OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 10 — May. 7, 2012
  • pp: 11451–11465

Three-dimensional reconstruction of blood vessels extracted from retinal fundus images

M. Elena Martinez-Perez and Arturo Espinosa-Romero  »View Author Affiliations


Optics Express, Vol. 20, Issue 10, pp. 11451-11465 (2012)
http://dx.doi.org/10.1364/OE.20.011451


View Full Text Article

Enhanced HTML    Acrobat PDF (2255 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We present a 3D reconstruction of retinal blood vessel trees using two views of fundus images. The problem is addressed by using well known computer vision techniques which consider: 1) The recovery of camera-eyeball model parameters by an auto-calibration method. The camera parameters are found via the solution of simplified Kruppa equations, based on correspondences found by a LMedS optimisation correlation between pairs of eight different views. 2) The extraction of blood vessels and skeletons from two fundus images. 3) The matching of corresponding points of the two skeleton trees. The trees are previously labelled during the analysis of 2D binary images. Finally, 4) the lineal triangulation of matched correspondence points and the surface modelling via generalised cylinders using diameter measurements extracted from the 2D binary images. The method is nearly automatic and it is tested with 2 sets of 10 fundus retinal images, each one taken from different subjects. Results of 3D vein and artery trees reconstructions are shown.

© 2012 OSA

OCIS Codes
(100.2000) Image processing : Digital image processing
(150.0150) Machine vision : Machine vision
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.4470) Medical optics and biotechnology : Ophthalmology

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: November 16, 2011
Revised Manuscript: March 5, 2012
Manuscript Accepted: March 6, 2012
Published: May 4, 2012

Virtual Issues
Vol. 7, Iss. 7 Virtual Journal for Biomedical Optics

Citation
M. Elena Martinez-Perez and Arturo Espinosa-Romero, "Three-dimensional reconstruction of blood vessels extracted from retinal fundus images," Opt. Express 20, 11451-11465 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-10-11451


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. M. E. Martinez-Perez, A. D. Hughes, A. V. Stanton, S. A. Thom, N. Chapman, A. A. Bharath, and K. H. Parker, “Retinal vascular tree morphology: A semi-automatic quantification,” IEEE Trans. Biomed. Eng.49, 912–917 (2002). [CrossRef]
  2. M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, “Segmentation of blood vessels from red-free and fluorescein retinal images,” Med. Image Anal.11, 47–61 (2007). [CrossRef] [PubMed]
  3. K. Deguchi, J. Noami, and H. Hontani, “3d fundus pattern reconstruction and display from multiple fundus images,” in Proceedings 15th International Conference on Pattern Recognition (IEEE, 2000), pp. 94–97. [CrossRef]
  4. K. Deguchi, D. Kawamata, K. Mizutani, H. Hontani, and K. Wakabayashi, “3d fundus shape reconstruction and display from stereo fundus images,” IEICE Trans. Inf. Syst.E83-D, 1408–1414 (2000).
  5. T. E. Choe, G. Medioni, I. Cohen, A. C. Walsh, and S. R. Sadda, “2-D registration and 3-D shape inference of the retinal fundus from fluorescein images,” Med. Image Anal.12, 174–190 (2008). [CrossRef]
  6. D. Liu, N. Wood, X. Xu, N. Witt, A. Hughes, and S. Thom, “3D reconstruction of the retinal arterial tree using subject-specific fundus images,” in Advances in Computational Vision and Medical Image Processing, J. M. R. S. Tavares and R. M. N. Jorge ed. (Springer, 2009), pp. 187–201. [CrossRef]
  7. M. E. Martinez-Perez and A. Espinosa-Romero, “3D Reconstruction of Retinal Blood Vessels From Two Views,” in Proceedings of the 4th Indian Conference on Computer Vision, Graphics and Image Processing, B. Chanda, S. Chandran, and L. Davis, ed. (Indian Statistical Insitute, 2004), pp. 258–263.
  8. J. Arnold, J. Gates, and K. Taylor, “Possible errors in the measurement of retinal lesions,” Invest. Ophthalmol. Vis. Sci.34, 2576–2580 (1993). [PubMed]
  9. M. I. A. Lourakis and R. Deriche, “Camera self-calibration using the singular value decomposition of the fundamental matrix: from point correspondences to 3d measurements,” Research Report 3748, INRIA Sophia-Antipolis, (1999).
  10. M. Sonka, Image Processing, Analysis, and Machine Vision (Thomson, 2008).
  11. K. Kanatani, Geometry Computation for Machine Vision (Oxford Science Publications, 1993).
  12. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge Uiversity Press, 2000).
  13. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outilier Detection (John Wiley & Sons, 1987). [CrossRef]
  14. Z. Zhang, R. Deriche, O. Faugeras, and Q.-T. Luong, “A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry,” Research Report 2273, INRIA Sophia-Antipolis, (1994).
  15. R. I. Hartley, “Estimation of relative camera positions for uncalibrated cameras,” in Proceedings of the 2nd European Conference on Computer Vision, G. Sandini, ed. (Springer-Verlag, 1992), pp. 579–587.
  16. J. Ponce, “Straight homogeneous generalized cylinders: Differential geometry and uniqueness results,” Int. J. Comput. Vis.4, 79–100 (1990). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Supplementary Material


» Media 1: MPG (3153 KB)     
» Media 2: MPG (2936 KB)     
» Media 3: MPG (6384 KB)     
» Media 4: MPG (2438 KB)     
» Media 5: MPG (1653 KB)     
» Media 6: MPG (4242 KB)     

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited