Abstract

Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (5)

M. A. Hussain, A. Bhuiyan, C. D Luu, R. Theodore Smith, R. H Guymer, H. Ishikawa, J. S Schuman, and K. Ramamohanarao, “Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm,” PLoS One 13(6), e0198281 (2018).
[Crossref] [PubMed]

T. B. Feldman, M. A. Yakovleva, A. V. Larichev, P. M. Arbukhanova, A. S. Radchenko, S. A. Borzenok, V. A. Kuzmin, and M. A. Ostrovsky, “Spectral analysis of fundus autofluorescence pattern as a tool to detect early stages of degeneration in the retina and retinal pigment epithelium,” Eye (Lond.) 32(9), 1440–1448 (2018).
[Crossref] [PubMed]

N. Ali, B. Zafar, F. Riaz, S. Hanif Dar, N. Iqbal Ratyal, K. Bashir Bajwa, M. Kashif Iqbal, and M. Sajid, “A Hybrid Geometric Spatial Image Representation for scene classification,” PLoS One 13(9), e0203339 (2018).
[Crossref] [PubMed]

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
[Crossref] [PubMed]

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

2017 (11)

J. Ma, R. Desai, P. Nesper, M. Gill, A. Fawzi, and D. Skondra, “Optical coherence tomographic angiography imaging in age-related macular degeneration,” Ophthalmol. Eye Dis. 9, 1179172116686075 (2017).
[Crossref] [PubMed]

L. Roisman and R. Goldhardt, “OCT Angiography: An Upcoming Non-invasive Tool for Diagnosis of Age-related Macular Degeneration,” Curr. Ophthalmol. Rep. 5(2), 136–140 (2017).
[Crossref] [PubMed]

R. Zhao, A. Camino, J. Wang, A. M. Hagag, Y. Lu, S. T. Bailey, C. J. Flaxel, T. S. Hwang, D. Huang, D. Li, and Y. Jia, “Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography,” Biomed. Opt. Express 8(11), 5049–5064 (2017).
[Crossref] [PubMed]

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmol. Retina 1(4), 322–327 (2017).
[Crossref]

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

P. Brynolfsson, D. Nilsson, T. Torheim, T. Asklund, C. T. Karlsson, J. Trygg, T. Nyholm, and A. Garpebring, “Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters,” Sci. Rep. 7(1), 4041 (2017).
[Crossref] [PubMed]

E. Tsikata, I. Laíns, J. Gil, M. Marques, K. Brown, T. Mesquita, P. Melo, M. da Luz Cachulo, I. K. Kim, D. Vavvas, J. N. Murta, J. B. Miller, R. Silva, J. W. Miller, T. C. Chen, and D. Husain, “Automated brightness and contrast adjustment of color fundus photographs for the grading of age-related macular degeneration,” Transl. Vis. Sci. Technol. 6(2), 3 (2017).
[Crossref] [PubMed]

H. Li, W. Liu, B. Dong, J. V. Kaluzny, A. A. Fawzi, and H. F. Zhang, “Snapshot hyperspectral retinal imaging using compact spectral resolving detector array,” J. Biophotonics 10(6-7), 830–839 (2017).
[Crossref] [PubMed]

J. Kaluzny, H. Li, W. Liu, P. Nesper, J. Park, H. F. Zhang, and A. A. Fawzi, “Bayer filter snapshot hyperspectral fundus camera for human retinal imaging,” Curr. Eye Res. 42(4), 629–635 (2017).
[Crossref] [PubMed]

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

S. Khalid, M. U. Akram, T. Hassan, A. Nasim, and A. Jameel, “Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images,” BioMed Res. Int. 2017, 7148245 (2017).
[Crossref] [PubMed]

2016 (4)

N. Ali, K. B. Bajwa, R. Sablatnig, S. A. Chatzichristofis, Z. Iqbal, M. Rashid, and H. A. Habib, “A novel image retrieval based on visual words integration of SIFT and SURF,” PLoS One 11(6), e0157428 (2016).
[Crossref] [PubMed]

