Abstract

Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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2016 (1)

J. Zhang, B. Dashtbozorg, E. Bekkers, J. Pluim, R. Duits, and B. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. on Med. Imaging 35, 2631–2644 (2016).
[Crossref]

2015 (1)

J. Cheng, F. Yin, D. W. K. Wong, D. Tao, and J. Liu, “Sparse dissimilarity-constrained coding for glaucoma screening,” IEEE Trans. on Biom. Eng. 62, 1395–1403 (2015).
[Crossref]

2013 (5)

T. K. E. Trucco, A. Ruggeri, and et al., “Validating retinal fundus image analysis algorithms: issues and a proposal,” Invest Ophthalmol Vis Sci. 54(5), 3546–3559 (2013).
[Crossref] [PubMed]

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, “A sparse-group lasso,” Journal of Computational and Graphical Statistics 22, 231–245 (2013).
[Crossref]

N. Annu and J. Justin, “Automated classification of glaucoma images by wavelet energy features,” International Journal of Engineering and Technology 5, 1716–1721 (2013).

R. Ingle and P. Mishra, “Cup segmentation by gradient method for the assessment of glaucoma from retinal image,” International Journal of Engineering Trends and Technology 4, 2540–2543 (2013).

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N. M. Tan, D. Tao, C. Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE Trans. Med. Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

2012 (1)

K. Narasimhan, K. Vijayarekha, K. A. JogiNarayana, P. SivaPrasad, and V. SatishKumar, “Glaucoma detection from fundus image using opencv,” Research Journal of Applied Sciences, Engineering and Technology 4, 5459–5463 (2012).

2011 (3)

M. Mishra, M. K. Nath, and S. Dandapat, “Glaucoma detection from color fundus images,” International Journal of Computer & Communication Technology 2, 7–10 (2011).

K. Narasimhan and K. Vijayarekha, “An efficient automated system for glaucoma detection using fundus image,” Journal of Theoretical and Applied Information Technology 33, 104–110 (2011).

G. D. Joshi, J. Sivaswamy, and S. R. Krishnadas, “Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment,” IEEE Trans. Med. Imag. 30, 1192–1205 (2011).
[Crossref]

2010 (3)

R. Bock, J. Meier, L. G. Nyl, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14, 471–481 (2010).
[Crossref] [PubMed]

Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
[Crossref]

C. A. Lupascu, D. Tegolo, and E. Trucco, “Fabc: Retinal vessel segmentation using adaboost,” IEEE Trans. on Information Technology in Biomedicine 14, 1267–1274 (2010).
[Crossref] [PubMed]

2009 (1)

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
[Crossref]

2008 (3)

S. Y. Shen, T. Y. Wong, P. J. Foster, J. L. Loo, M. Rosman, S. C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: the singapore malay eye study,” Invest. Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008).
[Crossref] [PubMed]

M. C. Leske, S. Y. Wu, A. Hennis, R. Honkanen, and B. Nemesure, “Risk factors for indident open-angle glaucoma: the barbados eye studies,” Ophthalmology 115, 85–93 (2008).
[Crossref]

E. J. Carmona, M. Rincón, J. García-Feijoó, and J. M. M. de-la Casa, “dentification of the optic nerve head with genetic algorithms,” Artificial Intelligence in Medicine 43(3), 243–259 (2008).
[Crossref] [PubMed]

2007 (3)

A. Bertozzi, S. Esedoglu, and A. Gillette, “Inpainting of binary images using the cahn-hilliard equation,” IEEE Trans. on Image Processing 16(1), 285–291 (2007).
[Crossref]

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. Kuan, “Optic disk feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recognition 40, 2063–2076 (2007).
[Crossref]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
[Crossref]

2006 (3)

H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol. 90(3), 262–267 (2006).
[Crossref] [PubMed]

N. Harizman, C. Oliveira, A. Chiang, C. Tello, M. Marmor, R. Ritch, and J. Liebmann, “The isnt rule and differentiation of normal from glaucomatous eyes,” Arch Ophthalmol. 124, 1579–1583 (2006).
[PubMed]

M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B 68, 49–67 (2006).
[Crossref]

2004 (1)

J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. on Med. Imaging 23, 501–509 (2004).
[Crossref]

2003 (1)

T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” International Symposium on Circuits and Systems 5, 21–24 (2003).

