Abstract

Optic disc or optic nerve (ON) head extraction in retinal images has widespread applications in retinal disease diagnosis and human identification in biometric systems. This paper introduces a fast and automatic algorithm for detecting and extracting the ON region accurately from the retinal images without the use of the blood-vessel information. In this algorithm, to compensate for the destructive changes of the illumination and also enhance the contrast of the retinal images, we estimate the illumination of background and apply an adaptive correction function on the curvelet transform coefficients of retinal images. In other words, we eliminate the fault factors and pave the way to extract the ON region exactly. Then, we detect the ON region from retinal images using the morphology operators based on geodesic conversions, by applying a proper adaptive correction function on the reconstructed image’s curvelet transform coefficients and a novel powerful criterion. Finally, using a local thresholding on the detected area of the retinal images, we extract the ON region. The proposed algorithm is evaluated on available images of DRIVE and STARE databases. The experimental results indicate that the proposed algorithm obtains an accuracy rate of 100% and 97.53% for the ON extractions on DRIVE and STARE databases, respectively.

© 2012 Optical Society of America

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  1. L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
    [CrossRef]
  2. T. Walter and J. C. Klein, “Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques,” Medical Data Analysis, Lecture Notes in Computer Science (Springer, 2001), Vol. 2199, pp. 282–287.
  3. A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
    [CrossRef]
  4. S. Sekhar, W. Al-Nuairny, and A. K. Nandi, “Automated localisation of retinal optic disk using Hough transform,” in Proceedings of International Symposium on Biomedical Imaging: Nano to Macro (IEEE, 2008), pp. 1577–1580.
  5. A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
    [CrossRef]
  6. H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
    [CrossRef]
  7. F. Haar, “Automatic localization of the optic disc in digital colour images of the human retina,” Master’s thesis (Utrecht University, 2005).
  8. A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
    [CrossRef]
  9. S. Lu, “Automatic optic disc detection using retinal background and retinal blood vessels,” in Proceedings of 3rd IEEE International Conference on Biomedical Engineering and Informatics (BMEI) (IEEE, 2010), pp. 141–145.
  10. M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
    [CrossRef]
  11. A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag. 22, 951–958 (2003).
    [CrossRef]
  12. P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
    [CrossRef]
  13. E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
    [CrossRef]
  14. G. D. Joshi and J. Sivaswamy, “Colour retinal image enhancement based on domain knowledge,” in Proceedings of 6th Indian Conference on Computer Vision, Graphics & Image Processing (2008), pp. 591–598.
  15. R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice-Hall, 2002), pp. 567–636.
  16. H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images,” in Proceedings of 9th IEEE International Conference on Computer Vision (IEEE, 2003), pp. 394–399.
  17. M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
    [CrossRef]
  18. Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.
  19. S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng. 58, 88–94 (2011).
    [CrossRef]
  20. “DRIVE: Digital Retinal Images for Vessel Extraction,” http://www.isi.uu.nl/Research/Databases/DRIVE/ .
  21. “STARE: STructured Analysis of the REtina,” http://www.ces.clemson.edu/~ahoover/stare/ .
  22. A. E. Mahfouz and A. S. Fahmy, “Fast localization of the optic disc using projection of image features,” IEEE Trans. Image Process. 19, 3285–3289 (2010).
    [CrossRef]
  23. M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
    [CrossRef]

2011

S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng. 58, 88–94 (2011).
[CrossRef]

2010

A. E. Mahfouz and A. S. Fahmy, “Fast localization of the optic disc using projection of image features,” IEEE Trans. Image Process. 19, 3285–3289 (2010).
[CrossRef]

A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
[CrossRef]

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

2009

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
[CrossRef]

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[CrossRef]

2008

A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
[CrossRef]

2007

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

2006

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

2005

M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
[CrossRef]

2004

M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
[CrossRef]

2003

A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag. 22, 951–958 (2003).
[CrossRef]

Abràmoff, M. D.

