Medical Image Analysis
Volume 14, Issue 3 , Pages 471-481 , June 2010

Glaucoma risk index:Automated glaucoma detection from color fundus images

  • Rüdiger Bock

      Affiliations

    • Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • Corresponding Author InformationCorresponding author. Address: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany. Tel.: +49 9131 85 27775; fax: +49 9131 303811.
    • ,
  • Jörg Meier

      Affiliations

    • Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • ,
  • László G. Nyúl

      Affiliations

    • Department of Image Processing and Computer Graphics, University of Szeged, Hungary
    • ,
  • Joachim Hornegger

      Affiliations

    • Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • ,
  • Georg Michelson

      Affiliations

    • Department of Ophthalmology, Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Germany
    • Interdisciplinary Center of Ophthalmic Preventive Medicine and Imaging, Friedrich-Alexander-University Erlangen-Nuremberg, Germany

Received 18 December 2008 ,Revised 17 December 2009 ,Accepted 18 December 2009.

References 

  1. Abràmoff MD, Alward WLM, Greenlee EC, Shuba L, Kim CY, Fingert JH, et al. Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest. Ophthalmol. Vis. Sci. 2007;48(4):1665–1673
  2. Al-Diri B, Hunter A, Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imag. 2009;28(9):1488–1497
  3. Alencar LM, Bowd C, Weinreb RN, Zangwill LM, Sample PA, Medeiros FA. Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma. Invest. Ophthalmol. Vis. Sci. 2008;49(5):1898–1906
  4. Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C., 2000. Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, New Orleans, USA, pp. 417–424.
  5. Betz P, Camps F, Collignon-Brach J, Lavergne G, Weekers R. Biometric study of the disc cup in open-angle glaucoma. Graefes Arch. Clin. Exp. Ophthalmol. 1982;218(2):70–74
  6. Blanco M, Penedo MG, Barreira N, Penas M, Carreira MJ. Localization and extraction of the optic disc using the fuzzy circular Hough transform. Lect. Notes Comput. Sci. 2006;4029:712–721
  7. Bledsoe, W.W., 1966. The model method in facial recognition. Tech. rep., Panoramic Research Inc., Palo Alto, CA, Rep. PRI:15.
  8. Bock, R., Meier, J., Michelson, G., Nyúl, L.G., Hornegger, J., 2007. Classifying glaucoma with image-based features from fundus photographs. In: 9th Annual Symposium of the German Association for Pattern Recognition, DAGM. Lecture Notes in Computer Science (LNCS), vol. 4713/2007, Berlin, pp. 355–365.
  9. Burgansky-Eliash Z, Wollstein G, Bilonick RA, Ishikawa H, Kagemann L, Schuman JS. Glaucoma detection with the Heidelberg Retina Tomograph 3. Ophthalmology. 2007;114(3):466–471
  10. Can A, Shen H, Turner JN, Tanenbaum HL, Roysam B. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inform. Technol. Biomed. 1999;3(2):125–138
  11. Canny JF. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986;8(6):679–698
  12. Chauhan BC, Blanchard JW, Hamilton DC, LeBlanc RP. Technique for detecting serial topographic changes in the optic disc and peripapillary retina using scanning laser tomography. Invest. Ophthalmol. Vis. Sci. 2000;41(3):775–782
  13. Chen PH, Lin CJ, Schölkopf B. A tutorial on -support vector machines. Appl. Stoch. Models Business Ind. 2005;21(2):111–136
  14. Chrástek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, et al. Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med. Image Anal. 2005;9(4):297–314
  15. DeLong E, DeLong D, Clarke-Pearson D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845
  16. EL-Manzalawy, Y., Honavar, V., 2005. WLSVM: Integrating LibSVM into Weka Environment. <http://www.cs.iastate.edu/yasser/wlsvm>.
  17. Fernández DC, Salinas HM, Puliafito CA. Automated detection of retinal layer structures on optical coherence tomography images. Opt. Express. 2005;13(25):10200–10216
  18. Greaney MJ, Hoffman DC, Garway-Heath DF, Nakla M, Coleman AL, Caprioli J. Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma. Invest. Ophthalmol. Vis. Sci. 2002;43(1):140–145
  19. Grisan, E., Pesce, A., Giani, A., Foracchia, M., Ruggeri, A., 2004. A new tracking system for the robust extraction of retinal vessel structure. In: Engineering in Medicine and Biology Society, 2004. IEMBS ’04. 26th Annual International Conference of the IEEE, vol. 1, pp. 1620–1623.
  20. Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imag. 2003;22(8):951–958
  21. Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 2000;19(3):203–210
  22. Ibanez, L., Schroeder, W., Ng, L., Cates, J., 2005. The ITK Software Guide, second ed. Kitware, Inc. ISBN 1-930934-15-7. <http://www.itk.org/ItkSoftwareGuide.pdf>.
  23. Klein BE, Klein R, Sponsel WE, Franke T, Cantor LB, Martone J, et al. Prevalence of glaucoma. The Beaver Dam Eye Study. Ophthalmology. 1992;99(10):1499–1504
  24. Li H, Chutatape O. Boundary detection of optic disk by a modified ASM method. Pattern Recognit. 2003;36(9):2093–2104
  25. Lin SC, Singh K, Jampel HD, Hodapp EA, Smith SD, Francis BA, et al. Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology. Ophthalmology. 2007;114(10):1937–1949
  26. Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, et al. Optic nerve head segmentation. IEEE Trans. Med. Imag. 2004;23(2):256–264