2002 (2)

J. Shen and T. F. Chan, “Mathematical models for local nontexture inpaintings,” SIAM Journal on Applied Mathematics 62(3), 1019–1043 (2002).
[Crossref]

S. Esedoglu and J. Shen, “Digital inpainting based on the mumford-shah-euler image model,” European Journal of Applied Mathematics 13(04), 353–370 (2002).
[Crossref]

2000 (1)

S. Roweis and L. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290(5500), 2323–2326 (2000).
[Crossref] [PubMed]

1999 (1)

D. Michael and O. D. Hancox, “Optic disc size, an important consideration in the glaucoma evaluation,” Clinical Eye and Vision Care 11, 59–62 (1999).
[Crossref]

1998 (1)

V. Caselles, J. M. Morel, and C. Sbert, “An axiomatic approach to image interpolation,” IEEE Trans. on Image Processing 7(3), 376–386 (1998).
[Crossref]

1996 (1)

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381(6583), 607–609 (1996).
[Crossref] [PubMed]

1993 (1)

T. Damms and F. Dannheim, “Sensitivity and specificity of optic disc parameters in chronic glaucoma,” Invest. Ophth. Vis. Sci. 34, 2246–2250 (1993).

1988 (1)

E. R. Delong, D. M. Delong, and D. L. Clarke-Pearson, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 44, 837–845 (1988).
[Crossref] [PubMed]

Abramoff, M. D.

J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. on Med. Imaging 23, 501–509 (2004).
[Crossref]

Abràmoff, M. D.

Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
[Crossref]

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
[Crossref]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
[Crossref]

Alayon, S.

F. Fumero, S. Alayon, J. L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez, “Rim-one: An open retinal image database for optic nerve evaluation,” Int. Symp. on Computer-Based Medical Systems (CBMS) pp. 1–6 (2011).

Alward, W. L. M.

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
[Crossref]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
[Crossref]

Annu, N.

N. Annu and J. Justin, “Automated classification of glaucoma images by wavelet energy features,” International Journal of Engineering and Technology 5, 1716–1721 (2013).

Aung, T.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N. M. Tan, D. Tao, C. Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE Trans. Med. Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

S. Y. Shen, T. Y. Wong, P. J. Foster, J. L. Loo, M. Rosman, S. C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: the singapore malay eye study,” Invest. Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008).
[Crossref] [PubMed]

F. Yin, J. Liu, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis,” IEEE Int. Symp. on Computer-Based Medical Systems pp. 1–6 (2012).

J. Cheng, J. Liu, D. W. K. Wong, F. Yin, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automatic optic disc segmentation with peripapillary atrophy elimination,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 6624–6627 (2011).

J. Cheng, J. Liu, F. Yin, B. H. Lee, D. W. K. Wong, T. Aung, C. Y. Cheng, and T. Y. Wong, “Self-assessment for optic disc segmentation,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5861–5864 (2013).

Baskaran, M.

J. Cheng, J. Liu, D. W. K. Wong, F. Yin, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automatic optic disc segmentation with peripapillary atrophy elimination,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 6624–6627 (2011).

F. Yin, J. Liu, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis,” IEEE Int. Symp. on Computer-Based Medical Systems pp. 1–6 (2012).

Bekkers, E.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. Pluim, R. Duits, and B. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. on Med. Imaging 35, 2631–2644 (2016).
[Crossref]

Bertozzi, A.

A. Bertozzi, S. Esedoglu, and A. Gillette, “Inpainting of binary images using the cahn-hilliard equation,” IEEE Trans. on Image Processing 16(1), 285–291 (2007).
[Crossref]

Bock, R.

R. Bock, J. Meier, L. G. Nyl, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14, 471–481 (2010).
[Crossref] [PubMed]

J. Meier, R. Bock, G. Michelson, L. G. Nyl, and J. Hornegger, “Effects of preprocessing eye fundus images on appearance based glaucoma classification,” Proc. CAIP pp. 165–172 (2007).

