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GRETINA: A Large-Scale High-Quality Generated Retinal Image Dataset for Security and Privacy Assessment

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

We present a generated dataset that is the largest and the first publicly shared high-quality synthetic retinal dataset. It is known that retinal patterns captured from humans are individual, even between identical twins. Despite the high accuracy and spoof resistance of retinal recognition systems, they have not reached the same level of maturity as the more popular face, fingerprint and iris. One cause is the lack of sufficient data for training and testing these systems. This paper reviews existing publicly available datasets of both real and generated retina images and identifies a lack of a large-scale high-quality retinal image dataset that can be used for security and privacy assessment. We fill this gap by using StyleGAN2-ADA to generate a synthetic dataset of five million high-quality retinal images from the limited available data.

The first author was supported by an RMIT University RD Gibson Grant and an RMIT University fee-waiver scholarship.

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Notes

  1. 1.

    Quality and fidelity are used interchangeably to show the resolution and clarity of images. Quality of the images is different from their diversity (distinctiveness).

  2. 2.

    https://github.com/mahshidsa/SG2-ADA-TheseRetinaeDoNotExist to support Reproducible Research (RR).

  3. 3.

    https://thispersondoesnotexist.com/.

  4. 4.

    https://github.com/mahshidsa/SG2-ADA-TheseRetinaeDoNotExist to support RR.

References

  1. oDocs retinal database. https://www.odocs-tech.com/database. Accessed 12 Nov 2022

  2. Abràmoff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013)

    Article  Google Scholar 

  3. Akram, M.U., Salam, A.A., Khawaja, S.G., Naqvi, S.G.H., Khan, S.A.: RIDB: a dataset of fundus images for retina based person identification. Data Brief 33, 106433 (2020)

    Article  Google Scholar 

  4. Aksac, A., Demetrick, D.J., Ozyer, T., Alhajj, R.: BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC Res. Notes 12(1), 1–3 (2019)

    Article  Google Scholar 

  5. Andreini, P., et al.: A two-stage GAN for high-resolution retinal image generation and segmentation. Electronics 11(1), 60 (2021)

    Article  Google Scholar 

  6. Arakala, A., Davis, S., Horadam, K.J.: Vascular biometric graph comparison: theory and performance. In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds.) Handbook of Vascular Biometrics. ACVPR, pp. 355–393. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27731-4_12

    Chapter  Google Scholar 

  7. Barratt, S., Sharma, R.: A note on the inception score. arXiv preprint arXiv:1801.01973 (2018)

  8. Beers, A., et al.: High-resolution medical image synthesis using progressively grown generative adversarial networks. arXiv preprint arXiv:1805.03144 (2018)

  9. Biswas, S., Rohdin, J., Drahanskỳ, M.: Synthetic retinal images from unconditional GANs. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2736–2739. IEEE (2019)

    Google Scholar 

  10. Carmona, E.J., Rincón, M., Garcí a Feijoó, J., Martínez-de-la Casa, J.M.: Identification of the optic nerve head with genetic algorithms. Artif. Intell. Med. 43(3), 243–259 (2008). https://doi.org/10.1016/j.artmed.2008.04.005

  11. Chalakkal, R.J., Abdulla, W.H., Sinumol, S.: Comparative analysis of university of Auckland diabetic retinopathy database. In: Proceedings of the 9th International Conference on Signal Processing Systems, pp. 235–239 (2017)

    Google Scholar 

  12. Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8188–8197 (2020)

    Google Scholar 

  13. Costa, P., et al.: Towards adversarial retinal image synthesis. arXiv preprint arXiv:1701.08974 (2017)

  14. Costa, P., et al.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imaging 37(3), 781–791 (2018)

    Article  MathSciNet  Google Scholar 

  15. Cuadros, J., Bresnick, G.: EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J. Diabetes Sci. Technol. 3(3), 509–516 (2009)

    Article  Google Scholar 

  16. Daugman, J.: 600 million citizens of India are now enrolled with biometric ID. SPIE Newsroom, vol. 7 (2014)

    Google Scholar 

  17. Decencière, E., et al.: Feedback on a publicly distributed image database: the messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)

    Article  MATH  Google Scholar 

  18. Dong, X., Jin, Z., Guo, Z., Teoh, A.B.J.: Towards generating high definition face images from deep templates. In: 2021 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–11. IEEE (2021)

    Google Scholar 

  19. Fraz, M.M., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012). https://doi.org/10.1109/TBME.2012.2205687

    Article  Google Scholar 

  20. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

  21. Guibas, J.T., Virdi, T.S., Li, P.S.: Synthetic medical images from dual generative adversarial networks. arXiv preprint arXiv:1709.01872 (2017)

  22. Hao, H., et al.: Does retinal vascular geometry vary with cardiac cycle? Investig. Ophthalmol. Vis. Sci. 53(9), 5799–5805 (2012)

    Article  Google Scholar 

  23. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  24. Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000). https://doi.org/10.1109/42.845178

    Article  Google Scholar 

  25. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  26. Jain, A.K., Nandakumar, K., Ross, A.: 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn. Lett. 79, 80–105 (2016)

    Article  Google Scholar 

  27. Kaplan, S., Lensu, L., Laaksonen, L., Uusitalo, H.: Evaluation of unconditioned deep generative synthesis of retinal images. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2020. LNCS, vol. 12002, pp. 262–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40605-9_23