N. Ali, K. B. Bajwa, R. Sablatnig, and Z. Mehmood, “Image retrieval by addition of spatial information based on histograms of triangular regions,” Comput. Electr. Eng. 54, 539–550 (2016).
[Crossref]

Y. Tong, T. Ben Ami, S. Hong, R. Heintzmann, G. Gerig, Z. Ablonczy, C. A. Curcio, T. Ach, and R. T. Smith, “Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration,” Retina 36(Suppl 1), S127–S136 (2016).
[Crossref] [PubMed]

T. V. Phan, L. Seoud, H. Chakor, and F. Cheriet, “Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images,” J. Ophthalmol. 2016, 5893601 (2016).
[Crossref] [PubMed]

2015 (3)

K. Kumari and D. Mittal, “Automated drusen detection technique for age-related macular degeneration,” Journal of Biomedical Engineering and Medical Imaging 2, 18 (2015).

D. Mittal and K. Kumari, “Automated detection and segmentation of drusen in retinal fundus images,” Comput. Electr. Eng. 47, 82–95 (2015).
[Crossref]

A. R. Prasath and M. Ramya, “Detection of macular drusen based on texture descriptors,” Research Journal of Information Technology 7(1), 70–79 (2015).
[Crossref]

2014 (2)

M. R. K. Mookiah, U. R. Acharya, J. E. Koh, V. Chandran, C. K. Chua, J. H. Tan, C. M. Lim, E. Y. Ng, K. Noronha, L. Tong, and A. Laude, “Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images,” Comput. Biol. Med. 53, 55–64 (2014).
[Crossref] [PubMed]

Y. Kanagasingam, A. Bhuiyan, M. D. Abràmoff, R. T. Smith, L. Goldschmidt, and T. Y. Wong, “Progress on retinal image analysis for age related macular degeneration,” Prog. Retin. Eye Res. 38, 20–42 (2014).
[Crossref] [PubMed]

2012 (2)

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
[Crossref] [PubMed]

G. Zhang and Y. Lu, “Bias-corrected random forests in regression,” J. Appl. Stat. 39(1), 151–160 (2012).
[Crossref]

2011 (3)

A. A. Fawzi, N. Lee, J. H. Acton, A. F. Laine, and R. T. Smith, “Recovery of macular pigment spectrum in vivo using hyperspectral image analysis,” J. Biomed. Opt. 16(10), 106008 (2011).
[Crossref] [PubMed]

A. D. Mora, P. M. Vieira, A. Manivannan, and J. M. Fonseca, “Automated drusen detection in retinal images using analytical modelling algorithms,” Biomed. Eng. Online 10(1), 59 (2011).
[Crossref] [PubMed]

Z. Yehoshua, P. J. Rosenfeld, G. Gregori, W. J. Feuer, M. Falcão, B. J. Lujan, and C. Puliafito, “Progression of geographic atrophy in age-related macular degeneration imaged with spectral domain optical coherence tomography,” Ophthalmology 118(4), 679–686 (2011).
[Crossref] [PubMed]

2009 (1)

D. Zinovev, D. Raicu, J. Furst, and S. G. Armato, “Predicting radiological panel opinions using a panel of machine learning classifiers,” Algorithms 2(4), 1473–1502 (2009).
[Crossref]

2003 (1)

R. T. Smith, T. Nagasaki, J. R. Sparrow, I. Barbazetto, C. C. Klaver, and J. K. Chan, “A method of drusen measurement based on the geometry of fundus reflectance,” Biomed. Eng. Online 2(1), 10 (2003).
[Crossref] [PubMed]

2002 (1)

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R News 2, 18–22 (2002).

