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Segmentation and Reconstruction Techniques for Modeling of Blood Vessel

by Vrushali Sudhir Taware, Pranali C. Choudhari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 8
Year of Publication: 2017
Authors: Vrushali Sudhir Taware, Pranali C. Choudhari
10.5120/ijca2017913392

Vrushali Sudhir Taware, Pranali C. Choudhari . Segmentation and Reconstruction Techniques for Modeling of Blood Vessel. International Journal of Computer Applications. 162, 8 ( Mar 2017), 22-27. DOI=10.5120/ijca2017913392

@article{ 10.5120/ijca2017913392,
author = { Vrushali Sudhir Taware, Pranali C. Choudhari },
title = { Segmentation and Reconstruction Techniques for Modeling of Blood Vessel },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 8 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number8/27264-2017913392/ },
doi = { 10.5120/ijca2017913392 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:29.155037+05:30
%A Vrushali Sudhir Taware
%A Pranali C. Choudhari
%T Segmentation and Reconstruction Techniques for Modeling of Blood Vessel
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 8
%P 22-27
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Vascular diseases are nowadays one of the serious issues which have a huge impact on someone's life. Number of researchers at different universities as well as medical device manufacturers are working in this field for better understanding of the vascular characteristics. It is expected that three dimensional structure of blood vessels can provide comprehensive visualization of vessel geometry. This information will be useful in diagnosis and therapy related to vascular diseases. Several studies describe different numerical approaches to reconstruct a modeling of blood vessels closest to reality by using medical imaging. This paper gives extensive literature survey on segmentation and reconstruction techniques for artery modeling that uses various image modalities such as X Ray Angiography, Magnetic Resonance Angiography, Computed Tomography Angiography, Ultrasound etc. for the assessment of blood vessels.