Broman, A. T.

H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol. 90(3), 262–267 (2006).
[Crossref] [PubMed]

Carmona, E. J.

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J. Cheng, J. Liu, D. W. K. Wong, F. Yin, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automatic optic disc segmentation with peripapillary atrophy elimination,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 6624–6627 (2011).

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J. Cheng, J. Liu, D. W. K. Wong, F. Yin, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automatic optic disc segmentation with peripapillary atrophy elimination,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 6624–6627 (2011).

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S. Y. Shen, T. Y. Wong, P. J. Foster, J. L. Loo, M. Rosman, S. C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: the singapore malay eye study,” Invest. Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008).
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García-Feijoó, J.

E. J. Carmona, M. Rincón, J. García-Feijoó, and J. M. M. de-la Casa, “dentification of the optic nerve head with genetic algorithms,” Artificial Intelligence in Medicine 43(3), 243–259 (2008).
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Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
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M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
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Greenlee, E.

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
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M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
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D. Michael and O. D. Hancox, “Optic disc size, an important consideration in the glaucoma evaluation,” Clinical Eye and Vision Care 11, 59–62 (1999).
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N. Harizman, C. Oliveira, A. Chiang, C. Tello, M. Marmor, R. Ritch, and J. Liebmann, “The isnt rule and differentiation of normal from glaucomatous eyes,” Arch Ophthalmol. 124, 1579–1583 (2006).
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N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, “A sparse-group lasso,” Journal of Computational and Graphical Statistics 22, 231–245 (2013).
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M. C. Leske, S. Y. Wu, A. Hennis, R. Honkanen, and B. Nemesure, “Risk factors for indident open-angle glaucoma: the barbados eye studies,” Ophthalmology 115, 85–93 (2008).
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C.-Y. Ho, T.-W. Pai, H.-T. Chang, and H.-Y. Chen, “An atomatic fundus image analysis system for clinical diagnosis of glaucoma,” in Proceedings of the 5th IEEE International Conference on Complex, Intelligent and Software Intensive Systems pp. 559–564 (2011).

Honkanen, R.

M. C. Leske, S. Y. Wu, A. Hennis, R. Honkanen, and B. Nemesure, “Risk factors for indident open-angle glaucoma: the barbados eye studies,” Ophthalmology 115, 85–93 (2008).
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J. Meier, R. Bock, G. Michelson, L. G. Nyl, and J. Hornegger, “Effects of preprocessing eye fundus images on appearance based glaucoma classification,” Proc. CAIP pp. 165–172 (2007).

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Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
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M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
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J. Sivaswamy, K. R. Krishnadas, G. D. Joshi, M. Jain, Ujjwal, and T. A. Syed, “Drishti-gs: retinal image dataset for optic nerve head segmentation,” IEEE Int. Sym. Bio. Imag. pp. 53–56 (2014).

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G. D. Joshi, J. Sivaswamy, and S. R. Krishnadas, “Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment,” IEEE Trans. Med. Imag. 30, 1192–1205 (2011).
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J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. Kuan, “Optic disk feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recognition 40, 2063–2076 (2007).
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Kwon, Y. H.

Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
[Crossref]

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
[Crossref]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of theoptic disc from stereo color photographs using physiologically plausible features,” Invest. Ophthalmol. Vis. Sci. 48, 1665–1673 (2007).
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D. W. K. Wong, J. Liu, J. H. La, and et al., “Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2266–2269 (2008).

Lee, B. H.

Z. Zhang, F. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, and T. Y. Wong, “Origa-light: An online retinal fundus image database for glaucoma analysis and research,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 3065–3068 (2010).

J. Cheng, J. Liu, F. Yin, B. H. Lee, D. W. K. Wong, T. Aung, C. Y. Cheng, and T. Y. Wong, “Self-assessment for optic disc segmentation,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5861–5864 (2013).

Lee, K.

