Abstract

Medical image analysis is a very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness. An automated system for early detection of DR can save a patient’s vision and can also help the ophthalmologists in screening of DR. The background or nonproliferative DR contains four types of lesions, i.e., microaneurysms, hemorrhages, hard exudates, and soft exudates. This paper presents a method for detection and classification of exudates in colored retinal images. We present a novel technique that uses filter banks to extract the candidate regions for possible exudates. It eliminates the spurious exudate regions by removing the optic disc region. Then it applies a Bayesian classifier as a combination of Gaussian functions to detect exudate and nonexudate regions. The proposed system is evaluated and tested on publicly available retinal image databases using performance parameters such as sensitivity, specificity, and accuracy. We further compare our system with already proposed and published methods to show the validity of the proposed system.

© 2012 Optical Society of America

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  1. E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).
  2. A. F. Amos, D. J. McCarty, and P. Zimmet, “The rising global burden of diabetes and its complications: estimates and projections to the year 2010,” Diabet. Med. 14, S7–S85 (1997).
    [CrossRef]
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    [CrossRef]
  4. R. N. Frank, “Diabetic retinopathy,” Prog. Retinal Eye Res. 14, 361–392 (1995).
    [CrossRef]
  5. S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
    [CrossRef]
  6. D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
    [CrossRef]
  7. C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachain, and A. Singlavanija, “Automated screening system for diabetic retinopathy,” in Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (IEEE, 2003), pp. 915–920.
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    [CrossRef]
  10. A. W. Reza, C. Eswaran, and K. Dimyati, “Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation,” J. Med. Syst. 35, 1491–1501 (2011).
    [CrossRef]
  11. H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
    [CrossRef]
  12. C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
    [CrossRef]
  13. 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]
  14. A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).
  15. A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
    [CrossRef]
  16. A. Osareh, B. Shadgar, and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Trans. Inf. Technol. Biomed. 13, 535–545 (2009).
    [CrossRef]
  17. T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
    [CrossRef]
  18. H. Wang, W. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in colour retinal images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 181–187.
  19. H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
    [CrossRef]
  20. U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).
  21. S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
    [CrossRef]
  22. M. U. Akram and S. A. Khan, “Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy,” J. Med. Syst., doi:10.1007/s10916-011-9802-2 (2011).
    [CrossRef]
  23. A. Tariq and M. U. Akram, “An automated system for colored retinal image background and noise segmentation,” in 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA) (IEEE, 2010), pp. 405–409.
  24. J. Sung, S. Y. Bang, and S. Choi, “A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition,” Pattern Recogn. Lett. 27, 66–75 (2006).
    [CrossRef]
  25. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice-Hall, 2002).
  26. M. U. Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal image: optic disk localization and detection,” Image Analysis and Recognition, Lecture Notes in Computer Science 6112 (Springer, 2010), pp. 40–49.
  27. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 1st ed. (Academic, 1999).
  28. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (Wiley, 2001).
  29. T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).
  30. T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

2012 (1)

H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
[CrossRef]

2011 (2)

A. W. Reza, C. Eswaran, and K. Dimyati, “Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation,” J. Med. Syst. 35, 1491–1501 (2011).
[CrossRef]

M. U. Akram and S. A. Khan, “Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy,” J. Med. Syst., doi:10.1007/s10916-011-9802-2 (2011).
[CrossRef]

2010 (2)

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

2009 (2)

A. Osareh, B. Shadgar, and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Trans. Inf. Technol. Biomed. 13, 535–545 (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]

2008 (2)

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

2006 (2)

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

J. Sung, S. Y. Bang, and S. Choi, “A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition,” Pattern Recogn. Lett. 27, 66–75 (2006).
[CrossRef]

2005 (1)

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

2004 (1)

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

2003 (1)

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]

2002 (1)

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
[CrossRef]

2001 (1)

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

1999 (1)

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

1998 (1)

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

1997 (1)

A. F. Amos, D. J. McCarty, and P. Zimmet, “The rising global burden of diabetes and its complications: estimates and projections to the year 2010,” Diabet. Med. 14, S7–S85 (1997).
[CrossRef]

1995 (1)

R. N. Frank, “Diabetic retinopathy,” Prog. Retinal Eye Res. 14, 361–392 (1995).
[CrossRef]

Aarskog, D.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Acharya, U. R.