References
  1. F. Yang, Z. G. Hou, S. H. Mi, G. Bin Bian, and X. L. Xie, “3D modeling of coronary arteries based on tubular-enhanced CURVES segmented regions for robotic surgical simulation,” 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, December, pp. 2013–2018, 2013.
  2. J. Zhou et al., “Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography,” Proceeding of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, vol. 2015–Novem, pp. 695–698, 2015.
  3. D. Ping, L. Risser, C. Metz, and L. Neefjes, “Coronary artery motion modeling from 3d cardiac CT sequences using template matching and graph search,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE 2010, pp. 1053–1056, 2010.
  4. O. Wink, W. J. Niessen, and M. A. Viergever, “Fast delineation and visualization of vessels in 3-D angiographic images,” IEEE Transactions on Medical Imaging, vol. 19, no. 4, pp. 337–346, 2000.
  5. K. C. Wang, R. W. Dutton, and C. A. Taylor, “Improving geometric model construction for blood flow modeling: Geometric image segmentation and image-based model construction for computational hemodynamics,” IEEE Engineering in Medicine And Biology, vol. 18, no. 6, pp. 33–39, 1999.
  6. G. Biglino et al., “3D-manufactured patient-specific models of congenital heart defects for communication in clinical practice: feasibility and acceptability,” BMJ Open, vol. 5, no. 4, p. e007165, 2015.
  7. D. Parker, C. A. Taylor, and K. Wang, “Imaged based 3D solid model construction of human arteries for blood flow simulations,” Proceeding of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,vol. 2, no. 2, pp. 998–1001, 1998.
  8. Q. Yingyi, “Segmentation and reconstruction of 3D artery models for surgical planning,” Diss. 2008.
  9. R. M. Tayebi et al., “Coronary artery segmentation in angiograms with pattern recognition techniques - A survey,” Proceeding of the 2013 International Conference on IEEE in Advanced Computer Science Applications and Technologies (ACSAT) 2013, pp. 321–326, 2014.
  10. L. Athanasiou et al., “Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography – comparison and registration with IVUS,” BMC Medical Imaging, vol. 16, no. 1, pp. 9, 2016.
  11. A. I. Sakellarios et al., “Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data,” Elsevier, Magnetic Resonance Imaging, vol. 30, no. 8, pp. 1068–1082, 2012.
  12. D. Craiem, M. E. Casciaro, S. Graf, G. Chironi, A. Simon, and R. L. Armentano, “Effects of aging on thoracic aorta size and shape: A non-contrast CT study,” Proceeding of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, pp. 4986–4989, 2012.
  13. Y. Sen, Y. Zhang, Y. Qian, and M. Morgan, “A comparison of medical image segmentation methods for cerebral aneurysm computational hemodynamics,” IEEE 2011 4th International Conference on Biomedical Engineering and Informatics, vol. 2, pp. 901–904, 2011.
  14. E. Smistad and F. Lindseth, “Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve,” IEEE Transaction on Medical Imaging, vol. 35, no. 3, pp. 752–761, 2016.
  15. T. Markiewicz, M. Dziekiewicz, S. Osowski, R. Boguslawska-Walecka, W. Kozlowski, and M. Maruszynski, “Computerized system for quantitative assessment of atherosclerotic plaques in the femoral and iliac arteries visualized by multislice computed tomography,” IEEE Transaction on Biomedical Engineering, vol. 62, no. 6, pp. 1490–1502, 2015.
  16. S. Fekkes et al., “2D versus 3D cross-correlation-based radial and circumferential strain imaging in a 3D atherosclerotic carotid artery model using ultrafast plane wave ultrasound,” IEEE International Ultrasonics Symposium Proceedings, no. 12122, pp. 1–4, 2015.
  17. M. Goyal and A. P. Agrawal, “Global optimization for 3-D reconstruction of coronary artery trees from angiographic image sequence,” IEEE 2013 Fourth International Conference on Computer Communication and Networking Technology, pp. 1–5, 2013.
  18. O. Hajati, K. Zarrabi, R. Karimi, and A. Hajati, “CFD simulation of hemodynamics in sequential and individual coronary bypass grafts based on multislice CT scan datasets,” Proceeding of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, pp. 641–644, 2012.
  19. I. Wächter, “3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography,” Diss. UCL (University College London), pp. 1–216, 2009.
  20. M. Schaap et al., “Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms,” Medical Image Analysis, vol. 13, no. 5, pp. 701–714, 2009, in press.
  21. F. Zhao, R. Bhotika, M. Carlo, “Coronary artery tree tracking with robust junction detection in 3D CT angiography,” IEEE ISBI-2011, pp. 2066–2071, 2011.
  22. L. S. Athanasiou et al., “A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: Comparison with virtual histology,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 391–400, 2012.
  23. A. Uus, P. Liatsis, M. M. Jawaid, R. Rajani, and E. Benderskaya, “Assessment of Stenosis Introduced Flow Resistance in CCTA-Reconstructed Coronary Arteries,” IEEE Xplore, pp. 313–320, 2015.
  24. S. Voros et al., “Prospective validation of standardized, 3-dimensional, quantitative coronary computed tomographic plaque measurements using radiofrequency backscatter intravascular ultrasound as reference standard in intermediate coronary arterial lesions,” Elsevier, JACC Cardiovascular Intervention, vol. 4, no. 2, pp. 198–208, 2011.
  25. R. Xiao, J. Yang, J. Fan, D. Ai, G. Wang, and Y. Wang, “Shape context and projection geometry constrained vasculature matching for 3D reconstruction of coronary artery,” Elsevier, Neurocomputing, vol. 195, pp. 65–73, 2016.
  26. C. V. Bourantas et al., “A method for 3D reconstruction of coronary arteries using biplane angiography and intravascular ultrasound images,” Elsevier, Computerized Medical Imaging and Graphics, vol. 29, no. 8, pp. 597–606, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

3D model segmentation and detection centerline extraction

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