Abstract

We present an algorithm for layer-specific edge detection in retinal optical coherence tomography images through a structured learning algorithm to reinforce traditional graph-based retinal layer segmentation. The proposed algorithm simultaneously identifies individual layers and their corresponding edges, resulting in the computation of layer-specific edges in 1 second. These edges augment classical dynamic programming based segmentation under layer deformation, shadow artifacts noise, and without heuristics or prior knowledge. We considered Duke’s online data set containing 110 B-scans of 10 diabetic macular edema subjects with 8 retinal layers annotated by two experts for experimentation, and achieved a mean distance error of 1.38 pixels whereas that of the state-of-the-art was 1.68 pixels.

© 2016 Optical Society of America

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References

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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref]

2015 (1)

2014 (4)

P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
[Crossref] [PubMed]

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

2013 (2)

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

2012 (2)

A. Criminisi, J. Shotton, and E. Konukoglu, “Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning,” Foundations and Trends in Comp. Grap. and Vis. 7(2–3), 81–227 (2012).

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

2011 (2)

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

2010 (2)

2009 (2)

D. C. Hood, A. S. Raza, K. Y. Kay, S. F. Sandler, D. Xin, R. Ritch, and J. M. Liebmann, “A comparison of retinal nerve fiber layer (RNFL) thickness obtained with frequency and time domain optical coherence tomography (OCT),” Opt. Express 17(5), 3997–4003 (2009).
[Crossref] [PubMed]

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

2008 (1)

W. Drexler and J. G. Fujimoto, “State-of-the-art retinal optical coherence tomography,” Prog. Retin. Eye Res. 27(1), 45–88 (2008).
[Crossref] [PubMed]

2007 (1)

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

2005 (2)

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

2004 (2)

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

N. Nassif, B. Cense, B. Park, M. Pierce, S. Yun, B. Bouma, G. Tearney, T. Chen, and J. de Boer, “In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve,” Opt. Express 12(3), 367–376 (2004).
[Crossref] [PubMed]

2003 (1)

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

1964 (1)

A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem. 36(8), 1627–1639 (1964).
[Crossref]

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Ahnelt, P. K.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Allingham, M. J.

Anger, E. M.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Arshavsky, V. Y.

Baek, S.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Bischof, H.

P. Kontschieder, S. Rota Bulò, H. Bischof, and M. Pelillo, “Structured class-labels in random forests for semantic image labeling,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2011), pp. 2190–2197.

Bouma, B.

Bowd, C.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Budenz, D. L.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Carneiro, G.

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Cense, B.

Chan, A. B.

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

Chang, R. T.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Chen, T.

Chiu, S. J.

Cousins, S. W.

Cowey, A.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Criminisi, A.

A. Criminisi, J. Shotton, and E. Konukoglu, “Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning,” Foundations and Trends in Comp. Grap. and Vis. 7(2–3), 81–227 (2012).

F. Schroff, A. Criminisi, and A. Zisserman, “Object Class Segmentation using Random Forests,” in Proceedings of British Machine Vision Conference,(BMVA, 2008), pp. 1–10.

de Boer, J.

De Dzanet, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

DeBuc, D. C.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Dollár, P.

P. Dollár and C. L. Zitnick, “Structured forests for fast edge detection,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2013), pp. 1841–1848.

Drexler, W.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

W. Drexler and J. G. Fujimoto, “State-of-the-art retinal optical coherence tomography,” Prog. Retin. Eye Res. 27(1), 45–88 (2008).
[Crossref] [PubMed]

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Dufour, P. A.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Durbin, M. K.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Dustin, L.

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

Escano, M. F.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Farsiu, S.

Ferencz, M.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Feuer, W. J.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Fitzgibbon, A.

M. Prasad, A. Zisserman, A. Fitzgibbon, M. P. Kumar, and P. H. Torr, “Learning class-specific edges for object detection and segmentation,” in Proceedings of Indian conference on Computer Vision, Graphics and Image Processing, (Springer, 2006), pp. 94–105.
[Crossref]

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Fua, P.

A. Lucchi, Y. Li, K. Smith, and P. Fua, “Structured image segmentation using kernelized features,” in Proceedings of European Conference on Computer Vision, (Springer, 2012), pp. 400–413.

Fujimoto, J. G.

W. Drexler and J. G. Fujimoto, “State-of-the-art retinal optical coherence tomography,” Prog. Retin. Eye Res. 27(1), 45–88 (2008).
[Crossref] [PubMed]

Golay, M. J.

A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem. 36(8), 1627–1639 (1964).
[Crossref]

Heflin, S. J.

Hermann, B.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Hood, D. C.

Hornegger, J.

Huang, J.

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

Izatt, J. A.

Jeoung, J. W.

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

Kafieh, R.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Kamali, T.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

Kanamori, A.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Kay, K. Y.

Khwarg, S. I.

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

Kim, D. M.

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

Kim, H. K.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Kim, T. W.

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

Kim, Y. J.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Knight, O. J.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Kontschieder, P.