2001 (1)

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

1997 (2)

P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss,” Mach. Learn. 29(2/3), 103–130 (1997).
[Crossref]

Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci. 55(1), 119–139 (1997).
[Crossref]

1990 (1)

G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
[Crossref] [PubMed]

1979 (1)

R. M. Haralick, “Statistical and structural approaches to texture,” Proc. IEEE 67(5), 786–804 (1979).
[Crossref]

1973 (1)

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[Crossref]

1947 (1)

B. L. Welch, “The generalisation of student’s problems when several different population variances are involved,” Biometrika 34(1-2), 28–35 (1947).
[Crossref] [PubMed]

Ablonczy, Z.

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X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
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M. A. Hussain, A. Bhuiyan, C. D Luu, R. Theodore Smith, R. H Guymer, H. Ishikawa, J. S Schuman, and K. Ramamohanarao, “Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm,” PLoS One 13(6), e0198281 (2018).
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U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
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Y. Tong, T. Ben Ami, S. Hong, R. Heintzmann, G. Gerig, Z. Ablonczy, C. A. Curcio, T. Ach, and R. T. Smith, “Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration,” Retina 36(Suppl 1), S127–S136 (2016).
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G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
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M. A. Hussain, A. Bhuiyan, C. D Luu, R. Theodore Smith, R. H Guymer, H. Ishikawa, J. S Schuman, and K. Ramamohanarao, “Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm,” PLoS One 13(6), e0198281 (2018).
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Ramya, M.

A. R. Prasath and M. Ramya, “Detection of macular drusen based on texture descriptors,” Research Journal of Information Technology 7(1), 70–79 (2015).
[Crossref]

Rao, A. K.

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

Rashid, M.

N. Ali, K. B. Bajwa, R. Sablatnig, S. A. Chatzichristofis, Z. Iqbal, M. Rashid, and H. A. Habib, “A novel image retrieval based on visual words integration of SIFT and SURF,” PLoS One 11(6), e0157428 (2016).
[Crossref] [PubMed]

Räth, U.

G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
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Remeseiro, B.

B. Remeseiro, N. Barreira, D. Calvo, M. Ortega, and M. G. Penedo, “Automatic drusen detection from digital retinal images: AMD prevention,” in International Conference on Computer Aided Systems Theory (Springer, 2009), pp. 187–194.
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Ren, X.

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

Riaz, F.

N. Ali, B. Zafar, F. Riaz, S. Hanif Dar, N. Iqbal Ratyal, K. Bashir Bajwa, M. Kashif Iqbal, and M. Sajid, “A Hybrid Geometric Spatial Image Representation for scene classification,” PLoS One 13(9), e0203339 (2018).
[Crossref] [PubMed]

Roisman, L.

L. Roisman and R. Goldhardt, “OCT Angiography: An Upcoming Non-invasive Tool for Diagnosis of Age-related Macular Degeneration,” Curr. Ophthalmol. Rep. 5(2), 136–140 (2017).
[Crossref] [PubMed]

Rosenfeld, P. J.

Z. Yehoshua, P. J. Rosenfeld, G. Gregori, W. J. Feuer, M. Falcão, B. J. Lujan, and C. Puliafito, “Progression of geographic atrophy in age-related macular degeneration imaged with spectral domain optical coherence tomography,” Ophthalmology 118(4), 679–686 (2011).
[Crossref] [PubMed]

S Schuman, J.

M. A. Hussain, A. Bhuiyan, C. D Luu, R. Theodore Smith, R. H Guymer, H. Ishikawa, J. S Schuman, and K. Ramamohanarao, “Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm,” PLoS One 13(6), e0198281 (2018).
[Crossref] [PubMed]

Sablatnig, R.

N. Ali, K. B. Bajwa, R. Sablatnig, and Z. Mehmood, “Image retrieval by addition of spatial information based on histograms of triangular regions,” Comput. Electr. Eng. 54, 539–550 (2016).
[Crossref]

N. Ali, K. B. Bajwa, R. Sablatnig, S. A. Chatzichristofis, Z. Iqbal, M. Rashid, and H. A. Habib, “A novel image retrieval based on visual words integration of SIFT and SURF,” PLoS One 11(6), e0157428 (2016).
[Crossref] [PubMed]

Sadeghipour, A.

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
[Crossref] [PubMed]

Sajid, M.

