Abstract

Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.

© 2017 Optical Society of America

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References

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    [Crossref]

2017 (1)

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Computerized Medical Imaging & Graphics 55, 28–41 (2017).
[Crossref]

2016 (1)

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

2015 (1)

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Computers in Biology and Medicine 56, 1–12 (2015).
[Crossref]

2014 (2)

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging 1, 024001 (2014).
[Crossref]

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

2013 (1)

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 (3)

L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. on Pat. Anal. and Machine Intell. 34, 465–479 (2012).
[Crossref]

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Y. Zheng, M. H. Hijazi, and F. Coenen, “Automated disease/no disease grading of age-related macular degeneration by an image mining approach,” Investigative Ophthalmology & Visual Science 53(13), 8310–8318 (2012).
[Crossref]

2011 (1)

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 (2)

A. Aquino, M. 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]

S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010).
[Crossref]

2007 (2)

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]

2005 (1)

Y. Nesterov, “Smoothing minimizaton of non-smooth functions,” Mathematical Programming 103, 127–152 (2005).
[Crossref]

2004 (1)

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Abràmoff, M. D.

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]

Achanta, R.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Alward, W. L. M.

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]

Aquino, A.

A. Aquino, M. 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]

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]

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, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Ballerini, L.

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging 1, 024001 (2014).
[Crossref]

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, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Basu, A.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Bu, W.

B. Dai, X. Wu, and W. Bu, “Optic disc segmentation based on variational model with multiple energies,” Pattern Recognition64 (2017).
[Crossref]

Buhmann, J. M.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Computerized Medical Imaging & Graphics 55, 28–41 (2017).
[Crossref]

Burget, R.

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

Campilho, A.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Computers in Biology and Medicine 56, 1–12 (2015).
[Crossref]

Cazuguel, G.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Charton, B.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Cheng, B.

B. Cheng, D. Zhang, and D. Shen, “Domain transfer learning for mci conversion prediction,” In: N. Ayache, H. Delingette, P. Golland, and K. Mori (eds.) MICCAI 2012, Part I. LNCS7510, 82–90 (2012).

Cheng, C. Y.

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]

Cheng, J.

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]

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).

Cherian, N. S.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

Cheung, C.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

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).

Chutatape, O.

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]

Cochener, B.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Coenen, F.

Y. Zheng, M. H. Hijazi, and F. Coenen, “Automated disease/no disease grading of age-related macular degeneration by an image mining approach,” Investigative Ophthalmology & Visual Science 53(13), 8310–8318 (2012).
[Crossref]

Dai, B.

B. Dai, X. Wu, and W. Bu, “Optic disc segmentation based on variational model with multiple energies,” Pattern Recognition64 (2017).
[Crossref]

Dashtbozorg, B.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Computers in Biology and Medicine 56, 1–12 (2015).
[Crossref]

Decencière, E.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Duan, L.

L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. on Pat. Anal. and Machine Intell. 34, 465–479 (2012).
[Crossref]

Dutta, M. K.

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

Erginay, A.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Fingert, J. H.

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]

Fletcher, E.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Fua, P.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Gain, P.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Gegundez-Arias, M.

A. Aquino, M. 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]

Geng, B.

S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010).
[Crossref]

Giachetti, A.

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging 1, 024001 (2014).
[Crossref]

Greenlee, E. C.

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]

Hijazi, M. H.

Y. Zheng, M. H. Hijazi, and F. Coenen, “Automated disease/no disease grading of age-related macular degeneration by an image mining approach,” Investigative Ophthalmology & Visual Science 53(13), 8310–8318 (2012).
[Crossref]

Hunter, A.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Joshi, G. D.

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]

Kennedy, L.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Kim, C. Y.

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]

Klein, J.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Krishnadas, S. R.

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]

Kuan, P.

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]

Kwok, J. T.

S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality reduction,” Proc. 23rd Nat’l Conf. Artificial Intelligence pp. 677–682 (2008).

Kwon, Y. H.

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]

Lee, B. H.

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

Li, H.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

Lim, J. H.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

Liu, J.

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]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

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).

Lowell, J.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Lu, S.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

Lucchi, A.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Mahapatra, D.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Computerized Medical Imaging & Graphics 55, 28–41 (2017).
[Crossref]

Marin, D.

A. Aquino, M. 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]

Massin, P.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Mendonça, A. M.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Computers in Biology and Medicine 56, 1–12 (2015).
[Crossref]

Nesterov, Y.

Y. Nesterov, “Smoothing minimizaton of non-smooth functions,” Mathematical Programming 103, 127–152 (2005).
[Crossref]

Ong, S. H.

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Ordonez, R.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Pan, S. J.

S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality reduction,” Proc. 23rd Nat’l Conf. Artificial Intelligence pp. 677–682 (2008).

Parthasarathi, V. U. M.

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

Rangayyan, R. M.