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[CrossRef]

Al-Nuairny, W.

S. Sekhar, W. Al-Nuairny, and A. K. Nandi, “Automated localisation of retinal optic disk using Hough transform,” in Proceedings of International Symposium on Biomedical Imaging: Nano to Macro (IEEE, 2008), pp. 1577–1580.

Aquino, A.

A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
[CrossRef]

Candes, E. J.

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

Cheung, N.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

Chutatape, O.

H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images,” in Proceedings of 9th IEEE International Conference on Computer Vision (IEEE, 2003), pp. 394–399.

Demanet, L.

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

Donoho, D. L.

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

Eswaran, C.

A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
[CrossRef]

Fahmy, A. S.

A. E. Mahfouz and A. S. Fahmy, “Fast localization of the optic disc using projection of image features,” IEEE Trans. Image Process. 19, 3285–3289 (2010).
[CrossRef]

Feng, P.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

Foracchia, M.

M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
[CrossRef]

M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
[CrossRef]

Gao, Q.

Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.

Gegundez-Arias, M. E.

A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
[CrossRef]

Ghalwash, A. Z.

A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
[CrossRef]

Ghoneim, A.

A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
[CrossRef]

Goldbaum, M.

A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag. 22, 951–958 (2003).
[CrossRef]

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice-Hall, 2002), pp. 567–636.

Grisan, E.

M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
[CrossRef]

M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
[CrossRef]

Haar, F.

F. Haar, “Automatic localization of the optic disc in digital colour images of the human retina,” Master’s thesis (Utrecht University, 2005).

Hati, S.

A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
[CrossRef]

Hoover, A.

A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag. 22, 951–958 (2003).
[CrossRef]

Jin, W.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

Joshi, G. D.

G. D. Joshi and J. Sivaswamy, “Colour retinal image enhancement based on domain knowledge,” in Proceedings of 6th Indian Conference on Computer Vision, Graphics & Image Processing (2008), pp. 591–598.

Klein, J. C.

T. Walter and J. C. Klein, “Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques,” Medical Data Analysis, Lecture Notes in Computer Science (Springer, 2001), Vol. 2199, pp. 282–287.

Kong, Y. H.

Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.

Li, H.

H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images,” in Proceedings of 9th IEEE International Conference on Computer Vision (IEEE, 2003), pp. 394–399.

Lim, J. H.

S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng. 58, 88–94 (2011).
[CrossRef]

Lim, L. S.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

Lin, F.

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

Liu, T.

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

Lu, S.

S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng. 58, 88–94 (2011).
[CrossRef]

S. Lu, “Automatic optic disc detection using retinal background and retinal blood vessels,” in Proceedings of 3rd IEEE International Conference on Biomedical Engineering and Informatics (BMEI) (IEEE, 2010), pp. 141–145.

Mahfouz, A. E.

A. E. Mahfouz and A. S. Fahmy, “Fast localization of the optic disc using projection of image features,” IEEE Trans. Image Process. 19, 3285–3289 (2010).
[CrossRef]

Marin, D.

A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
[CrossRef]

Mi, D.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

Mitchell, P.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

Nandi, A. K.

S. Sekhar, W. Al-Nuairny, and A. K. Nandi, “Automated localisation of retinal optic disk using Hough transform,” in Proceedings of International Symposium on Biomedical Imaging: Nano to Macro (IEEE, 2008), pp. 1577–1580.

Niemeijer, M.

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[CrossRef]

Pan, Y.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

Reza, A. W.

A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
[CrossRef]

Ruggeri, A.

M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
[CrossRef]

M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
[CrossRef]

Saw, S. M.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

Schaefer, G.

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

Sekhar, S.

S. Sekhar, W. Al-Nuairny, and A. K. Nandi, “Automated localisation of retinal optic disk using Hough transform,” in Proceedings of International Symposium on Biomedical Imaging: Nano to Macro (IEEE, 2008), pp. 1577–1580.