N. Ali, B. Zafar, F. Riaz, S. Hanif Dar, N. Iqbal Ratyal, K. Bashir Bajwa, M. Kashif Iqbal, and M. Sajid, “A Hybrid Geometric Spatial Image Representation for scene classification,” PLoS One 13(9), e0203339 (2018).
[Crossref] [PubMed]

Schapire, R. E.

Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci. 55(1), 119–139 (1997).
[Crossref]

Schmidt-Erfurth, U.

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
[Crossref] [PubMed]

Seoud, L.

T. V. Phan, L. Seoud, H. Chakor, and F. Cheriet, “Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images,” J. Ophthalmol. 2016, 5893601 (2016).
[Crossref] [PubMed]

Shanmugam, K.

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
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Silva, R.

E. Tsikata, I. Laíns, J. Gil, M. Marques, K. Brown, T. Mesquita, P. Melo, M. da Luz Cachulo, I. K. Kim, D. Vavvas, J. N. Murta, J. B. Miller, R. Silva, J. W. Miller, T. C. Chen, and D. Husain, “Automated brightness and contrast adjustment of color fundus photographs for the grading of age-related macular degeneration,” Transl. Vis. Sci. Technol. 6(2), 3 (2017).
[Crossref] [PubMed]

Sivaprasad, S.

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
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Skondra, D.

J. Ma, R. Desai, P. Nesper, M. Gill, A. Fawzi, and D. Skondra, “Optical coherence tomographic angiography imaging in age-related macular degeneration,” Ophthalmol. Eye Dis. 9, 1179172116686075 (2017).
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Smith, R. T.

Y. Tong, T. Ben Ami, S. Hong, R. Heintzmann, G. Gerig, Z. Ablonczy, C. A. Curcio, T. Ach, and R. T. Smith, “Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration,” Retina 36(Suppl 1), S127–S136 (2016).
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Y. Kanagasingam, A. Bhuiyan, M. D. Abràmoff, R. T. Smith, L. Goldschmidt, and T. Y. Wong, “Progress on retinal image analysis for age related macular degeneration,” Prog. Retin. Eye Res. 38, 20–42 (2014).
[Crossref] [PubMed]

A. A. Fawzi, N. Lee, J. H. Acton, A. F. Laine, and R. T. Smith, “Recovery of macular pigment spectrum in vivo using hyperspectral image analysis,” J. Biomed. Opt. 16(10), 106008 (2011).
[Crossref] [PubMed]

R. T. Smith, T. Nagasaki, J. R. Sparrow, I. Barbazetto, C. C. Klaver, and J. K. Chan, “A method of drusen measurement based on the geometry of fundus reflectance,” Biomed. Eng. Online 2(1), 10 (2003).
[Crossref] [PubMed]

Sparrow, J. R.

R. T. Smith, T. Nagasaki, J. R. Sparrow, I. Barbazetto, C. C. Klaver, and J. K. Chan, “A method of drusen measurement based on the geometry of fundus reflectance,” Biomed. Eng. Online 2(1), 10 (2003).
[Crossref] [PubMed]

Sree, S. V.

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

Tan, J. H.

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

M. R. K. Mookiah, U. R. Acharya, J. E. Koh, V. Chandran, C. K. Chua, J. H. Tan, C. M. Lim, E. Y. Ng, K. Noronha, L. Tong, and A. Laude, “Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images,” Comput. Biol. Med. 53, 55–64 (2014).
[Crossref] [PubMed]

Theodore Smith, R.

M. A. Hussain, A. Bhuiyan, C. D Luu, R. Theodore Smith, R. H Guymer, H. Ishikawa, J. S Schuman, and K. Ramamohanarao, “Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm,” PLoS One 13(6), e0198281 (2018).
[Crossref] [PubMed]

Tong, L.

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

M. R. K. Mookiah, U. R. Acharya, J. E. Koh, V. Chandran, C. K. Chua, J. H. Tan, C. M. Lim, E. Y. Ng, K. Noronha, L. Tong, and A. Laude, “Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images,” Comput. Biol. Med. 53, 55–64 (2014).
[Crossref] [PubMed]

Tong, Y.