  27. Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH. Segmentation of blood vessels from red-free and fluorescein retinal images. Med. Image Anal. 2007;11(1):47–61
  28. Medeiros FA, Sample PA, Weinreb RN. Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. Am. J. Ophthalmol. 2004;137(5):863–871
  29. Medeiros FA, Zangwill LM, Bowd C, Weinreb RN. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. Arch. Ophthalmol. 2004;122(6):827–837
  30. Meier, J., Bock, R., Michelson, G., Nyúl, L.G., Hornegger, J., 2007. Effects of preprocessing eye fundus images on appearance based glaucoma classification. In: 12th International Conference on Computer Analysis of Images and Patterns, CAIP. Lecture Notes in Computer Science (LNCS), vol. 4673/2007, Berlin, pp. 165–173.
  31. Merickel MBJ, Abràmoff MD, Sonka M, Wu X. Segmentation of the optic nerve head combining pixel classification and graph search. Proc. SPIE. 2007;6512(1):651215
  32. Michelson G, Wärntges S, Hornegger J, Lausen B. The papilla as screening parameter for early diagnosis of glaucoma. Dtsch. Arztebl. Int. 2008;105(34–35):583–589
  33. Miglior S, Guareschi M, Albe’ E, Gomarasca S, Vavassori M, Orzalesi N. Detection of glaucomatous visual field changes using the Moorfields regression analysis of the Heidelberg retina tomograph. Am. J. Ophthalmol. 2003;136(1):26–33
  34. Narasimha-Iyer H, Can A, Roysam B, Stewart CV, Tanenbaum HL, Majerovics A, et al. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans. Biomed. Eng. 2006;53(6):1084–1098
  35. Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abràmoff, M.D., 2004. Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Proceedings of SPIE, vol. 5370, p. 648.
  36. Niemeijer M, Abràmoff M, van Ginneken B. Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Trans. Med. Imag. 2007;26(1):116–127
  37. Niemeijer M, Abràmoff MD, van Ginneken B. Fast detection of the optic disc and fovea in color fundus photographs. Med. Image Anal. 2009;13(6):859–870
  38. Niemeijer, M., van Ginneken, B., Abràmoff, M.D., 2009b. A linking framework for pixel classification based retinal vessel segmentation. In: Proceedings of SPIE, vol. 7262, p. 726216.
  39. Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imag. 2007;26(10):1357–1365
  40. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL. New support vector algorithms. Neural Comput. 2000;12(5):1207–1245
  41. Sehi M, Guaqueta DC, Feuer WJ, Greenfield DS. Scanning laser polarimetry with variable and enhanced corneal compensation in normal and glaucomatous eyes. Am. J. Ophthalmol. 2007;143(2):272–279
  42. Sharma P, Sample PA, Zangwill LM, Schuman JS. Diagnostic tools for glaucoma detection and management. Surv. Ophthalmol. 2008;53(Suppl. 1):S17–S32
  43. Shen J, Chan TF. Mathematical models for local nontexture inpaintings. SIAM J. Appl. Math. 2002;62(3):1019–1043
  44. Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 2006;25(9):1214–1222
  45. Sofka M, Stewart CV. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imag. 2006;25(12):1531–1546
  46. Staal J, Abràmoff M, Niemeijer M, Viergever M, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 2004;23(4):501–509
  47. Swindale NV, Stjepanovic G, Chin A, Mikelberg FS. Automated analysis of normal and glaucomatous optic nerve head topography images. Invest. Ophthalmol. Vis. Sci. 2000;41(7):1730–1742
  48. Turk M, Pentland A. Eigenfaces for recognition. J. Cognit. Neurosci. 1991;3(1):71–86
  49. Unser M. Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 1999;16(6):22–38
  50. Unser M, Aldroubi A, Eden M. B-spline signal processing. I. Theory. IEEE Trans. Signal Process. 1993;41(2):821–833
  51. Unser M, Aldroubi A, Eden M. B-spline signal processing. II. Efficiency design and applications. IEEE Trans. Signal Process. 1993;41(2):834–848
  52. Varma R, Steinmann WC, Scott IU. Expert agreement in evaluating the optic disc for glaucoma. Ophthalmology. 1992;99(2):215–221
  53. Vergara I, Norambuena T, Ferrada E, Slater A, Melo F. StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinform. 2008;9:265–269
  54. Wang L, Bhalerao A, Wilson R. Analysis of retinal vasculature using a multiresolution hermite model. IEEE Trans. Med. Imag. 2007;26(2):137–152
  55. Wollstein G, Garway-Heath DF, Hitchings RA. Identification of early glaucoma cases with the scanning laser ophthalmoscope. Ophthalmology. 1998;105(8):1557–1563
  56. Xu J, Chutatape O, Sung E, Zheng C, Kuan PCT. Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognit. 2007;40(7):2063–2076
  57. Youssif, A.A.A., Ghalwash, A.Z., Ghoneim, A.S., 2006. Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation. In: Proceedings of the Third Cairo International Biomedical Engineering Conference (CIBEC’06), pp. 1–5.
  58. Youssif AA, Ghalwash AZ, Ghoneim A. Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans. Med. Imag. 2008;27(1):11–18
  59. Zhu, X., Rangayyan, R., Ells, A., 2009. Detection of the optic nerve head in fundus images of the retina using the hough transform for circles. J. Digit. Imag. <http://dx.doi.org/10.1007/s10278-009-9189-5>.

PII: S1361-8415(09)00150-9

doi: 10.1016/j.media.2009.12.006

Medical Image Analysis
Volume 14, Issue 3 , Pages 471-481 , June 2010

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