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

Akram, M. U.

M. U. Akram and S. A. Khan, “Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy,” J. Med. Syst., doi:10.1007/s10916-011-9802-2 (2011).
[CrossRef]

A. Tariq and M. U. Akram, “An automated system for colored retinal image background and noise segmentation,” in 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA) (IEEE, 2010), pp. 405–409.

M. U. Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal image: optic disk localization and detection,” Image Analysis and Recognition, Lecture Notes in Computer Science 6112 (Springer, 2010), pp. 40–49.

Aldington, S. J.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

Amos, A. F.

A. F. Amos, D. J. McCarty, and P. Zimmet, “The rising global burden of diabetes and its complications: estimates and projections to the year 2010,” Diabet. Med. 14, S7–S85 (1997).
[CrossRef]

Arof, H.

H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
[CrossRef]

Bang, S. Y.

J. Sung, S. Y. Bang, and S. Choi, “A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition,” Pattern Recogn. Lett. 27, 66–75 (2006).
[CrossRef]

Barman, S.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

Bell, G. I.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Bergmann, I.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Boyce, J.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

Boyce, J. A.

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

Butt, W. H.

M. U. Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal image: optic disk localization and detection,” Image Analysis and Recognition, Lecture Notes in Computer Science 6112 (Springer, 2010), pp. 40–49.

Can, A.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Chee, C.

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

Choi, S.

J. Sung, S. Y. Bang, and S. Choi, “A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition,” Pattern Recogn. Lett. 27, 66–75 (2006).
[CrossRef]

Chua, K. C.

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

Cook, H. L.

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

Dailey, M. N.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

Dimyati, K.

A. W. Reza, C. Eswaran, and K. Dimyati, “Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation,” J. Med. Syst. 35, 1491–1501 (2011).
[CrossRef]

Duda, R. O.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (Wiley, 2001).

Dumskyj, M.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

Erginay, A.

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
[CrossRef]

Eswaran, C.

A. W. Reza, C. Eswaran, and K. Dimyati, “Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation,” J. Med. Syst. 35, 1491–1501 (2011).
[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]

Frank, R. N.

R. N. Frank, “Diabetic retinopathy,” Prog. Retinal Eye Res. 14, 361–392 (1995).
[CrossRef]

Goh, K.

H. Wang, W. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in colour retinal images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 181–187.

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, 2nd ed. (Prentice-Hall, 2002).

Hart, P. E.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (Wiley, 2001).

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]

Himaga, M.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

Holman, R. R.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

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]

Hsu, W.

H. Wang, W. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in colour retinal images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 181–187.

Iqbal, K.

M. U. Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal image: optic disk localization and detection,” Image Analysis and Recognition, Lecture Notes in Computer Science 6112 (Springer, 2010), pp. 40–49.

Isa, H. M.

H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
[CrossRef]

Joner, G.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Kalesnykiene, V.

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Kamarainen, J. K.

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

Kamarainen, J.-K.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Kauppi, T.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

Khan, A.

M. U. Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal image: optic disk localization and detection,” Image Analysis and Recognition, Lecture Notes in Computer Science 6112 (Springer, 2010), pp. 40–49.

Khan, S. A.

M. U. Akram and S. A. Khan, “Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy,” J. Med. Syst., doi:10.1007/s10916-011-9802-2 (2011).
[CrossRef]

Kingsley, R. M.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Klein, J. C.

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
[CrossRef]

Klein, R.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Klviinen, H.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

Kohner, E. M.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

Kongbunkiat, V.

C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachain, and A. Singlavanija, “Automated screening system for diabetic retinopathy,” in Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (IEEE, 2003), pp. 915–920.

Koutroumbas, K.

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 1st ed. (Academic, 1999).

Lee, E. T.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Lee, M.

H. Wang, W. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in colour retinal images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 181–187.

Lee, S. C.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Lensu, L.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

Lipkind, G. M.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Majerovics, A.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Manley, S. E.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

Markham, R.

A. Osareh, B. Shadgar, and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Trans. Inf. Technol. Biomed. 13, 535–545 (2009).
[CrossRef]

A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Classification and localisation of diabetic-related eye disease,” in Proceedings of the 7th European Conference on Computer Vision, Lecture Notes in Computer Science 2353 (Springer, 2002), pp. 502–516.

Massin, P.

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
[CrossRef]

Matthews, D. R.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

McCarty, D. J.

A. F. Amos, D. J. McCarty, and P. Zimmet, “The rising global burden of diabetes and its complications: estimates and projections to the year 2010,” Diabet. Med. 14, S7–S85 (1997).
[CrossRef]

Mirmehdi, M.

A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Classification and localisation of diabetic-related eye disease,” in Proceedings of the 7th European Conference on Computer Vision, Lecture Notes in Computer Science 2353 (Springer, 2002), pp. 502–516.

Moe, Y. A.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

Molven, A.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Narasimha-Iyer, H.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Ng, E. Y. K.

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

Njolstad, P. R.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Nordbo, A. M.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Nussey, S.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

Nwe, K. T.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

Osareh, A.

A. Osareh, B. Shadgar, and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Trans. Inf. Technol. Biomed. 13, 535–545 (2009).
[CrossRef]

A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Classification and localisation of diabetic-related eye disease,” in Proceedings of the 7th European Conference on Computer Vision, Lecture Notes in Computer Science 2353 (Springer, 2002), pp. 502–516.

Philipson, L. H.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Phoojaruenchanachain, S.

C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachain, and A. Singlavanija, “Automated screening system for diabetic retinopathy,” in Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (IEEE, 2003), pp. 915–920.

Pietil, J.

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Raeder, H.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Raninen, A.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Reza, A. W.

A. W. Reza, C. Eswaran, and K. Dimyati, “Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation,” J. Med. Syst. 35, 1491–1501 (2011).
[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]

Ringdal, M.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Roysam, B.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Russell, D.

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Shadgar, B.

A. Osareh, B. Shadgar, and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Trans. Inf. Technol. Biomed. 13, 535–545 (2009).
[CrossRef]

Singh, H.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Singlavanija, A.

C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachain, and A. Singlavanija, “Automated screening system for diabetic retinopathy,” in Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (IEEE, 2003), pp. 915–920.

Sinthanayothin, C.

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachain, and A. Singlavanija, “Automated screening system for diabetic retinopathy,” in Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (IEEE, 2003), pp. 915–920.

Sopharak, A.

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

Sorri, I.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

Sovik, O.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Steiner, D. F.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Stewart, C. V.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Stork, D. G.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (Wiley, 2001).

Stoy, J.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Stratton, I. M.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

Sung, J.

J. Sung, S. Y. Bang, and S. Choi, “A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition,” Pattern Recogn. Lett. 27, 66–75 (2006).
[CrossRef]

Tanenbaum, H. L.

H. Narasimha-Iyer, A. Can, B. Roysam, C. V. Stewart, H. L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006).
[CrossRef]

Tariq, A.

A. Tariq and M. U. Akram, “An automated system for colored retinal image background and noise segmentation,” in 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA) (IEEE, 2010), pp. 405–409.

Theodoridis, S.

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 1st ed. (Academic, 1999).

Thomas, B.

A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Classification and localisation of diabetic-related eye disease,” in Proceedings of the 7th European Conference on Computer Vision, Lecture Notes in Computer Science 2353 (Springer, 2002), pp. 502–516.

Turner, R. C.

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

Undlien, D. E.

A. Molven, M. Ringdal, A. M. Nordbo, H. Raeder, J. Stoy, G. M. Lipkind, D. F. Steiner, L. H. Philipson, I. Bergmann, D. Aarskog, D. E. Undlien, G. Joner, O. Sovik, G. I. Bell, and P. R. Njolstad, “Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes,” Diabetes 57, 1131–1135 (2008).
[CrossRef]

Usher, D.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

Uusitalo, H.

T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms,” Tech. Rep. (2005).

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Uyyanonvara, B.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

Vongkittirux, S.

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

Voutilainen, R.

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Klviinen, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” Tech. Rep. (2006).

Walter, T.

T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag. 21, 1236–1243 (2002).
[CrossRef]

Wang, H.

H. Wang, W. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in colour retinal images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 181–187.

Wang, Y.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Warn, A.

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

Wei, W.

U. R. Acharya, K. C. Chua, E. Y. K. Ng, W. Wei, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” J. Med. Syst. 32, 431–488 (2008).