Y. Tong, T. Ben Ami, S. Hong, R. Heintzmann, G. Gerig, Z. Ablonczy, C. A. Curcio, T. Ach, and R. T. Smith, “Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration,” Retina 36(Suppl 1), S127–S136 (2016).
[Crossref] [PubMed]

Torheim, T.

P. Brynolfsson, D. Nilsson, T. Torheim, T. Asklund, C. T. Karlsson, J. Trygg, T. Nyholm, and A. Garpebring, “Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters,” Sci. Rep. 7(1), 4041 (2017).
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Tridandapani, S.

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
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Trygg, J.

P. Brynolfsson, D. Nilsson, T. Torheim, T. Asklund, C. T. Karlsson, J. Trygg, T. Nyholm, and A. Garpebring, “Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters,” Sci. Rep. 7(1), 4041 (2017).
[Crossref] [PubMed]

Tsikata, E.

E. Tsikata, I. Laíns, J. Gil, M. Marques, K. Brown, T. Mesquita, P. Melo, M. da Luz Cachulo, I. K. Kim, D. Vavvas, J. N. Murta, J. B. Miller, R. Silva, J. W. Miller, T. C. Chen, and D. Husain, “Automated brightness and contrast adjustment of color fundus photographs for the grading of age-related macular degeneration,” Transl. Vis. Sci. Technol. 6(2), 3 (2017).
[Crossref] [PubMed]

van Kaick, G.

G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
[Crossref] [PubMed]

Vavvas, D.

E. Tsikata, I. Laíns, J. Gil, M. Marques, K. Brown, T. Mesquita, P. Melo, M. da Luz Cachulo, I. K. Kim, D. Vavvas, J. N. Murta, J. B. Miller, R. Silva, J. W. Miller, T. C. Chen, and D. Husain, “Automated brightness and contrast adjustment of color fundus photographs for the grading of age-related macular degeneration,” Transl. Vis. Sci. Technol. 6(2), 3 (2017).
[Crossref] [PubMed]

Vieira, P. M.

A. D. Mora, P. M. Vieira, A. Manivannan, and J. M. Fonseca, “Automated drusen detection in retinal images using analytical modelling algorithms,” Biomed. Eng. Online 10(1), 59 (2011).
[Crossref] [PubMed]

Waldstein, S. M.

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
[Crossref] [PubMed]

Wang, H.

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

Wang, J.

Welch, B. L.

B. L. Welch, “The generalisation of student’s problems when several different population variances are involved,” Biometrika 34(1-2), 28–35 (1947).
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Wiener, M.

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R News 2, 18–22 (2002).

Wong, T. Y.

Y. Kanagasingam, A. Bhuiyan, M. D. Abràmoff, R. T. Smith, L. Goldschmidt, and T. Y. Wong, “Progress on retinal image analysis for age related macular degeneration,” Prog. Retin. Eye Res. 38, 20–42 (2014).
[Crossref] [PubMed]

Yakovleva, M. A.

T. B. Feldman, M. A. Yakovleva, A. V. Larichev, P. M. Arbukhanova, A. S. Radchenko, S. A. Borzenok, V. A. Kuzmin, and M. A. Ostrovsky, “Spectral analysis of fundus autofluorescence pattern as a tool to detect early stages of degeneration in the retina and retinal pigment epithelium,” Eye (Lond.) 32(9), 1440–1448 (2018).
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Yáñez-Márquez, C.

A. García-Floriano, Á. Ferreira-Santiago, O. Camacho-Nieto, and C. Yáñez-Márquez, “A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images,” Comput. Electr. Eng. (2017).
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Yang, X.

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
[Crossref] [PubMed]

Yehoshua, Z.

