Abstract

Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

© 2014 Optical Society of America

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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]

2013 (8)

L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
[CrossRef] [PubMed]

V. Kajić, M. Esmaeelpour, C. Glittenberg, M. F. Kraus, J. Honegger, R. Othara, S. Binder, J. G. Fujimoto, W. Drexler, “Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data,” Biomed. Opt. Express 4(1), 134–150 (2013).
[CrossRef] [PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[CrossRef] [PubMed]

J. Tian, P. Marziliano, M. Baskaran, T. A. Tun, T. Aung, “Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images,” Biomed. Opt. Express 4(3), 397–411 (2013).
[CrossRef] [PubMed]

B. J. Antony, M. D. Abràmoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, M. K. Garvin, “A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[CrossRef]

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

E. S. Lobanova, S. Finkelstein, N. P. Skiba, V. Y. Arshavsky, “Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration,” Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986–9991 (2013).
[CrossRef] [PubMed]

2012 (5)

2011 (5)

A. Yazdanpanah, G. Hamarneh, B. R. Smith, M. V. Sarunic, “Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express 2(10), 2871–2887 (2011).
[CrossRef] [PubMed]

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

K. A. Vermeer, J. van der Schoot, H. G. Lemij, J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express 2(6), 1743–1756 (2011).
[CrossRef] [PubMed]

2010 (3)

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
[CrossRef] [PubMed]

M. D. Abramoff, M. K. Garvin, M. Sonka, “Retinal Imaging and Image Analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[CrossRef]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

2009 (1)

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

2008 (2)

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
[CrossRef] [PubMed]

2007 (6)

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[CrossRef] [PubMed]

D. Bizios, A. Heijl, B. Bengtsson, “Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms,” J. Glaucoma 16(1), 20–28 (2007).
[CrossRef] [PubMed]

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

2006 (1)

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

2005 (2)

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

1999 (1)

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

1998 (1)

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Abramoff, M. D.

M. D. Abramoff, M. K. Garvin, M. Sonka, “Retinal Imaging and Image Analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[CrossRef]

Abràmoff, M. D.

Antony, B. J.

Arshavsky, V. Y.

E. S. Lobanova, S. Finkelstein, N. P. Skiba, V. Y. Arshavsky, “Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration,” Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986–9991 (2013).
[CrossRef] [PubMed]

Aung, T.

Balaiya, S.

L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
[CrossRef] [PubMed]

Barhoum, R.

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
[CrossRef] [PubMed]

Baskaran, M.

Beaton, S.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Bengtsson, B.

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
[CrossRef] [PubMed]

D. Bizios, A. Heijl, B. Bengtsson, “Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms,” J. Glaucoma 16(1), 20–28 (2007).
[CrossRef] [PubMed]

Binder, S.

Bizios, D.

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
[CrossRef] [PubMed]

D. Bizios, A. Heijl, B. Bengtsson, “Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms,” J. Glaucoma 16(1), 20–28 (2007).
[CrossRef] [PubMed]

Borsdorf, A.

Bowd, C.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Burgansky-Eliash, Z.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

Bursell, S.-E.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

Cabrera, M. T.

Cabrera DeBuc, D.

J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, G. Somfai, “Layer extraction in rodent retinal images acquired by optical coherence tomography,” Mach. Vis. Appl. 23(6), 1129–1139 (2012).
[CrossRef]

Calabresi, P. A.

Calvert, P. D.

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Cameron, D. A.

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Carass, A.

Carvalho, M.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

Chalam, K. V.

L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
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Chetverikov, D.

J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, G. Somfai, “Layer extraction in rodent retinal images acquired by optical coherence tomography,” Mach. Vis. Appl. 23(6), 1129–1139 (2012).
[CrossRef]

Chiu, S. J.

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

Choi, S.

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

Choi, S. S.

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

Chu, T.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

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V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
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R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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de la Rosa, E. J.

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
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L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
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Duan, Y.

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
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V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
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M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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Esmaeelpour, M.

Estrada, R.

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L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
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Farsiu, S.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
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R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, S. Farsiu, “Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO),” Biomed. Opt. Express 3(2), 327–339 (2012).
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L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
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R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express 2(10), 2871–2887 (2011).
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F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

Ferencz, M.

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

Ferguson, L. R.

L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
[CrossRef] [PubMed]

Fernandez-Sanchez, L.

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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Feuer, W. J.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
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E. S. Lobanova, S. Finkelstein, N. P. Skiba, V. Y. Arshavsky, “Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration,” Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986–9991 (2013).
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K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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Fujimoto, J. G.

V. Kajić, M. Esmaeelpour, C. Glittenberg, M. F. Kraus, J. Honegger, R. Othara, S. Binder, J. G. Fujimoto, W. Drexler, “Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data,” Biomed. Opt. Express 4(1), 134–150 (2013).
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V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
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R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
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A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

Gao, W.

J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, G. Somfai, “Layer extraction in rodent retinal images acquired by optical coherence tomography,” Mach. Vis. Appl. 23(6), 1129–1139 (2012).
[CrossRef]

Garvin, M. K.