Sivaswamy, J.

G. D. Joshi and J. Sivaswamy, “Colour retinal image enhancement based on domain knowledge,” in Proceedings of 6th Indian Conference on Computer Vision, Graphics & Image Processing (2008), pp. 591–598.

van Ginneken, B.

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[CrossRef]

Walter, T.

T. Walter and J. C. Klein, “Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques,” Medical Data Analysis, Lecture Notes in Computer Science (Springer, 2001), Vol. 2199, pp. 282–287.

Wei, B.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

Wong, T. Y.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice-Hall, 2002), pp. 567–636.

Ying, L.

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

Youssif, A.

A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
[CrossRef]

Yuan, J. Sh.

Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.

Zhao, Z. B.

Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.

Zhou, H.

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

Am. J. Ophthalmol.

L. S. Lim, S. M. Saw, N. Cheung, P. Mitchell, and T. Y. Wong, “Relationship of retinal vascular caliber with optic disc and macular structure,” Am. J. Ophthalmol. 148, 68–375(2009).
[CrossRef]

IEEE Trans. Biomed. Eng.

S. Lu and J. H. Lim, “Automatic optic disc detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng. 58, 88–94 (2011).
[CrossRef]

IEEE Trans. Image Process.

A. E. Mahfouz and A. S. Fahmy, “Fast localization of the optic disc using projection of image features,” IEEE Trans. Image Process. 19, 3285–3289 (2010).
[CrossRef]

IEEE Trans. Med. Imag.

M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag. 23, 1189–1195(2004).
[CrossRef]

A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag. 22, 951–958 (2003).
[CrossRef]

A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag. 29, 1860–1869 (2010).
[CrossRef]

A. Youssif, A. Z. Ghalwash, and A. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag. 27, 11–18 (2008).
[CrossRef]

J. Med. Syst.

A. W. Reza, C. Eswaran, and S. Hati, “Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds,” J. Med. Syst. 33, 73–80 (2009).
[CrossRef]

Med. Image Anal.

M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal. 9, 179–190 (2005).
[CrossRef]

M. Niemeijer, M. D. Abràmoff, and B. van Ginneken, “Fast detection of the optic disc and fovea in color fundus photographs,” Med. Image Anal. 13, 859–870 (2009).
[CrossRef]

Multimedia Tools Appl.

H. Zhou, G. Schaefer, T. Liu, and F. Lin, “Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm,“ Multimedia Tools Appl. 49, 447–462 (2010).
[CrossRef]

Pattern Recogn. Lett.

P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recogn. Lett. 28, 516–522 (2007).
[CrossRef]

SIAM Multiscale Model. Simul.

E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, “Fast discrete curvelet transforms,” SIAM Multiscale Model. Simul. 5, 861–899 (2006).
[CrossRef]

Other

G. D. Joshi and J. Sivaswamy, “Colour retinal image enhancement based on domain knowledge,” in Proceedings of 6th Indian Conference on Computer Vision, Graphics & Image Processing (2008), pp. 591–598.

R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice-Hall, 2002), pp. 567–636.

H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images,” in Proceedings of 9th IEEE International Conference on Computer Vision (IEEE, 2003), pp. 394–399.

Z. B. Zhao, J. Sh. Yuan, Q. Gao, and Y. H. Kong, “Wavelet image de-noising method based on noise standard deviation estimation,” in Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition (IEEE, 2007), pp. 1910–1914.

“DRIVE: Digital Retinal Images for Vessel Extraction,” http://www.isi.uu.nl/Research/Databases/DRIVE/ .

“STARE: STructured Analysis of the REtina,” http://www.ces.clemson.edu/~ahoover/stare/ .

F. Haar, “Automatic localization of the optic disc in digital colour images of the human retina,” Master’s thesis (Utrecht University, 2005).

S. Lu, “Automatic optic disc detection using retinal background and retinal blood vessels,” in Proceedings of 3rd IEEE International Conference on Biomedical Engineering and Informatics (BMEI) (IEEE, 2010), pp. 141–145.