Z. Yehoshua, P. J. Rosenfeld, G. Gregori, W. J. Feuer, M. Falcão, B. J. Lujan, and C. Puliafito, “Progression of geographic atrophy in age-related macular degeneration imaged with spectral domain optical coherence tomography,” Ophthalmology 118(4), 679–686 (2011).
[Crossref] [PubMed]

Yoshida, E. J.

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
[Crossref] [PubMed]

Yu, D. S.

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity,” Med. Phys. 39(9), 5732–5739 (2012).
[Crossref] [PubMed]

Zafar, B.

N. Ali, B. Zafar, F. Riaz, S. Hanif Dar, N. Iqbal Ratyal, K. Bashir Bajwa, M. Kashif Iqbal, and M. Sajid, “A Hybrid Geometric Spatial Image Representation for scene classification,” PLoS One 13(9), e0203339 (2018).
[Crossref] [PubMed]

Zerban, H.

G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
[Crossref] [PubMed]

Zhang, G.

G. Zhang and Y. Lu, “Bias-corrected random forests in regression,” J. Appl. Stat. 39(1), 151–160 (2012).
[Crossref]

Zhang, H. F.

J. Kaluzny, H. Li, W. Liu, P. Nesper, J. Park, H. F. Zhang, and A. A. Fawzi, “Bayer filter snapshot hyperspectral fundus camera for human retinal imaging,” Curr. Eye Res. 42(4), 629–635 (2017).
[Crossref] [PubMed]

H. Li, W. Liu, B. Dong, J. V. Kaluzny, A. A. Fawzi, and H. F. Zhang, “Snapshot hyperspectral retinal imaging using compact spectral resolving detector array,” J. Biophotonics 10(6-7), 830–839 (2017).
[Crossref] [PubMed]

Zhao, R.

Zhao, Y.

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

Zheng, Y.

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

Zinovev, D.

D. Zinovev, D. Raicu, J. Furst, and S. G. Armato, “Predicting radiological panel opinions using a panel of machine learning classifiers,” Algorithms 2(4), 1473–1502 (2009).
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Zuna, I.

G. Layer, I. Zuna, A. Lorenz, H. Zerban, U. Haberkorn, P. Bannasch, G. van Kaick, and U. Räth, “Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution,” Ultrason. Imaging 12(3), 171–188 (1990).
[Crossref] [PubMed]

Algorithms (1)

D. Zinovev, D. Raicu, J. Furst, and S. G. Armato, “Predicting radiological panel opinions using a panel of machine learning classifiers,” Algorithms 2(4), 1473–1502 (2009).
[Crossref]

BioMed Res. Int. (1)

S. Khalid, M. U. Akram, T. Hassan, A. Nasim, and A. Jameel, “Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images,” BioMed Res. Int. 2017, 7148245 (2017).
[Crossref] [PubMed]

Biomed. Eng. Online (2)

A. D. Mora, P. M. Vieira, A. Manivannan, and J. M. Fonseca, “Automated drusen detection in retinal images using analytical modelling algorithms,” Biomed. Eng. Online 10(1), 59 (2011).
[Crossref] [PubMed]

R. T. Smith, T. Nagasaki, J. R. Sparrow, I. Barbazetto, C. C. Klaver, and J. K. Chan, “A method of drusen measurement based on the geometry of fundus reflectance,” Biomed. Eng. Online 2(1), 10 (2003).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Biometrika (1)

B. L. Welch, “The generalisation of student’s problems when several different population variances are involved,” Biometrika 34(1-2), 28–35 (1947).
[Crossref] [PubMed]

Comput. Biol. Med. (3)

P. Burlina, K. D. Pacheco, N. Joshi, D. E. Freund, and N. M. Bressler, “Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis,” Comput. Biol. Med. 82, 80–86 (2017).
[Crossref] [PubMed]

M. R. K. Mookiah, U. R. Acharya, J. E. Koh, V. Chandran, C. K. Chua, J. H. Tan, C. M. Lim, E. Y. Ng, K. Noronha, L. Tong, and A. Laude, “Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images,” Comput. Biol. Med. 53, 55–64 (2014).
[Crossref] [PubMed]