Z. Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated segmentation of neural canal opening and optic cup in 3-d spectral optical coherence tomography volumes of the optic nerve head,” Inv Ophthalmol Vis Sci. 51, 5708–5717 (2010).
[Crossref]

M. D. Abràmoff, K. Lee, M. Niemeijer, W. L. M. Alward, E. Greenlee, M. K. Garvin, M. Sonka, and Y. H. Kwon, “Automated segmentation of the cup and rim from spectral domain oct of the optic nerve head,” Inv Ophthalmol Vis Sci. 50, 5778–5784 (2009).
[Crossref]

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M. C. Leske, S. Y. Wu, A. Hennis, R. Honkanen, and B. Nemesure, “Risk factors for indident open-angle glaucoma: the barbados eye studies,” Ophthalmology 115, 85–93 (2008).
[Crossref]

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A. Li, J. Cheng, D. W. K. Wong, and J. Liu, “Integrating holistic and local deep features for glaucoma classification,” IEEE Engineering in Medicine and Biology Society (EMBC) pp. 1328–1331 (2016).

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D. W. K. Wong, J. Liu, J. Lim, H. Li, and T. Wong, “Automated detection of kinks from blood vessels for optic cup segmentation in retinal images,” Proceedings of the SPIE, Volume 7260, id. 72601J (2009).

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N. Harizman, C. Oliveira, A. Chiang, C. Tello, M. Marmor, R. Ritch, and J. Liebmann, “The isnt rule and differentiation of normal from glaucomatous eyes,” Arch Ophthalmol. 124, 1579–1583 (2006).
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H. Fu, Y. Xu, S. Lin, D. W. Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” In: S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, eds.MICCAI 2016, Part II, LNCS 9901 (2016).

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J. Cheng, F. Yin, D. W. K. Wong, D. Tao, and J. Liu, “Sparse dissimilarity-constrained coding for glaucoma screening,” IEEE Trans. on Biom. Eng. 62, 1395–1403 (2015).
[Crossref]

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N. M. Tan, D. Tao, C. Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE Trans. Med. Imaging 32, 1019–1032 (2013).
[Crossref] [PubMed]

Y. Xu, S. Lin, D. W. K. Wong, J. Liu, and D. Xu, “Efficient reconstruction-based optic cup localization for glaucoma screening,” In: K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Nassir, eds. MICCAI 2013, Part III, LNCS 8151, 445–452 (2013).

D. W. K. Wong, J. Liu, J. Lim, H. Li, and T. Wong, “Automated detection of kinks from blood vessels for optic cup segmentation in retinal images,” Proceedings of the SPIE, Volume 7260, id. 72601J (2009).

D. W. K. Wong, J. Liu, J. H. La, and et al., “Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2266–2269 (2008).

F. Yin, J. Liu, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis,” IEEE Int. Symp. on Computer-Based Medical Systems pp. 1–6 (2012).

A. Li, J. Cheng, D. W. K. Wong, and J. Liu, “Integrating holistic and local deep features for glaucoma classification,” IEEE Engineering in Medicine and Biology Society (EMBC) pp. 1328–1331 (2016).

X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, and J. Liu, “Glaucoma detection based on deep convolutional neural network,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 715–718 (2015).

J. Liu, S. Ji, and J. Ye, SLEP: Sparse Learning with Efficient Projection, Arizona State University (2009). http://www.public.asu.edu/∼jye02/Software/SLEP .

H. Fu, Y. Xu, S. Lin, D. W. Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” In: S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, eds.MICCAI 2016, Part II, LNCS 9901 (2016).

J. Cheng, J. Liu, F. Yin, B. H. Lee, D. W. K. Wong, T. Aung, C. Y. Cheng, and T. Y. Wong, “Self-assessment for optic disc segmentation,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5861–5864 (2013).

J. Cheng, J. Liu, D. W. K. Wong, F. Yin, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Automatic optic disc segmentation with peripapillary atrophy elimination,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 6624–6627 (2011).

Z. Zhang, F. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, and T. Y. Wong, “Origa-light: An online retinal fundus image database for glaucoma analysis and research,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 3065–3068 (2010).

Loo, J. L.

S. Y. Shen, T. Y. Wong, P. J. Foster, J. L. Loo, M. Rosman, S. C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: the singapore malay eye study,” Invest. Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008).
[Crossref] [PubMed]

Loon, S. C.

