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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 5 — May. 1, 2012
  • pp: 1127–1140

Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming

Stephanie J. Chiu, Cynthia A. Toth, Catherine Bowes Rickman, Joseph A. Izatt, and Sina Farsiu  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 5, pp. 1127-1140 (2012)
http://dx.doi.org/10.1364/BOE.3.001127


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Abstract

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

© 2012 OSA

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

ToC Category:
Image Processing

History
Original Manuscript: March 21, 2012
Revised Manuscript: April 24, 2012
Manuscript Accepted: April 25, 2012
Published: April 26, 2012

Citation
Stephanie J. Chiu, Cynthia A. Toth, Catherine Bowes Rickman, Joseph A. Izatt, and Sina Farsiu, "Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming," Biomed. Opt. Express 3, 1127-1140 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-5-1127


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References

  1. A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging30(2), 484–496 (2011). [CrossRef] [PubMed]
  2. K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express2(6), 1743–1756 (2011). [CrossRef] [PubMed]
  3. Y. Y. Liu, M. Chen, H. Ishikawa, G. Wollstein, J. S. Schuman, and J. M. Rehg, “Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding,” Med. Image Anal.15(5), 748–759 (2011). [CrossRef] [PubMed]
  4. Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express2(9), 2493–2503 (2011). [CrossRef] [PubMed]
  5. S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012). [CrossRef] [PubMed]
  6. 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(18), 19413–19428 (2010). [CrossRef] [PubMed]
  7. V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P. L. Rosin, and W. Drexler, “Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model,” Biomed. Opt. Express3(1), 86–103 (2012). [CrossRef] [PubMed]
  8. L. Duan, M. Yamanari, and Y. Yasuno, “Automated phase retardation oriented segmentation of chorio-scleral interface by polarization sensitive optical coherence tomography,” Opt. Express20(3), 3353–3366 (2012). [CrossRef] [PubMed]
  9. J. Eichel, A. Mishra, P. Fieguth, D. Clausi, and K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in Canadian Conference on Computer and Robot Vision, 2009. CRV '09 (IEEE, 2009), pp. 313–320.
  10. F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express2(6), 1524–1538 (2011). [CrossRef] [PubMed]
  11. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991). [CrossRef] [PubMed]
  12. J. D. Ding, L. V. Johnson, R. Herrmann, S. Farsiu, S. G. Smith, M. Groelle, B. E. Mace, P. Sullivan, J. A. Jamison, U. Kelly, O. Harrabi, S. S. Bollini, J. Dilley, D. Kobayashi, B. Kuang, W. Li, J. Pons, J. C. Lin, and C. B. Rickman, “Anti-amyloid therapy protects against retinal pigmented epithelium damage and vision loss in a model of age-related macular degeneration,” Proc. Natl. Acad. Sci. U.S.A.108(28), E279–E287 (2011). [CrossRef] [PubMed]
  13. M. R. Hee, C. A. Puliafito, J. S. Duker, E. Reichel, J. G. Coker, J. R. Wilkins, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Topography of diabetic macular edema with optical coherence tomography,” Ophthalmology105(2), 360–370 (1998). [CrossRef] [PubMed]
  14. T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol.127(6), 688–693 (1999). [CrossRef] [PubMed]
  15. J. Liang, D. R. Williams, and D. T. Miller, “Supernormal vision and high-resolution retinal imaging through adaptive optics,” J. Opt. Soc. Am. A14(11), 2884–2892 (1997). [CrossRef] [PubMed]
  16. H. Hofer, L. Chen, G. Y. Yoon, B. Singer, Y. Yamauchi, and D. R. Williams, “Improvement in retinal image quality with dynamic correction of the eye’s aberrations,” Opt. Express8(11), 631–643 (2001). [CrossRef] [PubMed]
  17. A. Roorda, F. Romero-Borja, W. Donnelly Iii, H. Queener, T. Hebert, and M. Campbell, “Adaptive optics scanning laser ophthalmoscopy,” Opt. Express10(9), 405–412 (2002). [PubMed]
  18. R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express2(9), 2577–2589 (2011). [CrossRef] [PubMed]
  19. D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging24(8), 929–945 (2005). [CrossRef] [PubMed]
  20. C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol.92(2), 197–203 (2008). [CrossRef] [PubMed]
  21. G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abramoff,, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging29(6), 1321–1330 (2010). [CrossRef] [PubMed]
  22. R. T. Smith, J. K. Chan, T. Nagasaki, U. F. Ahmad, I. Barbazetto, J. Sparrow, M. Figueroa, and J. Merriam, “Automated detection of macular drusen using geometric background leveling and threshold selection,” Arch. Ophthalmol.123(2), 200–206 (2005). [CrossRef] [PubMed]