J. E. W. Koh, U. R. Acharya, Y. Hagiwara, U. Raghavendra, J. H. Tan, S. V. Sree, S. V. Bhandary, A. K. Rao, S. Sivaprasad, K. C. Chua, A. Laude, and L. Tong, “Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies,” Comput. Biol. Med. 84, 89–97 (2017).
[Crossref] [PubMed]

Comput. Electr. Eng. (2)

N. Ali, K. B. Bajwa, R. Sablatnig, and Z. Mehmood, “Image retrieval by addition of spatial information based on histograms of triangular regions,” Comput. Electr. Eng. 54, 539–550 (2016).
[Crossref]

D. Mittal and K. Kumari, “Automated detection and segmentation of drusen in retinal fundus images,” Comput. Electr. Eng. 47, 82–95 (2015).
[Crossref]

Curr. Eye Res. (1)

J. Kaluzny, H. Li, W. Liu, P. Nesper, J. Park, H. F. Zhang, and A. A. Fawzi, “Bayer filter snapshot hyperspectral fundus camera for human retinal imaging,” Curr. Eye Res. 42(4), 629–635 (2017).
[Crossref] [PubMed]

Curr. Ophthalmol. Rep. (1)

L. Roisman and R. Goldhardt, “OCT Angiography: An Upcoming Non-invasive Tool for Diagnosis of Age-related Macular Degeneration,” Curr. Ophthalmol. Rep. 5(2), 136–140 (2017).
[Crossref] [PubMed]

Eye (Lond.) (1)

T. B. Feldman, M. A. Yakovleva, A. V. Larichev, P. M. Arbukhanova, A. S. Radchenko, S. A. Borzenok, V. A. Kuzmin, and M. A. Ostrovsky, “Spectral analysis of fundus autofluorescence pattern as a tool to detect early stages of degeneration in the retina and retinal pigment epithelium,” Eye (Lond.) 32(9), 1440–1448 (2018).
[Crossref] [PubMed]

IEEE Access (1)

X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang, J. Lian, and Y. He, “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access 6, 2952–2961 (2018).
[Crossref]

IEEE Trans. Syst. Man Cybern. (1)

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (1)

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Invest. Ophthalmol. Vis. Sci. 59(8), 3199–3208 (2018).
[Crossref] [PubMed]

J. Appl. Stat. (1)

G. Zhang and Y. Lu, “Bias-corrected random forests in regression,” J. Appl. Stat. 39(1), 151–160 (2012).
[Crossref]

J. Biomed. Opt. (1)

A. A. Fawzi, N. Lee, J. H. Acton, A. F. Laine, and R. T. Smith, “Recovery of macular pigment spectrum in vivo using hyperspectral image analysis,” J. Biomed. Opt. 16(10), 106008 (2011).
[Crossref] [PubMed]

J. Biophotonics (1)

H. Li, W. Liu, B. Dong, J. V. Kaluzny, A. A. Fawzi, and H. F. Zhang, “Snapshot hyperspectral retinal imaging using compact spectral resolving detector array,” J. Biophotonics 10(6-7), 830–839 (2017).
[Crossref] [PubMed]

J. Comput. Syst. Sci. (1)

Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci. 55(1), 119–139 (1997).
[Crossref]

J. Ophthalmol. (1)

T. V. Phan, L. Seoud, H. Chakor, and F. Cheriet, “Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images,” J. Ophthalmol. 2016, 5893601 (2016).
[Crossref] [PubMed]

Journal of Biomedical Engineering and Medical Imaging (1)

K. Kumari and D. Mittal, “Automated drusen detection technique for age-related macular degeneration,” Journal of Biomedical Engineering and Medical Imaging 2, 18 (2015).