S. Y. Shen, T. Y. Wong, P. J. Foster, J. L. Loo, M. Rosman, S. C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: the singapore malay eye study,” Invest. Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008).
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Lupascu, C. A.

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Marmor, M.

N. Harizman, C. Oliveira, A. Chiang, C. Tello, M. Marmor, R. Ritch, and J. Liebmann, “The isnt rule and differentiation of normal from glaucomatous eyes,” Arch Ophthalmol. 124, 1579–1583 (2006).
[PubMed]

Meier, J.

R. Bock, J. Meier, L. G. Nyl, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14, 471–481 (2010).
[Crossref] [PubMed]

J. Meier, R. Bock, G. Michelson, L. G. Nyl, and J. Hornegger, “Effects of preprocessing eye fundus images on appearance based glaucoma classification,” Proc. CAIP pp. 165–172 (2007).

Michael, D.

D. Michael and O. D. Hancox, “Optic disc size, an important consideration in the glaucoma evaluation,” Clinical Eye and Vision Care 11, 59–62 (1999).
[Crossref]

Michelson, G.

R. Bock, J. Meier, L. G. Nyl, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14, 471–481 (2010).
[Crossref] [PubMed]

J. Meier, R. Bock, G. Michelson, L. G. Nyl, and J. Hornegger, “Effects of preprocessing eye fundus images on appearance based glaucoma classification,” Proc. CAIP pp. 165–172 (2007).

Mishra, M.

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Zheng, C.

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. Kuan, “Optic disk feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recognition 40, 2063–2076 (2007).
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Artificial Intelligence in Medicine (1)

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Figures (12)

Fig. 1
Fig. 1 Sample Images: two images with obvious cup boundary and two disc images with unclear cup boundary.
Fig. 2
Fig. 2 Illustration of reconstruction based CDR computation
Fig. 3
Fig. 3 Flowchart of the processing
Fig. 4
Fig. 4 Effects of vessel impainting.
Fig. 5
Fig. 5 Linear mapping for illumination correction
Fig. 6
Fig. 6 Performance changes as λ1 changes
Fig. 7
Fig. 7 Performance changes as λ2 changes
Fig. 8
Fig. 8 Performance changes as λ3 changes
Fig. 9
Fig. 9 Performance changes as the number of reference images n changes.
Fig. 10
Fig. 10 Scatter plot and Bland-Altman plot between manual and automated CDR by the proposed method.
Fig. 11
Fig. 11 Comparison of ROC by different methods.
Fig. 12
Fig. 12 Comparison of ROC curves from automated and manually segmented discs

Tables (3)

Tables Icon

Table 1 Performance by various methods.

Tables Icon

Table 2 Performance from different discs.

Tables Icon

Table 3 p values of ROCs based on automatically segmented disc in comparison with manually segmented disc.

Equations (9)

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r ^ = 1 1 n T w r T w ,
G i = { x j | t i < x j t i + 1 } ,
y X w 2 + λ 1 s w 2 + λ 2 w 1 + λ 3 i = 1 N ψ i x G i 2 ,
y X w 2 + λ 1 s w 2 + λ 2 w 1 + λ 3 i = 1 N ψ i x G i 2 = y X w 2 + λ 1 S w 2 + λ 2 w 1 + λ 3 i = 1 N ψ i x G i 2 = [ y 0 ] [ X λ 1 S ] w 2 + λ 2 w 1 + λ 3 i = 1 N ψ i x G i 2 = y ^ X ^ w 2 + λ 2 w 1 + λ 3 i = 1 N ψ i x G i 2
x b ( i , j ) = ( j j m a x / 2 ) j m a x p ( x ¯ l x ¯ r ) + x ^ ( i , j ) ,
U ( j , k ) = a 1 j 2 + a 2 j + a 3 k 2 + a 5 k + a 5 = ( q ) T a
u = Q a
a = ( Q T Q ) 1 Q T x
s i = ( a 1 , x i a 1 , y ) 2 + ( a 3 , x i a 3 , y ) 2 ,

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