    Chapter  Google Scholar 

  28. Kaplan, S., et al.: Deep generative models for synthetic retinal image generation (2017)

    Google Scholar 

  29. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)

  30. Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676 (2020)

  31. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

    Google Scholar 

  32. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)

    Google Scholar 

  33. Kauppi, T., et al.: The DIARETDB1 diabetic retinopathy database and evaluation protocol. In: BMVC, vol. 1, pp. 1–10 (2007)

    Google Scholar 

  34. Kim, T.N., et al.: A smartphone-based tool for rapid, portable, and automated wide-field retinal imaging. Transl. Vis. Sci. Technol. 7(5), 21–21 (2018)

    Article  Google Scholar 

  35. Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. arXiv preprint arXiv:1904.06991 (2019)

  36. Lajevardi, S.M., Arakala, A., Davis, S.A., Horadam, K.J.: Retina verification system based on biometric graph matching. IEEE Trans. Image Process. 22(9), 3625–3635 (2013)

    Article  Google Scholar 

  37. Lin, Z., Khetan, A., Fanti, G., Oh, S.: PacGAN: the power of two samples in generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  38. Mai, G., Cao, K., Yuen, P.C., Jain, A.K.: On the reconstruction of face images from deep face templates. IEEE Trans. Pattern Anal. Mach. Intell. 41(5), 1188–1202 (2018)

    Article  Google Scholar 

  39. Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)

  40. Marchesi, M.: Megapixel size image creation using generative adversarial networks. arXiv preprint arXiv:1706.00082 (2017)

  41. Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

  42. Ngan, M.L., Grother, P.J., Hanaoka, K.K., et al.: Ongoing face recognition vendor test (FRVT) part 6B: face recognition accuracy with face masks using post-COVID-19 algorithms (2020)

    Google Scholar 

  43. Niemeijer, M., et al.: Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans. Med. Imaging 29(1), 185–195 (2009)

    Article  Google Scholar 

  44. Ortega, M., Penedo, M.G., Rouco, J., Barreira, N., Carreira, M.J.: Personal verification based on extraction and characterisation of retinal feature points. J. Vis. Lang. Comput. 20(2), 80–90 (2009)

    Article  MATH  Google Scholar 

  45. Ortega, M., Penedo, M.G., Rouco, J., Barreira, N., Carreira, M.J.: Retinal verification using a feature points-based biometric pattern. EURASIP J. Adv. Signal Process. 2009, 1–13 (2009)

    Article  MATH  Google Scholar 

  46. Popovic, N., Vujosevic, S., Radunović, M., Radunović, M., Popovic, T.: Trend database: retinal images of healthy young subjects visualized by a portable digital non-mydriatic fundus camera. PLoS ONE 16(7), e0254918 (2021)

    Article  Google Scholar 

  47. Porwal, P., et al.: IDRiD: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)

    Article  Google Scholar 

  48. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

  49. Sadeghpour, M., Arakala, A., Davis, S., Horadam, K.: Protection of sparse retinal templates using cohort-based dissimilarity vectors. TechrXiv preprint techrxiv.20278923.v1 (2022)

    Google Scholar 

  50. Sajjadi, M.S., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.: Assessing generative models via precision and recall. arXiv preprint arXiv:1806.00035 (2018)

  51. Semerád, L., Drahanský, M.: Retinal vascular characteristics. In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds.) Handbook of Vascular Biometrics. ACVPR, pp. 309–354. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27731-4_11

    Chapter  Google Scholar 

  52. Sivaswamy, J., Krishnadas, S., Joshi, G.D., Jain, M., Tabish, A.U.S.: Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53–56. IEEE (2014)

    Google Scholar 

  53. Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

    Article  Google Scholar 

  54. Uhl, A.: State of the art in vascular biometrics. In: Uhl, A., Busch, C., Marcel, S., Veldhuis, R. (eds.) Handbook of Vascular Biometrics. ACVPR, pp. 3–61. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27731-4_1

    Chapter  Google Scholar 

  55. Uhl, A., Busch, C., Marcel, S., Veldhuis, R.: Handbook of Vascular Biometrics. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27731-4

    Book  Google Scholar 

  56. Yu, Z., Xiang, Q., Meng, J., Kou, C., Ren, Q., Lu, Y.: Retinal image synthesis from multiple-landmarks input with generative adversarial networks. Biomed. Eng. Online 18(1), 1–15 (2019)

    Article  Google Scholar 

  57. Zhang, Z., et al.: ORIGA-light: an online retinal fundus image database for glaucoma analysis and research. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 3065–3068. IEEE (2010)

    Google Scholar 

  58. Zhao, H., Li, H., Maurer-Stroh, S., Cheng, L.: Synthesizing retinal and neuronal images with generative adversarial nets. Med. Image Anal. 49, 14–26 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Mathematical Sciences, STEM College, RMIT University, Melbourne, Australia

    Mahshid Sadeghpour, Arathi Arakala, Stephen A. Davis & Kathy J. Horadam

Authors
  1. Mahshid Sadeghpour
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  2. Arathi Arakala
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  3. Stephen A. Davis
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  4. Kathy J. Horadam
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Correspondence to Mahshid Sadeghpour .

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Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Sadeghpour, M., Arakala, A., Davis, S.A., Horadam, K.J. (2023). GRETINA: A Large-Scale High-Quality Generated Retinal Image Dataset for Security and Privacy Assessment. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_27

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_27

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