Mach. Learn. (2)

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss,” Mach. Learn. 29(2/3), 103–130 (1997).
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Supplementary Material (1)

NameDescription
» Dataset 1       This is the dataset used in the paper "Drusen diagnosis comparison between hyper-spectral and color retinal images

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Figures (8)

Fig. 1
Fig. 1 The classification approach for drusen diagnosis. The process consists of cropping of ROIs, image feature extraction, and classification.
Fig. 2
Fig. 2 An illustration of hyper-spectral imaging structure. The image resolution is 1024 × 2048. Each hyper-spectral superpixel is represented by 4 × 4 CMOS pixels.
Fig. 3
Fig. 3 The process of cropping one drusen in the image. (A) Drusen image: one single spectral image with 256 × 512 pixels of resolution. The irregular outline in red encloses the cropping area. (B) ROI drusen image: all the pixels values except for the cropping area were converted to zero. The image resolution remains the same.
Fig. 4
Fig. 4 An example of GLCM calculation. Left: image with quantized gray level L g =8. Right: the GLCM matrix for d=1and θ= 0 ο .
Fig. 5
Fig. 5 An illustration of the OOB error calculation. OOB error is the average of all the instance errors. Each instance error is the average error of trees that do not select the instance.
Fig. 6
Fig. 6 The optimal parameters of random forest algorithm. (A and B) The number of features selected at each split is 7 when the number of trees is 212 on hyper-spectral image data; (C and D) The number of features selected at each split is 15 when the number of trees is 427 on RGB image data
Fig. 7
Fig. 7 Mean decrease in Gini Index of each feature. Inverse difference moment is the most important feature since it has the highest mean decrease in Gini Index.
Fig. 8
Fig. 8 The distribution of inverse difference moment. Drusen images have relatively low inverse difference moment, which indicate that drusen images are more heterogeneous.

Tables (15)

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Table 1 A summary of hyper-spectral data and RGB data set

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Table 2 Mean accuracy of different split ratios in the hyper-spectral image test data

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Table 3 Mean sensitivity of different split ratios in the hyper-spectral image test Data

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Table 4 Mean specificity of different split ratios in the hyper-spectral image test Data

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Table 5 P-values of Welch’s t-test when comparing the mean sensitivity between the 80%-20% split ratio with other split ratios (hyper-spectral image)

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Table 6 Mean accuracy of different split ratios in the RGB image test data

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Table 7 Mean sensitivity of different split ratios in the RGB image test data

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Table 8 Mean specificity of different split ratios in the RGB image test data

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Table 9 P-values of Welch’s t-test when comparing the mean accuracy between the 80%-20% split ratio with other split ratios (RGB image)

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Table 10 Classification performance for hyper-spectral image data set; the numbers between parentheses represent P-values of Welch’s t-test when comparing random forest with the other classifiers

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Table 11 Classification performance on the RGB image data set; the numbers between parentheses represent P-values of Welch’s t-test when comparing AdaBoost with the other classifiers

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Table 12 P-values of Welch’s t-test for random forest classification performance comparison between hyper-spectral and RGB image

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Table 13 Random forest classification result in hyper-spectral testing data using different feature sets

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Table 14 P-values of Welch’s t-test when comparing classification performance using intensity features vs texture features

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Table 15 Haralick's Texture Features Employed in the Study

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

p(i,j)= f(i,j) i=0 L g 1 j=0 L g 1 f(i,j) ,
I( s 1 , s 2 ,, s m )= i=1 m p i log 2 ( p i ) ,
E(A)= j=1 v s 1j ++ s mj s I( s 1j ,, s mj )
Gain(A)=I( s 1 , s 2 ,, s m )E(A)
C(x)=arg max k{1,,m} p( C k ) l=1 n p( x i | C k )
D 1 (i)= 1 S , for i=1,,S
α t = 1 2 ln( 1 ε t ε t ),
D t+1 (i)= D t (i)exp( α t y i h t ( x i )) z t ,
H(x)=sign( t=1 T α t h t (x) )
OOB( x i )=(1/ (T T t OOB ) i ) t=1 T [ h t ^ ( x i )I(( y i , x i ) T t OOB ) f t ( x i )] ,
Gini(node)= i=1 m p i (1 p i ) ,

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