S. Sekhar, W. Al-Nuairny, and A. K. Nandi, “Automated localisation of retinal optic disk using Hough transform,” in Proceedings of International Symposium on Biomedical Imaging: Nano to Macro (IEEE, 2008), pp. 1577–1580.

T. Walter and J. C. Klein, “Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques,” Medical Data Analysis, Lecture Notes in Computer Science (Springer, 2001), Vol. 2199, pp. 282–287.

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Figures (14)

Fig. 1.
Fig. 1.

ON is the area on the right where the blood vessels converge.

Fig. 2.
Fig. 2.

Block diagram of the proposed ON region extraction algorithm.

Fig. 3.
Fig. 3.

Nonuniform sampling grid.

Fig. 4.
Fig. 4.

(a) Retinal image with uneven illumination and contrast, (b) first principal component of (a) from PCA analysis, (c) background pixel classification using PCA analysis, (d) image enhancement on single channel with PCA analysis, (e) background pixel classification without the use of PCA analysis, and (f) image enhancement on single channel without the use of PCA analysis.

Fig. 5.
Fig. 5.

Curvelet tiling of frequency plane.

Fig. 6.
Fig. 6.

Enhanced retinal image by our preprocessing procedure.

Fig. 7.
Fig. 7.

Example of the structural element with s=1 and radius R=3.

Fig. 8.
Fig. 8.

(a) Reconstructed retinal image from the retinal image in Fig. 6, (b) “peak image” from (a), and (c) extracted ON region.

Fig. 9.
Fig. 9.

Sample of the concentric circles in the ON region.

Fig. 10.
Fig. 10.

(a)–(c) Samples of the original retinal images from DRIVE database; (d)–(f) their corresponding enhanced images were obtained from our preprocessing procedure.

Fig. 11.
Fig. 11.

(a)–(c) Samples of the original retinal images from STARE database; (d)–(f) their corresponding enhanced images were obtained from our preprocessing procedure.

Fig. 12.
Fig. 12.

(a)–(c) Reconstructed images from the retinal images in Fig. 10; (d)–(f) their corresponding “peak image” from (a)–(c); (g)–(i) their corresponding extracted ON regions.

Fig. 13.
Fig. 13.

(a)–(c) Reconstructed images from the retinal images in Fig. 11; (d)–(f) their corresponding “peak image” from (a)–(d); (g)–(i) their corresponding extracted ON regions.

Fig. 14.
Fig. 14.

Accuracy rate of the ON extraction on the retinal images from the STARE database with different sizes of the structure element.

Tables (1)

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Table 1. Comparison between our ON Region Extraction Algorithm and Other Algorithms on Different Databases

Equations (16)

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U(x,y)=I(x,y)k1·L(x,y)k2.C(x,y),
D(x,y)=|I(x,y)μ(x,y)σ(x,y)|.
t=μα·σ,
C(j,l,k)=1(2π)2f(ω)Uj(Rθlω)eixk(j,l),ωdω,
Rθl=(CosθlSinθlSinθlCosθl).
y(x,σ)={1if|x|<σc|x|aσaσ·(maσ)p+2·aσ|x|aσifaσ|x|<2aσK1·(m|x|)pif2aσ|x|<mK2·(m|x|)qif|x|m,
m=K(Mijσ),
σ=π216(k2)(l2)|f(x,y)*M|,M=[121242121],
OpeningG=RFD[(FΘnB)],
RgFD=Dg(k)(F)=Dg(1)[Dg(k1)(F)],g=F,
Dg(1)(F)=(FB)g,
p=s·R,
y(x)={K1(mσ)pif|x|<aσK2(m|x|)pifaσ|x|<mK3m|x|,
SUB(x,y)=d=0r1I(d)N1d=0r2I(d)N2,
CritO(x,y)=F(x,y).SUB(x,y),
Accuracy=Area(TD)Area(TD).

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