Williamson, T.

A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J. Mod. Opt. 57, 124–135 (2010).
[CrossRef]

Williamson, T. H.

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
[CrossRef]

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

Wongkamchang, N.

A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” Int. J. Appl. Biomed. Eng. 1, 45–50 (2010).

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice-Hall, 2002).

Yazid, H.

H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
[CrossRef]

Zimmet, P.

A. F. Amos, D. J. McCarty, and P. Zimmet, “The rising global burden of diabetes and its complications: estimates and projections to the year 2010,” Diabet. Med. 14, S7–S85 (1997).
[CrossRef]

Arch. Ophthalmol. (3)

E. M. Kohner, S. J. Aldington, I. M. Stratton, S. E. Manley, R. R. Holman, D. R. Matthews, and R. C. Turner, “United Kingdom prospective diabetes study, 30: diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors,” Arch. Ophthalmol. 116, 297–303 (1998).

S. C. Lee, E. T. Lee, R. M. Kingsley, Y. Wang, D. Russell, R. Klein, and A. Warn, “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts,” Arch. Ophthalmol. 119, 509–515 (2001).
[CrossRef]

S. C. Lee, E. T. Lee, Y. Wang, R. Klein, R. M. Kingsley, and A. Warn, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol. 123, 759–764 (2005).
[CrossRef]

Br. J. Ophthalmol. (1)

C. Sinthanayothin, J. A. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[CrossRef]

Diabet. Med. (2)

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Med. 21, 84–90 (2004).
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Figures (8)

Fig. 1.
Fig. 1.

Retinal images: (a) healthy retinal image, (b) retinal image with exudates.

Fig. 2.
Fig. 2.

Flow chart of proposed system.

Fig. 3.
Fig. 3.

Preprocessing: (a) original retinal colored image, (b) preprocessing mask created using method defined in [23].

Fig. 4.
Fig. 4.

Contrast enchantment: (a) smoothing of red components with mathematical morphological closing, (b) contrast-enhanced bright regions.

Fig. 5.
Fig. 5.

Bright region detection: (a) enhanced bright regions using filter bank, (b) segmented bright regions using adaptive thresholding.

Fig. 6.
Fig. 6.

Removal of OD region: (a) segmented OD using [26], (b) candidate exudate regions after elimination of spurious OD region.

Fig. 7.
Fig. 7.

Exudate detection results for proposed method using three images from the DiaretDb0 and DiaretDB1 databases: (top) input retinal images, (bottom) detected exudates highlighted with blue color on the image.

Fig. 8.
Fig. 8.

Exudate detection results for proposed method using three images from the STARE database: (top) input retinal images, (bottom) detected exudates highlighted with blue color on the image.

Tables (3)

Tables Icon

Table 1. Performance Evaluation of Proposed Method

Tables Icon

Table 2. Performance Comparison of Exudate Segmentation

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Table 3. Performance Comparison of Exudate Segmentation for STARE Database

Equations (17)

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ϕf(sB)=min[maxf(x+b)].
g=255[Φw(ϕf)Φw(ϕfmin)][Φw(ϕfmax)Φw(ϕfmin)],
Φw(ϕf)=[1+exp(mwfσw)]1
GFB=1πrσe12[(d1σ)2+(d2σ)2](d1(cosΩ+ιsinΩ)),
γ(σ,Ω,θ)=xyg(x,y)GFB(sx,ty,σ,Ω,θ,r).
Mγ(σ,Ω)=max|γ(σ,Ω,θ)|.
chooseR1ifp(v|R1)P(R1)>p(v|R2)P(R2),otherwise chooseR2,
N(v|μ,Σ)=1(2π)m2|Σ|2exp[12(vμ)Σ1(vμ)],
p(v|Ri)=j=1κiN(v|μj,Σj)ωj,
PE(n,j)=wjN(υn|μj,Σj)i=1κN(υn|μi,Σi)ωi.
μj=1ξjn=1NTotalPE(n,j)υn,
Σj=1ξjn=1NTotalPE(n,j)(υnμj)(υnμj)T,
ωj=ξjNTotal,
sensitivity=TP(TP+FN),
specificity=TN(TN+FP),
PPV=TP(TP+FP),
accuracy=(TP+TN)(TP+TN+FP+FN),

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