Germain, F.

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
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Glittenberg, C.

Glymour, C.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
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C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
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G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
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M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

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G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

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L. R. Ferguson, J. M. Dominguez, S. Balaiya, S. Grover, K. V. Chalam, “Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT,” PLoS ONE 8(6), e67265 (2013).
[CrossRef] [PubMed]

Hackam, A.

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

Hamann, B.

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

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A. Yazdanpanah, G. Hamarneh, B. R. Smith, M. V. Sarunic, “Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
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C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

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Hauser, M.

Hearst, M. A.

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Heijl, A.

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
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D. Bizios, A. Heijl, B. Bengtsson, “Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms,” J. Glaucoma 16(1), 20–28 (2007).
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O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
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Honegger, J.

Hornegger, J.

Hougaard, J. L.

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
[CrossRef] [PubMed]

Ishikawa, H.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Izatt, J. A.

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[CrossRef] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

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J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

Jeong, W.

Jiao, S.

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

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M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
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O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

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Kardon, R.

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
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O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
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Kuo, A. N.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
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Kwon, Y. H.

Lang, A.

LaRocca, F.

Lee, J. Y.

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

Lee, T.-W.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Lem, J.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Lemij, H. G.

Li, S.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[CrossRef] [PubMed]

Li, X. T.

Lobanova, E. S.

E. S. Lobanova, S. Finkelstein, N. P. Skiba, V. Y. Arshavsky, “Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration,” Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986–9991 (2013).
[CrossRef] [PubMed]

Lujan, B. J.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Makino, C. L.

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Manns, F.

O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

Mardin, C. Y.

Martínez-Navarrete, G.

R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, N. Cuenca, “Functional and structural modifications during retinal degeneration in the rd10 mouse,” Neuroscience 155(3), 698–713 (2008).
[CrossRef] [PubMed]

Marziliano, P.

Mayer, M. A.

McNabb, R. P.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

Medeiros, F. A.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Molnár, J.

J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, G. Somfai, “Layer extraction in rodent retinal images acquired by optical coherence tomography,” Mach. Vis. Appl. 23(6), 1129–1139 (2012).
[CrossRef]

Nicholas, P.

Nicolò, M.

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Nie, Q.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[CrossRef] [PubMed]

Noecker, R. J.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

O’Connell, R. V.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[CrossRef] [PubMed]

Osman, E.

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Othara, R.

Parel, J.-M.

O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

Platt, J.

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Prince, J. L.

Puliafito, C. A.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

Ramsey, J. D.

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

Ranganathan, S.

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

Rosenfeld, P. J.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Ruggeri, M.

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

Sample, P. A.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Sarunic, M. V.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, M. V. Sarunic, “Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[CrossRef] [PubMed]

Scholkopf, B.

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Appl. 13, 18–28 (1998).

Schuman, J. S.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

Sidman, R. L.

J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, R. L. Sidman, “Morphological, physiological, and biochemical changes in rhodopsin knockout mice,” Proc. Natl. Acad. Sci. U.S.A. 96(2), 736–741 (1999).
[CrossRef] [PubMed]

Skiba, N. P.

E. S. Lobanova, S. Finkelstein, N. P. Skiba, V. Y. Arshavsky, “Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration,” Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986–9991 (2013).
[CrossRef] [PubMed]

Smith, B. R.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, M. V. Sarunic, “Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[CrossRef] [PubMed]

Sohn, E. H.

Somfai, G.

J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, G. Somfai, “Layer extraction in rodent retinal images acquired by optical coherence tomography,” Mach. Vis. Appl. 23(6), 1129–1139 (2012).
[CrossRef]

Somfai, G. M.

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

Song, Q. H.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

Sonka, M.

M. D. Abramoff, M. K. Garvin, M. Sonka, “Retinal Imaging and Image Analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[CrossRef]

Sotirchos, E. S.

Srinivasan, P.

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

Srinivasan, V. J.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

Stein, D. M.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Tátrai, E.

D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, C. A. Puliafito, “Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software,” J. Biomed. Opt. 14(6), 064023 (2009).
[CrossRef] [PubMed]

Tavares, I. M.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Tian, J.

Tomasi, C.

Tornow, R. P.

Toth, C. A.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, S. Farsiu, “Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[CrossRef] [PubMed]

J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, G. J. Jaffe, “Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines,” Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013).
[CrossRef] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[CrossRef] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

Tun, T. A.

Uhlhorn, S. R.

O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

van der Schoot, J.

Vermeer, K. A.

Wagner, M.

Wallace, D. K.

Wang, F.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Wehbe, H.

M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, C. A. Puliafito, “In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 48(4), 1808–1814 (2007).
[CrossRef] [PubMed]

Weinreb, R. N.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Werner, J. S.

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

Wiley, D. F.

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

Will, R.

O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, F. Manns, “Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina,” Invest. Ophthalmol. Vis. Sci. 48(3), 1283–1289 (2007).
[CrossRef] [PubMed]

Winter, K. P.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, S. Farsiu, “Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[CrossRef] [PubMed]

Wojtkowski, M.