  23. A. D. Mora, P. M. Vieira, A. Manivannan, and J. M. Fonseca, “Automated drusen detection in retinal images using analytical modelling algorithms,” Biomed. Eng. Online10(1), 59 (2011). [CrossRef] [PubMed]
  24. S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE6844, 68440D, 68440D-12 (2008). [CrossRef]
  25. N. Lee, A. F. Laine, and R. T. Smith, “A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration,” in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. EMBS 2007 (IEEE, 2007), pp. 4965–4968.
  26. S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys.39(1), 503–513 (2012). [CrossRef] [PubMed]
  27. S. V. Patel, J. W. McLaren, J. J. Camp, L. R. Nelson, and W. M. Bourne, “Automated quantification of keratocyte density by using confocal microscopy in vivo,” Invest. Ophthalmol. Vis. Sci.40(2), 320–326 (1999). [PubMed]
  28. F. J. Sanchez-Marin, “Automatic segmentation of contours of corneal cells,” Comput. Biol. Med.29(4), 243–258 (1999). [CrossRef] [PubMed]
  29. A. Ruggeri, E. Grisan, and J. Jaroszewski, “A new system for the automatic estimation of endothelial cell density in donor corneas,” Br. J. Ophthalmol.89(3), 306–311 (2005). [CrossRef] [PubMed]
  30. M. E. Díaz, G. Ayala, R. Sebastian, and L. Martínez-Costa, “Granulometric analysis of corneal endothelium specular images by using a germ-grain model,” Comput. Biol. Med.37(3), 364–375 (2007). [CrossRef] [PubMed]
  31. A. H. Karimi, A. Wong, and K. Bizheva, “Automated detection and cell density assessment of keratocytes in the human corneal stroma from ultrahigh resolution optical coherence tomograms,” Biomed. Opt. Express2(10), 2905–2916 (2011). [CrossRef] [PubMed]
  32. B. Xue, S. S. Choi, N. Doble, and J. S. Werner, “Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera,” J. Opt. Soc. Am. A24(5), 1364–1372 (2007). [CrossRef] [PubMed]
  33. K. Y. Li and A. Roorda, “Automated identification of cone photoreceptors in adaptive optics retinal images,” J. Opt. Soc. Am. A24(5), 1358–1363 (2007). [CrossRef] [PubMed]
  34. M. Pircher, J. S. Kroisamer, F. Felberer, H. Sattmann, E. Götzinger, and C. K. Hitzenberger, “Temporal changes of human cone photoreceptors observed in vivo with SLO/OCT,” Biomed. Opt. Express2(1), 100–112 (2011). [CrossRef] [PubMed]
  35. M. Mujat, R. D. Ferguson, A. H. Patel, N. Iftimia, N. Lue, and D. X. Hammer, “High resolution multimodal clinical ophthalmic imaging system,” Opt. Express18(11), 11607–11621 (2010). [CrossRef] [PubMed]
  36. R. S. Jonnal, O. P. Kocaoglu, Q. Wang, S. Lee, and D. T. Miller, “Phase-sensitive imaging of the outer retina using optical coherence tomography and adaptive optics,” Biomed. Opt. Express3(1), 104–124 (2012). [CrossRef] [PubMed]
  37. K. Loquin, I. Bloch, K. Nakashima, F. Rossant, and M. Paques, “Photoreceptor detection in in-vivo adaptive optics images of the retina: towards a simple interactive tool for the physicians,” in 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro (IEEE 2011), pp. 191–194.
  38. C. A. Glasbey and M. J. Young, “Maximum a posteriori estimation of image boundaries by dynamic programming,” J. R. Stat. Soc. Ser. C Appl. Stat.51(2), 209–221 (2002). [CrossRef]
  39. S. Timp and N. Karssemeijer, “A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography,” Med. Phys.31(5), 958–971 (2004). [CrossRef] [PubMed]
  40. Z. Yan, B. J. Matuszewski, S. Lik-Kwan, and C. J. Moore, “A novel medical image segmentation method using dynamic programming,” in International Conference on Medical Information Visualisation—BioMedical Visualisation,2007. MediVis 200 (IEEE 2007), pp. 69–74.
  41. S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Trans. Med. Imaging30(12), 2126–2133 (2011). [CrossRef] [PubMed]
  42. S. Farsiu, J. Christofferson, B. Eriksson, P. Milanfar, B. Friedlander, A. Shakouri, and R. Nowak, “Statistical detection and imaging of objects hidden in turbid media using ballistic photons,” Appl. Opt.46(23), 5805–5822 (2007). [CrossRef] [PubMed]
  43. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik1(1), 269–271 (1959). [CrossRef]
  44. N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA291(15), 1900–1901 (2004). [CrossRef] [PubMed]
  45. P. Soille, Morphological Image Analysis: Principles and Applications (Springer, 1999).
  46. T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms (The MIT Press, 2001).
  47. R. Gonzalez and R. Woods, Digital Image Processing, 3rd ed. (Prentice Hall, 2007).
  48. H. Takeda, S. Farsiu, and P. Milanfar, “Robust kernel regression for restoration and reconstruction of images from sparse noisy data,” in 2006 IEEE International Conference on Image Processing (IEEE, 2006), pp. 1257–1260.
  49. L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity-based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express3(5), 927–942 (2012). [CrossRef]

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