Non-invasive patient registration based on 3D feature points of vein

Computer assisted surgical system (CASS) development is one of the most vigorously pursued research fields in biomedical engineering because of its numerous advantages. Nevertheless, patient registration is still a heavy burden for both patients and surgeons. Meanwhile, it is well known that veins can be seen under the skin in near infrared images because of the properties of hemoglobin. In this paper, we propose a novel registration method based on this principle. In our registration method, we extract feature points from veins using an infrared stereo vision system, and then match them with the feature points extracted from preoperative medical images. Experiments were performed to verify the feasibility of this vein-based registration. As a result, we confirmed the availability of our proposed method, and showed that it can be utilized for numerous applications.

[1]  Kenneth A. Schenkman,et al.  Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin , 2002 .

[2]  Hui Zhu,et al.  Adaptive thresholding by variational method , 1998, IEEE Trans. Image Process..

[3]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Martin Wolf,et al.  Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications. , 2007, Journal of biomedical optics.

[5]  L. Joskowicz,et al.  Surface-based facial scan registration in neuronavigation procedures: a clinical study. , 2009, Journal of neurosurgery.

[6]  H. Zeman,et al.  The clinical evaluation of vein contrast enhancement , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Deukhee Lee,et al.  Markerless registration for intracerebral hemorrhage surgical system using weighted Iterative Closest Point (ICP) , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Rasmus Larsen,et al.  Convolution approach for feature detection in topological skeletons obtained from vascular patterns , 2011, 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[9]  Kunwoo Lee,et al.  Novel methods for 3D postoperative analysis of total knee arthroplasty using 2D-3D image registration. , 2011, Clinical biomechanics.

[10]  Tai-hoon Kim,et al.  Palm Vein Authentication System: A Review , 2010 .

[11]  Alexandru Telea,et al.  An Augmented Fast Marching Method for Computing Skeletons and Centerlines , 2002, VisSym.

[12]  J. Weickert Applications of nonlinear diffusion in image processing and computer vision , 2000 .

[13]  Hideki Yoshikawa,et al.  Improvement of depth position in 2-D/3-D registration of knee implants using single-plane fluoroscopy , 2004, IEEE Transactions on Medical Imaging.

[14]  Chan Gook Park,et al.  Theoretical analysis of reflection process using MEMS CCR array for the localization of optics-based sensor node , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[15]  Christoph Busch,et al.  Spectral minutiae for vein pattern recognition , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[16]  Hyochoong Bang,et al.  Numerical modeling, testing and bias drift analysis of MEMS based three-axis gyroscope for accurate angular rate estimation for attitude determination of nano-satellites , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[17]  James K. Hahn,et al.  Registration of 3D CT Data to 2D Endoscopic Image using a Gradient Mutual Information based Viewpoint Matching for Image-Guided Medialization Laryngoplasty , 2010, J. Comput. Sci. Eng..

[18]  Yorktown Heights,et al.  An Image-directed Robotic System for Precise Orthopaedic Surgery , 1990 .