X. Zhu and R. M. Rangayyan, “Detection of the optic disc in images of the retina using the hough transform,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 3546–3549 (2008).

Ryder, R.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Shaji, A.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Shen, D.

B. Cheng, D. Zhang, and D. Shen, “Domain transfer learning for mci conversion prediction,” In: N. Ayache, H. Delingette, P. Golland, and K. Mori (eds.) MICCAI 2012, Part I. LNCS7510, 82–90 (2012).

Shuba, L.

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]

Si, S.

S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010).
[Crossref]

Singh, A.

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

Sivaswamy, J.

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]

Smith, K.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Steel, D.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

Sun, Y.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Sung, E.

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]

Susstrunk, S.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Tan, N. M.

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]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Tao, D.

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. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010).
[Crossref]

T. Zhou, D. Tao, and X. Wu, “Nesvm: a fast gradient method for support vector machines,” IEEE Int. Conf. on Data Mining (ICDM) pp. 679–688 (2010).

Trone, C.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Trucco, E.

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging 1, 024001 (2014).
[Crossref]

Tsang, I. W.

L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. on Pat. Anal. and Machine Intell. 34, 465–479 (2012).
[Crossref]

Wong, D. W. K.

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]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

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, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

Wong, T. Y.

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]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

F. Yin, J. Liu, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

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).

Wu, X.

B. Dai, X. Wu, and W. Bu, “Optic disc segmentation based on variational model with multiple energies,” Pattern Recognition64 (2017).
[Crossref]

T. Zhou, D. Tao, and X. Wu, “Nesvm: a fast gradient method for support vector machines,” IEEE Int. Conf. on Data Mining (ICDM) pp. 679–688 (2010).

Xu, D.

L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. on Pat. Anal. and Machine Intell. 34, 465–479 (2012).
[Crossref]

Xu, J.

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]

Xu, Y.

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]

Yang, Q.

S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality reduction,” Proc. 23rd Nat’l Conf. Artificial Intelligence pp. 677–682 (2008).

Yin, F.

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]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

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, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

Zhang, D.

B. Cheng, D. Zhang, and D. Shen, “Domain transfer learning for mci conversion prediction,” In: N. Ayache, H. Delingette, P. Golland, and K. Mori (eds.) MICCAI 2012, Part I. LNCS7510, 82–90 (2012).

Zhang, X.

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Zhang, Z.

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

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).
[Crossref]

Zheng, Y.

Y. Zheng, M. H. Hijazi, and F. Coenen, “Automated disease/no disease grading of age-related macular degeneration by an image mining approach,” Investigative Ophthalmology & Visual Science 53(13), 8310–8318 (2012).
[Crossref]

Zhou, T.

T. Zhou, D. Tao, and X. Wu, “Nesvm: a fast gradient method for support vector machines,” IEEE Int. Conf. on Data Mining (ICDM) pp. 679–688 (2010).

Zhu, X.

X. Zhu and R. M. Rangayyan, “Detection of the optic disc in images of the retina using the hough transform,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 3546–3549 (2008).

Zilly, J.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Computerized Medical Imaging & Graphics 55, 28–41 (2017).
[Crossref]

Computer Methods & Programs in Biomedicine (1)

A. Singh, M. K. Dutta, V. U. M. Parthasarathi, and R. Burget, “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,” Computer Methods & Programs in Biomedicine 124, 108–120 (2016).
[Crossref]

Computerized Medical Imaging & Graphics (1)

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Computerized Medical Imaging & Graphics 55, 28–41 (2017).
[Crossref]

Computers in Biology and Medicine (1)

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Computers in Biology and Medicine 56, 1–12 (2015).
[Crossref]

IEEE Trans. Knowl. Data Eng. (1)

S. Si, D. Tao, and B. Geng, “Bregman divergence-based regularization for transfer subspace learning,” IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010).
[Crossref]

IEEE Trans. Med. Imag. (3)

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]

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Trans. Med. Imag. 23, 256–264 (2004).
[Crossref]

A. Aquino, M. 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]

IEEE Trans. Med. Imaging (1)

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]

IEEE Trans. on Pat. Anal. and Machine Intell. (1)

L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. on Pat. Anal. and Machine Intell. 34, 465–479 (2012).
[Crossref]

IEEE Trans. Pattern Anal. and Mach. Intell. (1)

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Pattern Anal. and Mach. Intell. 34, 2274–2281 (2012).
[Crossref]

Image Analysis & Stereology (1)

E. Decencière, X. Zhang, G. Cazuguel, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, and J. Klein, “Feedback on a publicly distributed image database: The messidor database,” Image Analysis & Stereology 33(3), 231–234 (2014).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (1)

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]

Investigative Ophthalmology & Visual Science (1)

Y. Zheng, M. H. Hijazi, and F. Coenen, “Automated disease/no disease grading of age-related macular degeneration by an image mining approach,” Investigative Ophthalmology & Visual Science 53(13), 8310–8318 (2012).
[Crossref]

Journal of Medical Imaging (1)

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging 1, 024001 (2014).
[Crossref]

Mathematical Programming (1)

Y. Nesterov, “Smoothing minimizaton of non-smooth functions,” Mathematical Programming 103, 127–152 (2005).
[Crossref]

Pattern Recognition (1)

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]

Other (11)

Z. Zhang, J. Liu, N. S. Cherian, Y. Sun, J. H. Lim, D. W. K. Wong, N. M. Tan, S. Lu, H. Li, and T. Y. Wong, “Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 1441–1444 (2009).

