medical-robots/profile-veebot (2013).

R.D. Brewer and J.K. Salisbury, “Visual Vein-Finding for Robotic IV Insertion,” IEEE International Conference on Robotics and Automation, (2010), pp. 4597–4602.

Veebot’s Robot Technician Draws Blood From Patients, With Higher Accuracy Rate, http://www.medicaldaily.com/veebots-robot-technician-draws-blood-patients-higher-accuracy-rate-video-248176

P. Deepa, K. Mohanavelu, B.S. Sundersheshu, and V.C. Padaki, “Vein Identification and Localization for Automated Intravenous Drug Delivery System,” Volume 292 of the series Communications in Computer and Information Science, (2012), pp. 270–281.

A. Chen, K. Nikitczuk, J. Nikitczuk, T. Maguire, and M. Yarmush, “Portable robot for autonomous venipuncture using 3D near infrared image guidance,” Technology (Singap World Sci)01 (2013).

M. Vlachos and E. Dermatas, “Vein segmentation in infrared images using compound enhancing and crisp clustering,” Volume 5008 of the series Lecture Notes in Computer Science, (2008) pp. 393–402.

M. Vlachos and E. 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. 741–744.

J. Yang and J. Yang, “Multi-channel gabor filter design for finger-vein image enhancement,” Fifth International Conference on Image and Graphics (2009), pp. 87–91.

V.C. Paquit, F. 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.

H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” 9th European Conference on Computer Vision (2006), pp. 404–417.

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Figures (21)

Fig. 1
Fig. 1 Prototype of the imaging and augmented system: (a) front view; (b) front view without cover; and (c) enlarged view of the part in the red box.
Fig. 2
Fig. 2 (a) The overall system assembly drawing; (b) Front view; (c) Left view; (d) overlooking; (e) Six-degree of freedom of the support structure.
Fig. 3
Fig. 3 Pose relationship of binocular vision system.
Fig. 4
Fig. 4 Back-projection error of left and right views.
Fig. 5
Fig. 5 The projector calibration plate.
Fig. 6
Fig. 6 Back-projection error of the projector calibration.
Fig. 7
Fig. 7 Feature images: (a) rough segmentation image, (b) vein similarity image, (c) vein scale image, and (d) vein direction image.
Fig. 8
Fig. 8 Surface rendering of 3D reconstructed vein structures with scales in four different views using a real image.
Fig. 9
Fig. 9 Row/column Gray encoding project on the back of the skin surface.
Fig. 10
Fig. 10 Decoding images obtained by transforming the Gray code into binary values: (a) Column decoding image in the left view; (b) Row decoding image in the left view.
Fig. 11
Fig. 11 Surface rendering of 3D reconstruction skin in three different direction using a real hand.
Fig. 12
Fig. 12 Real subcutaneous vein image with low contrast and acutance of edges: (a) influenced by the light shadow; (b) deep-seated vessels; (c) obscured intersection of multiple vessels.
Fig. 13
Fig. 13 The segmentation results of real subcutaneous vein image by using (a) the proposed method; (b d) the Gaussian matched filtering method of three thresholds 1.0, 0.95 and 0.92.
Fig. 14
Fig. 14 Vein back-projection error. (a1∼a5, b1∼b5) Five different groups of real subcutaneous veins are imaged in the left and right views; (c1∼c5, d1∼d5) The ground truth segmented veins; (e1∼e5, f1∼f5) Obtained 3D vein structure back-projected into two imaging planes.
Fig. 15
Fig. 15 Back-projection error of vein center lines.
Fig. 16
Fig. 16 Measurement for distance between corresponding feature points in 3D space and 2D image. (a) distance between feature points in 3D point clouds; (b) distance between feature points in the simulated vein image.
Fig. 17
Fig. 17 Distance errors of vein feature points for nine simulated vein images.
Fig. 18
Fig. 18 Comparison of duration times for each real-time step.
Fig. 19
Fig. 19 3D reconstruction of a plane. (a) flat top view; (b) plane side view.
Fig. 20
Fig. 20 Relationship between the plane dip angle (0°10°20°30°) and the reconstruction precision of structure light.
Fig. 21
Fig. 21 Back-projection of the real subcutaneous veins. The first row shows four real subcutaneous veins; the second row shows the corresponding back-projection images; the last four rows are the 3D rendering and fusion display of the subcutaneous veins and skin surface.

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

λ p 0 = A [ R T ] p W
[ x y ] = ( 1 + k 1 r 2 + k 2 r 4 + k 3 r 6 1 + k 4 r 2 + k 5 r 4 + k 6 r 6 ) [ x d y d ] + [ 2 p 1 x d y d + p 2 ( r 2 + 2 x d 2 ) p 1 ( r 2 + 2 y d 2 ) + 2 p 2 x d y d ]
[ u v 1 ] = [ a x 0 u 0 0 a y v 0 0 0 1 ] [ x y 1 ]
D ( p H , p ) = Dis ( p H , p ) | cos ( v H , v ) | = ( x H x ) 2 + ( y H y ) 2 | v H v | v H v
q 1 , i = P 1 X i = K 1 [ I | 0 ] X i q 2 , i = P 2 X i = K 2 [ R | t ] X i
A X = [ u 1 p 1 3 T p 1 1 T v 1 p 1 3 T p 1 2 T u 2 p 2 3 T p 2 1 T v 2 p 2 3 T p 2 2 T ] X = 0
Gray ( j ) = FuncG ( ps + j μ )

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