P. Kontschieder, S. Rota Bulò, H. Bischof, and M. Pelillo, “Structured class-labels in random forests for semantic image labeling,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2011), pp. 2190–2197.

Konukoglu, E.

A. Criminisi, J. Shotton, and E. Konukoglu, “Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning,” Foundations and Trends in Comp. Grap. and Vis. 7(2–3), 81–227 (2012).

Kotsiantis, S. B.

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” in Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, (IOS, 2007), pp. 3–24.

Kowal, J.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Kumar, A.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

Kumar, M. P.

M. Prasad, A. Zisserman, A. Fitzgibbon, M. P. Kumar, and P. H. Torr, “Learning class-specific edges for object detection and segmentation,” in Proceedings of Indian conference on Computer Vision, Graphics and Image Processing, (Springer, 2006), pp. 94–105.
[Crossref]

Lafferty, J.

J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of International Conference on Machine Learning, (ACM, 2001), pp. 282–289.

Lee, H. J.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Leitgeb, R. A.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

Li, X. T.

Li, Y.

A. Lucchi, Y. Li, K. Smith, and P. Fua, “Structured image segmentation using kernelized features,” in Proceedings of European Conference on Computer Vision, (Springer, 2012), pp. 400–413.

Liebmann, J. M.

Liu, M.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

Lucchi, A.

A. Lucchi, Y. Li, K. Smith, and P. Fua, “Structured image segmentation using kernelized features,” in Proceedings of European Conference on Computer Vision, (Springer, 2012), pp. 400–413.

Maeda, H.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Mardin, C. Y.

Mayer, M. A.

McCallum, A.

J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of International Conference on Machine Learning, (ACM, 2001), pp. 282–289.

Medeiros, F. A.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Mettu, P. S.

Moreno, P. J.

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

Morgan, J. E.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Mwanza, J. C.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Na, J. H.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Nakamura, M.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Nassif, N.

Negi, A.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Nicholas, P.

O’Connell, R. V.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Oakley, J. D.

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

Park, B.

Park, K. H.

J. W. Jeoung, K. H. Park, T. W. Kim, S. I. Khwarg, and D. M. Kim, “Diagnostic ability of optical coherence tomography with a normative database to detect localized retinal nerve fiber layer defects,” Ophthalmology 112(12), 2157–2163 (2005).
[Crossref] [PubMed]

Pelillo, M.

P. Kontschieder, S. Rota Bulò, H. Bischof, and M. Pelillo, “Structured class-labels in random forests for semantic image labeling,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2011), pp. 2190–2197.

Pereira, F. C.

J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of International Conference on Machine Learning, (ACM, 2001), pp. 282–289.

Pierce, M.

Pintelas, P.

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” in Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, (IOS, 2007), pp. 3–24.

Prasad, M.

M. Prasad, A. Zisserman, A. Fitzgibbon, M. P. Kumar, and P. H. Torr, “Learning class-specific edges for object detection and segmentation,” in Proceedings of Indian conference on Computer Vision, Graphics and Image Processing, (Springer, 2006), pp. 94–105.
[Crossref]

Rabbani, H.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Ranganathan, S.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Rathke, F.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Raza, A. S.

Ritch, R.

Rota Bulò, S.

P. Kontschieder, S. Rota Bulò, H. Bischof, and M. Pelillo, “Structured class-labels in random forests for semantic image labeling,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2011), pp. 2190–2197.

Sadda, S. R.

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

Sandler, S. F.

Sattmann, H.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Savitzky, A.

A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem. 36(8), 1627–1639 (1964).
[Crossref]

Schmidt, S.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schnörr, C.

F. Rathke, S. Schmidt, and C. Schnörr, “Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization,” Med. Image Anal. 18(5), 781–794 (2014).
[Crossref] [PubMed]

Schröder, S.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Schroff, F.

F. Schroff, A. Criminisi, and A. Zisserman, “Object Class Segmentation using Random Forests,” in Proceedings of British Machine Vision Conference,(BMVA, 2008), pp. 1–10.

Schubert, C.

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Seya, R.

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Shotton, J.

A. Criminisi, J. Shotton, and E. Konukoglu, “Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning,” Foundations and Trends in Comp. Grap. and Vis. 7(2–3), 81–227 (2012).

Smith, K.

A. Lucchi, Y. Li, K. Smith, and P. Fua, “Structured image segmentation using kernelized features,” in Proceedings of European Conference on Computer Vision, (Springer, 2012), pp. 400–413.

Sohn, Y. H.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Somfai, G. M.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Sonka, M.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Srinivasan, P. P.

Sung, K. R.

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

Susanna, R.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Tátrai, E.

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

Tearney, G.

Tornow, R. P.

Torr, P. H.

M. Prasad, A. Zisserman, A. Fitzgibbon, M. P. Kumar, and P. H. Torr, “Learning class-specific edges for object detection and segmentation,” in Proceedings of Indian conference on Computer Vision, Graphics and Image Processing, (Springer, 2006), pp. 94–105.
[Crossref]

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and 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]

Unterhuber, A.

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Vasconcelos, N.