V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, J. G. Fujimoto, “Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 47(12), 5522–5528 (2006).
[CrossRef] [PubMed]

Wollstein, G.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, J. S. Schuman, “Macular Segmentation with Optical Coherence Tomography,” Invest. Ophthalmol. Vis. Sci. 46(6), 2012–2017 (2005).
[CrossRef] [PubMed]

Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, J. S. Schuman, “Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study,” Invest. Ophthalmol. Vis. Sci. 46(11), 4147–4152 (2005).
[CrossRef] [PubMed]

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, M. V. Sarunic, “Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[CrossRef] [PubMed]

Yehoshua, Z.

G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, W. J. Feuer, “Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration,” Ophthalmology 118(7), 1373–1379 (2011).
[PubMed]

Ying, H. S.

Zangwill, L. M.

C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, M. H. Goldbaum, “Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci. 49(3), 945–953 (2008).
[CrossRef] [PubMed]

Zawadzki, R. J.

A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, B. Hamann, “Segmentation of Three-dimensional Retinal Image Data,” IEEE Trans. Vis. Comput. Graph. 13(6), 1719–1726 (2007).
[CrossRef] [PubMed]

R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, J. S. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[CrossRef] [PubMed]

Acta Ophthalmol. (Copenh.) (1)

D. Bizios, A. Heijl, J. L. Hougaard, B. Bengtsson, “Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT,” Acta Ophthalmol. (Copenh.) 88(1), 44–52 (2010).
[CrossRef] [PubMed]

Biomed. Opt. Express (10)

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

K. A. Vermeer, J. van der Schoot, H. G. Lemij, J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express 2(6), 1743–1756 (2011).
[CrossRef] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[CrossRef] [PubMed]

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).
[CrossRef] [PubMed]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, S. Farsiu, “Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO),” Biomed. Opt. Express 3(2), 327–339 (2012).
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Figures (9)

Fig. 1
Fig. 1

(a) Ten targeted retinal layer boundaries in a WT mouse SD-OCT B-scan (Group A). (b) Morphological cross-section from an age-matched WT mouse retina stained with toluidine blue. Bar: 50 μm. (c) Eight targeted retinal layer boundaries in a Rho(−/−) mouse SD-OCT B-scan (Group B). (d) Morphological cross-section from an age-matched Rho(−/−) mouse retina stained with toluidine blue. Bar: 50 μm.

Fig. 2
Fig. 2

Overview of the algorithm for classifying and segmenting murine SD-OCT volumes.

Fig. 3
Fig. 3

Example rectangular region-of-interest isolated from an SD-OCT B-scan from a WT mouse, and the corresponding feature vector used for classifying SD-OCT volumes.

Fig. 4
Fig. 4

Example gradient images of the SD-OCT image in Fig. 1(a), where retinal layer boundaries are deinterlaced. (a) Dark-to-light gradient image. (b) Light-to-dark gradient image.

Fig. 5
Fig. 5

ONH segmentation. (a) SVP for ONH center estimation. (b) SVP for ONH segmentation. (c) The corresponding fitted ONH ellipse.

Fig. 6
Fig. 6

(a) Automatic segmentation of an SD-OCT B-scan from a WT mouse by Bioptigen Inc. Diver 2.0 software with inconsistent NFL-GCL segmentation in the presence of vessels. (b) The corresponding automatic segmentation by our S-GTDP method.

Fig. 7
Fig. 7

Vessel segmentation. (a) Low intensity vessels SVP. (b) High intensity vessels SVP. (c) Gabor-filtered combined SVP for vessel segmentation.

Fig. 8
Fig. 8

ONH scan separated into regions, with the hyper-reflective peak (consisting of the nerve fibers and the hyaloid artery) and flecks labeled. The right region is segmented by S-GTDP.

Fig. 9
Fig. 9

Original SD-OCT images and the same images with retinal layer boundaries automatically segmented by S-GTDP. (a) WT retina. (b) WT retina including ONH. (c) WT retinal periphery. (d) Rho(−/−) retina. (e) Rho(−/−) retina including ONH. (f) Rho(−/−) retinal periphery. (g) Retina displaying abnormal morphology in the OPL of unknown origin. (h) Retina from an Rd10 mutant mouse.

Tables (7)

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Table 1 Segmentation parameters for Group A.

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Table 2 Segmentation parameters for Group B.

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Table 3 Fraction of images in each data set correctly classified by the SVM algorithm.

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Table 4 Comparison of segmentation results for the limited number of A-scans from 99 B-scans in Group A for which Bioptigen’s software provided valid results. (STD = Standard Deviation)

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Table 5 Comparison of segmentation results for the limited number of A-scans from 93 B-scans in Group B for which Bioptigen’s software provided valid results. (STD = Standard Deviation)

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Table 6 Comparison of all A-scans from the 100 B-scans in Group A. (STD = Standard Deviation)

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Table 7 Comparison of all A-scans from the 100 B-scans in Group B. (STD = Standard Deviation)

Equations (1)

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