OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 2 — Feb. 1, 2012
  • pp: 327–339

Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO)

Rolando Estrada, Carlo Tomasi, Michelle T. Cabrera, David K. Wallace, Sharon F. Freedman, and Sina Farsiu  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 2, pp. 327-339 (2012)
http://dx.doi.org/10.1364/BOE.3.000327


View Full Text Article

Enhanced HTML    Acrobat PDF (1453 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 methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online.

© 2012 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.2960) Image processing : Image analysis
(170.4470) Medical optics and biotechnology : Ophthalmology

ToC Category:
Image Processing

History
Original Manuscript: November 30, 2011
Revised Manuscript: January 2, 2012
Manuscript Accepted: January 2, 2012
Published: January 18, 2012

Citation
Rolando Estrada, Carlo Tomasi, Michelle T. Cabrera, David K. Wallace, Sharon F. Freedman, and Sina Farsiu, "Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO)," Biomed. Opt. Express 3, 327-339 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-2-327


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. W. Tasman, A. Patz, J. A. McNamara, R. S. Kaiser, M. T. Trese, and B. T. Smith, “Retinopathy of prematurity: The life of a lifetime disease,” Am. J. Ophthalmol.141, 167 – 174 (2006). [CrossRef] [PubMed]
  2. G. A. Gole, A. L. Ells, X. Katz, G. Holmstrom, A. R. Fielder, A. Capone, J. T. Flynn, W. G. Good, J. M. Holmes, J. A. McNamara, E. A. Palmer, G. Quinn, E, M. J. Shapiro, M. G. J. Trese, and D. K. Wallace, “The international classification of retinopathy of prematurity revisited,” Arch. Ophthalmol.123, 991–999 (2011).
  3. D. K. Wallace, G. E. Quinn, S. F. Freedman, and M. F. Chiang, “Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity,” J. Am. Assoc. Pediatric Ophthalmol. Strabismus12, 352 – 356 (2008). [CrossRef]
  4. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18, 19413–19428 (2010). [CrossRef] [PubMed]
  5. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag.8, 263–269 (1989). [CrossRef]
  6. C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv.36, 81–121 (2004). [CrossRef]
  7. Q. Li, J. You, L. Zhang, and P. Bhattacharya, “Automated retinal vessel segmentation using Gabor filters and scale multiplication,” in Proceedings of System, Man and Cybernetics (IEEE, 2006), pp. 3521–3527.
  8. E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag.26, 1357–1365 (2007). [CrossRef]
  9. J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag.25, 1214–1222 (2006). [CrossRef]
  10. J. Staal, M. Abràmoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag.23, 501–509 (2004). [CrossRef]
  11. B. Lam, Y. Gao, and A. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag.29, 1369–1381 (2010). [CrossRef]
  12. G. Lathen, J. Jonasson, and M. Borga, “Blood vessel segmentation using multi-scale quadrature filtering,” Pattern Recogn. Lett.31, 762–767 (2010). [CrossRef]
  13. D. Marín, A. Aquino, M. Gegúndez-Arias, and J. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag.30, 146–158 (2011). [CrossRef]
  14. A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag.19, 203–210 (2002). [CrossRef]
  15. T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” in Proceedings of the International Symposium on Circuits and Systems (IEEE2003), pp. 21–24.
  16. M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer1999), pp. 90–97. [CrossRef]
  17. F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process.10, 1010–1019 (2002). [CrossRef]
  18. L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, and U. Skands, “Quantitative measurement of changes in retinal vessel diameter in ocular fundus images,” Pattern Recogn. Lett. (21), 1215–1223 (2000). [CrossRef]
  19. M. Cree, D. Cornforth, and HF. Jelinek, “Vessel segmentation and tracking using a two-dimensional model,” in Proceedings of Image and Vision Computing New Zealand (IVCNZ, 2005), pp. 345–350.
  20. F. Benmansour and L. Cohen, “Tubular structure segmentation based on minimal path method and anisotropic enhancement,” Int. J. Comput. Vision92, 192–210 (2011). [CrossRef]
  21. H. Li and A. Yezzi, “Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines,” IEEE Trans. Med. Imag.26, 1213–1223 (2007). [CrossRef]
  22. M. Pechaud, R. Keriven, and G. Peyre, “Extraction of tubular structures over an orientation domain,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2009), pp. 336–342.
  23. O. Wink, W. Niessen, and M. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imag.23, 130–133 (2004). [CrossRef]
  24. S. Ahmad, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted assessment of plus disease in retinopathy of prematurity using video indirect ophthalmoscopy images,” Retina28, 1458–1462 (2008). [CrossRef] [PubMed]
  25. A. Kiely, D. Wallace, S. Freedman, and Z. Zhao, “Computer-assisted measurement of retinal vascular width and tortuosity in retinopathy of prematurity,” Arch. Ophthalmol.128, 847–852 (2010). [CrossRef] [PubMed]
  26. T. Lindeberg, “Edge detection and ridge detection with automatic scale selection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 1996), pp. 465–470.
  27. J. Sethian, Level Set Methods and Fast Marching Methods (Cambridge University Press, 1999).
  28. E. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math.1, 269–271 (1959). [CrossRef]
  29. R. Estrada, C. Tomasi, M. Cabrera, D. Wallace, S. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express2, 2871–2887 (2011). [CrossRef] [PubMed]
  30. R. Bellman, Dynamic Programming (Dover, 2003).
  31. T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2001).
  32. M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE5370, 648–656 (2004). [CrossRef]
  33. B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “REVIEW - A reference data set for retinal vessel profiles,” in Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society (IEEE, 2008), pp. 2262–2265.
  34. J. Cohen, “A Coefficient of agreement for nominal scales,” Educ. Psychol. Meas.20, 37–46 (1960). [CrossRef]
  35. J. Gibbons and S. Chakraborti, Nonparametric Statistical Inference (CRC Press, 2003).

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.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited