OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 27 — Dec. 19, 2011
  • pp: 26239–26248

Automatic montage of SD-OCT data sets

Ying Li, Giovanni Gregori, Byron L. Lam, and Philip J. Rosenfeld  »View Author Affiliations

Optics Express, Vol. 19, Issue 27, pp. 26239-26248 (2011)

View Full Text Article

Enhanced HTML    Acrobat PDF (2026 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



This paper proposes an automatic algorithm for the montage of OCT data sets, which produces a composite 3D OCT image over a large field of view out of several separate, partially overlapping OCT data sets. First the OCT fundus images (OFIs) are registered, using blood vessel ridges as the feature of interest and a two step iterative procedure to minimize the distance between all matching point pairs over the set of OFIs. Then the OCT data sets are merged to form a full 3D montage using cross-correlation. The algorithm was tested using an imaging protocol consisting of 8 OCT images for each eye, overlapping to cover a total retinal region of approximately 50x35 degrees. The results for 3 normal eyes and 3 eyes with retinal degeneration are analyzed, showing registration errors of 1.5±0.3 and 2.0±0.8 pixels respectively.

© 2011 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(110.4500) Imaging systems : Optical coherence tomography
(170.4460) Medical optics and biotechnology : Ophthalmic optics and devices
(170.5755) Medical optics and biotechnology : Retina scanning

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: September 19, 2011
Revised Manuscript: November 17, 2011
Manuscript Accepted: November 20, 2011
Published: December 8, 2011

Virtual Issues
Vol. 7, Iss. 2 Virtual Journal for Biomedical Optics

Ying Li, Giovanni Gregori, Byron L. Lam, and Philip J. Rosenfeld, "Automatic montage of SD-OCT data sets," Opt. Express 19, 26239-26248 (2011)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. Z. Yehoshua, P. J. Rosenfeld, G. Gregori, and F. Penha, “Spectral domain optical coherence tomography imaging of dry age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 41(6Suppl), S6–S14 (2010). [CrossRef] [PubMed]
  2. B. Potsaid, B. Baumann, D. Huang, S. Barry, A. E. Cable, J. S. Schuman, J. S. Duker, and J. G. Fujimoto, “Ultrahigh speed 1050nm swept source/Fourier domain OCT retinal and anterior segment imaging at 100,000 to 400,000 axial scans per second,” Opt. Express 18(19), 20029–20048 (2010). [CrossRef] [PubMed]
  3. B. Povazay, B. Hermann, B. Hofer, V. Kajić, E. Simpson, T. Bridgford, and W. Drexler, “Wide-field optical coherence tomography of the choroid in vivo,” Invest. Ophthalmol. Vis. Sci. 50(4), 1856–1863 (2008). [CrossRef] [PubMed]
  4. T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser,” Opt. Express 19(4), 3044–3062 (2011). [CrossRef] [PubMed]
  5. M. Emmenlauer, O. Ronneberger, A. Ponti, P. Schwarb, A. Griffa, A. Filippi, R. Nitschke, W. Driever, and H. Burkhardt, “XuvTools: free, fast and reliable stitching of large 3D datasets,” J. Microsc. 233(1), 42–60 (2009). [CrossRef] [PubMed]
  6. S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics 25(11), 1463–1465 (2009). [CrossRef] [PubMed]
  7. Y. Yu and H. Peng, “Automated high speed stitching of large 3D microscopic images,” Proc. of IEEE 2011 International Symposium on Biomedical Imaging: From Nano to Macro 238–241 (2011).
  8. J. Yoo, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, “Increasing the field-of-view of dynamic cardiac OCT via post-acquisition mosaicing without affecting frame-rate or spatial resolution,” Biomed. Opt. Express 2(9), 2614–2622 (2011). [CrossRef] [PubMed]
  9. S. L. Jiao, R. Knighton, X. R. Huang, G. Gregori, and C. A. Puliafito, “Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography,” Opt. Express 13(2), 444–452 (2005). [CrossRef] [PubMed]
  10. M. Wojtkowski, T. Bajraszewski, I. Gorczyńska, P. Targowski, A. Kowalczyk, W. Wasilewski, and C. Radzewicz, “Ophthalmic imaging by spectral optical coherence tomography,” Am. J. Ophthalmol. 138(3), 412–419 (2004). [CrossRef] [PubMed]
  11. M. Niemeijer, M. K. Garvin, B. V. Ginneken, M. Sonka, and M. D. Abramoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE 6914, 69141R (2008).
  12. Z. Hu, M. Niemeijer, M. D. Abràmoft, K. Lee, and M. K. Garvin, “Automated segmentation of 3-D spectral OCT retinal blood vessels by neural canal opening false positive suppression,” Med. Image Comput. Comput. Assist. Interv. 13(Pt 3), 33–40 (2010). [PubMed]
  13. R. Szeliski, “Image alignment and stitching: A tutorial,” MSR-TR-2004–92, Microsoft Research (2004).
  14. X. Fang, B. Luo, H. Zhao, J. Tang, and S. Zhai, “New multi-resolution image stitching with local and global alignment,” IET Comput. Vision 4(4), 231–246 (2010). [CrossRef]
  15. H. Y. Shum and R. Szeliski, “Construction of panoramic image mosaics with global and local alignment,” Int. J. Comput. Vis. 36(2), 101–130 (2000). [CrossRef]
  16. D. L. Milgram, “Computer Methods for Creating Photomosaics,” IEEE Trans. Comput. C-24(11), 1113–1119 (1975). [CrossRef]
  17. G. H. Yang, C. V. Stewart, M. Sofka, and C. L. Tsai, “Registration of challenging image pairs: initialization, estimation, and decision,” IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1973–1989 (2007). [CrossRef] [PubMed]
  18. A. A. Mahurkar, M. A. Vivino, B. L. Trus, E. M. Kuehl, M. B. Datiles, and M. I. Kaiser-Kupfer, “Constructing retinal fundus photomontages. A new computer-based method,” Invest. Ophthalmol. Vis. Sci. 37(8), 1675–1683 (1996). [PubMed]
  19. P. C. Cattin, H. Bay, L. Van Gool, and G. Szekely, “Retina mosaicing using local features,” Med. Image Comput. Comput. Assist. Interv. 4191, 185–192 (2006).
  20. D. E. Becker, A. Can, J. N. Turner, H. L. Tanenbaum, and B. Roysam, “Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination,” IEEE Trans. Biomed. Eng. 45(1), 105–118 (1998). [CrossRef] [PubMed]
  21. A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: Mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002). [CrossRef]
  22. S. Lee, M. D. Abramoff, and J. M. Reinhardt, “Retinal image mosaicing using the radial distortion correction model,” Proc. SPIE Medical Imaging 6914, 91435 (2008).
  23. G. H. Yang and C. V. Stewart, “Covariance-driven mosaic formation from sparsely-overlapping image sets with application to retinal image mosaicing,” Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 804–810 (2004).
  24. T. E. Choe, I. Cohen, M. Lee, and G. Medioni, “Optimal global mosaic generation from retinal images,” Proceedings of 18th International Conference on Pattern Recognition 3, 681–684 (2006).
  25. W. Aguilar, M. E. Martinez-Perez, Y. Frauel, F. Escolano, M. A. Lozano, and A. Espinosa-Romero, “Graph-based methods for retinal mosaicing and vascular characterization,” Lect. Notes Comput. Sci. 4538, 25–36 (2007). [CrossRef]
  26. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004). [CrossRef]
  27. J. Zheng, J. Tian, K. Deng, X. Dai, X. Zhang, and M. Xu, “Salient feature region: a new method for retinal image registration,” IEEE Trans. Inf. Technol. Biomed. 15(2), 221–232 (2011). [CrossRef] [PubMed]
  28. J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–105 (1999). [CrossRef]
  29. Y. Li, G. Gregori, R. W. Knighton, B. J. Lujan, and P. J. Rosenfeld, “Registration of OCT fundus images with color fundus photographs based on blood vessel ridges,” Opt. Express 19(1), 7–16 (2011). [CrossRef] [PubMed]
  30. A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 347–364 (2002). [CrossRef]
  31. T. Chanwimaluang, G. L. Fan, and S. R. Fransen, “Hybrid retinal image registration,” IEEE Trans. Inf. Technol. Biomed. 10(1), 129–142 (2006). [CrossRef] [PubMed]
  32. S. Lee, M. D. Abramoff, and J. M. Reinhardt, “Retinal image mosaicing using the radial distortion correction model - art. no. 691435,” Proc. SPIE  6914, 91435 (2008).
  33. M. Sofka, Y. Gehua, and C. V. Stewart, “Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework,” Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on 1–8 (2007).
  34. Y. Li, N. Hutchings, R. W. Knighton, G. Gregori, R. J. Lujan, and J. G. Flanagan, “Ridge-branch-based blood vessel detection algorithm for multimodal retinal images,” Proc. SPIE. 7259, 72594K (2009).
  35. T. Fabritius, S. Makita, M. Miura, R. Myllylä, and Y. Yasuno, “Automated segmentation of the macula by optical coherence tomography,” Opt. Express 17(18), 15659–15669 (2009). [CrossRef] [PubMed]
  36. G. Gregori, F. H. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011). [PubMed]
  37. Q. Yang, C. A. Reisman, Z. G. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. P. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Express 18(20), 21293–21307 (2010). [CrossRef] [PubMed]

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