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

Vessani, R. M.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Weinreb, R. N.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Wolf-Schnurrbusch, U.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

Wu, Z.

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

Xin, D.

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Yun, S.

Zaharakis, I.

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” in Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, (IOS, 2007), pp. 3–24.

Zangwill, L. M.

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

Zisserman, A.

F. Schroff, A. Criminisi, and A. Zisserman, “Object Class Segmentation using Random Forests,” in Proceedings of British Machine Vision Conference,(BMVA, 2008), pp. 1–10.

M. Prasad, A. Zisserman, A. Fitzgibbon, M. P. Kumar, and P. H. Torr, “Learning class-specific edges for object detection and segmentation,” in Proceedings of Indian conference on Computer Vision, Graphics and Image Processing, (Springer, 2006), pp. 94–105.
[Crossref]

Zitnick, C. L.

P. Dollár and C. L. Zitnick, “Structured forests for fast edge detection,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2013), pp. 1841–1848.

Am. J. Ophthalmol. (2)

F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna, and R. N. Weinreb, “Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography,” Am. J. Ophthalmol. 139(1), 44–55 (2005).
[Crossref] [PubMed]

A. Kanamori, M. Nakamura, M. F. Escano, R. Seya, H. Maeda, and A. Negi, “Evaluation of the glaucomatous damage on retinal nerve fiber layer thickness measured by optical coherence tomography,” Am. J. Ophthalmol. 135(4), 513–520 (2003).
[Crossref] [PubMed]

Anal. Chem. (1)

A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem. 36(8), 1627–1639 (1964).
[Crossref]

Biomed. Opt. Express (3)

Exp. Eye Res. (1)

E. M. Anger, A. Unterhuber, B. Hermann, H. Sattmann, C. Schubert, J. E. Morgan, A. Cowey, P. K. Ahnelt, and W. Drexler, “Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections,” Exp. Eye Res. 78(6), 1117–1125 (2004).
[Crossref] [PubMed]

Foundations and Trends in Comp. Grap. and Vis. (1)

A. Criminisi, J. Shotton, and E. Konukoglu, “Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning,” Foundations and Trends in Comp. Grap. and Vis. 7(2–3), 81–227 (2012).

IEEE Trans. Med. Imaging (1)

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref] [PubMed]

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

G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007).
[Crossref] [PubMed]

Invest. Ophthalmol. Vis. Sci. (2)

J. C. Mwanza, J. D. Oakley, D. L. Budenz, R. T. Chang, O. J. Knight, and W. J. Feuer, “Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma,” Invest. Ophthalmol. Vis. Sci. 52(11), 8323–8329 (2011).
[Crossref] [PubMed]

J. H. Na, K. R. Sung, S. Baek, Y. J. Kim, M. K. Durbin, H. J. Lee, H. K. Kim, and Y. H. Sohn, “Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 53(7), 3817–3826 (2012).
[Crossref] [PubMed]

J. Biomed. Opt. (2)

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

E. Tátrai, S. Ranganathan, M. Ferencz, D. C. DeBuc, and G. M. Somfai, “Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images,” J. Biomed. Opt. 16(5), 056004 (2011).
[Crossref] [PubMed]

J. Glaucoma (1)

Z. Wu, J. Huang, L. Dustin, and S. R. Sadda, “Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography,” J. Glaucoma 18(3), 213–216 (2009).
[Crossref] [PubMed]

Med. Image Anal. (2)

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Figures (9)

Fig. 1
Fig. 1 Random forest predictions and structured random forest predictions for given feature maps.
Fig. 2
Fig. 2 Training and prediction process of structured forest for edge. (a) Required data, (b) decision construction, (c) training, and (d) prediction of a structured forest for edge.
Fig. 3
Fig. 3 Predictions of edges with and without layer information. (a) Input image, (b) structured forests for edge prediction, and (c) proposed method prediction.
Fig. 4
Fig. 4 Flow of the processing blocks for proposed algorithm. (a) During training, and (b) during prediction.
Fig. 5
Fig. 5 Required images for preparing training data set. (a) Input image, (b) label image, and (c) contour image.
Fig. 6
Fig. 6 Processed outputs of layer ‘n’ selection block for Layer 3. (a) Label image, (b) contour image, (c) processed label binary mask, and (d) processed contour binary mask.
Fig. 7
Fig. 7 Block diagram illustrating image inputs during training, and the resulting images at each block during testing.
Fig. 8
Fig. 8 Predictions of proposed approach on test images with various deformations.
Fig. 9
Fig. 9 Illustration of Layers delineation in the presence of deformation (red), noise (yellow), shadows (orange) and low gradients (blue). (a) Proposed, (b) AD, (c) expert 1, and (d) expert 2.

Tables (3)

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Table 1 Metric 1: comparison of the classical graph approach, kernel-guided graph approach, and the proposed approach.

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Table 2 Metric 2: comparison of the classical graph approach, kernel-guided graph approach, and the proposed approach.

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Table 3 Metric 3: comparison of the classical graph approach, kernel-guided graph approach, and the proposed approach.

Metrics