X. Zhu and R. M. Rangayyan, “Detection of the optic disc in images of the retina using the hough transform,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 3546–3549 (2008).

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, S. H. Ong, Y. Sun, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, T. Aung, and T. Y. Wong, “Model-based optic nerve head segmentation on retinal fundus images,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 2626–2629 (2011).

B. Dai, X. Wu, and W. Bu, “Optic disc segmentation based on variational model with multiple energies,” Pattern Recognition64 (2017).
[Crossref]

D. W. K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, and T. Y. Wong, “Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs,” Int. Conf. of IEEE Eng. in Med. and Bio. Soc. pp. 5355–5358 (2010).

S. J. Pan, J. T. Kwok, and Q. Yang, “Transfer learning via dimensionality reduction,” Proc. 23rd Nat’l Conf. Artificial Intelligence pp. 677–682 (2008).

M. T.-V. P. F. Download Images Section, MESSIDOR: Digital Retinal Images. [Online]. Available: http://messidor.crihan.fr/download-en.php .

S. U. H. O. Expert system for early automated detection of DR by analysis of digital retinal images project website, Huelva. Available: http://www.uhu.es/retinopathy .

T. Zhou, D. Tao, and X. Wu, “Nesvm: a fast gradient method for support vector machines,” IEEE Int. Conf. on Data Mining (ICDM) pp. 679–688 (2010).

B. Cheng, D. Zhang, and D. Shen, “Domain transfer learning for mci conversion prediction,” In: N. Ayache, H. Delingette, P. Golland, and K. Mori (eds.) MICCAI 2012, Part I. LNCS7510, 82–90 (2012).

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Figures (4)

Fig. 1
Fig. 1 Typical retinal image: The region enclosed by the green line is the optic disc.
Fig. 2
Fig. 2 Flow chart of learning based disc segmentation.
Fig. 3
Fig. 3 Sample Results: (a) original; (b) the manual disc; (c) initial disc by SVM; (d) initial disc by QDSVM; (e) after ASM from (c); (f) after ASM from (d).
Fig. 4
Fig. 4 Disc segmentation with different number of labelled images in target domain.

Tables (2)

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Table 1 Classification error Ps and mean overlapping error Ē by different methods

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Table 2 Comparison with other methods on MESSIDOR

Equations (17)

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p ¯ = 1 N k = 1 N p k ,
S = 1 N 1 k = 1 N ( p k p ¯ ) ( p k p ¯ ) T ,
p = p ¯ + ϕ b ,
f ( g k ) = ( g k g ¯ ) S g 1 ( g k g ¯ )
min w { 1 2 w 2 + C i = 1 n ( z i Y i , w ) } ,
min P , w { 1 2 w 2 + C i = 1 n ( z i Y i , w ) + λ D P ( Q l Q u ) } ,
D P ( Q l Q u ) = ( q l ( y ) q u ( y ) ) 2 d y = ( q l ( y ) 2 2 q l ( y ) q u ( y ) + q u ( y ) 2 ) d y
D P ( Q l Q u ) = ( 1 l i = 1 l G Σ 1 ( y y i ) ) 2 d y + ( 1 u j = l + 1 l + u G Σ 2 ( y y j ) ) 2 d y ( 2 l u i = 1 l j = l + 1 l + u G Σ 1 ( y y i ) G Σ 2 ( y y j ) ) d y
D P ( Q l Q u ) = 1 l 2 s = 1 l t = 1 l G Σ 11 ( y t y s )
+ 1 u 2 s = l + 1 l + u t = l + 1 l + u G Σ 2 ( y t y s ) 2 l u s = 1 l t = l + 1 l + u G Σ 12 ( y t y s )
u = max u i u i ( 1 z i X i P w ) μ 2 X i u i 2
u i = median { 1 z i X i P w μ X i , 0 , 1 }
F u ( P , w ) = 1 2 w 2 + C i = 1 n u + λ D P ( Q l Q u )
P 1 k = P k 1 L μ F μ ( P k , w )
P 2 k = P k σ 2 L μ i = 0 k i + 1 2 F μ ( P i , w )
P k + 1 = k + 1 k + 3 P 1 k + 2 k + 3 P 2 k
E = 1 Area ( S M ) Area ( S M )

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