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Selected Image Analysis Methods for Ophthalmology

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Artificial Intelligence in Ophthalmology

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Abstract

Imaging tests are one of the main medical data sources, and image analysis methods have become a permanent element of the diagnostic process. The methods presented in this chapter are general and can be applied to a wide variety of clinical problems. We start with the introduction to digital representation of images and color space. Then we discuss selected issues of image preprocessing, registration and inference. We also present the methods for preprocessing fundus retinal images prior to applying the inference process which may support the diagnosis of many diseases. Finally, we present a sample technique for diagnosing diabetic retinopathy.

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References

  1. Grosso A. Hypertensive retinopathy revisited: some answers, more questions. Br J Ophthalmol. 2005;89:1646–54. https://doi.org/10.1136/bjo.2005.072546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Danis RP, Davis MD. Proliferative diabetic retinopathy, diabetic retinopathy. Totowa, NJ: Humana Press; 2008. p. 29–65.

    Book  Google Scholar 

  3. Matsui M, Tashiro T, Matsumoto K, Yamamoto S. A study on automatic and quantitative diagnosis of fundus photographs. I. Detection of contour line of retinal blood vessel images on color fundus photographs. Nippon Ganka Gakkai Zasshi. 1973;77(8):907–18.

    CAS  PubMed  Google Scholar 

  4. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. ISSN 1361-8415. https://doi.org/10.1016/j.media.2017.07.005.

    Article  Google Scholar 

  5. Lathuilière S, Mesejo P, Alameda-Pineda X, Horaud R. A comprehensive analysis of deep regression. IEEE Trans Pattern Anal Mach Intell. 2020;42(9):2065–81. https://doi.org/10.1109/TPAMI.2019.2910523.

    Article  PubMed  Google Scholar 

  6. Min E, Guo X, Liu Q, Zhang G, Cui J, Long J. A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access. 2018;6:39501–14. https://doi.org/10.1109/ACCESS.2018.2855437.

    Article  Google Scholar 

  7. Stanescu L, Burdescu DD, Stoica C. Color image segmentation applied to medical domain. In: Yin H, Tino P, Corchado E, Byrne W, Yao X, editors. Intelligent Data Engineering and Automated Learning – IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol. 4881. Berlin: Springer; 2007. https://doi.org/10.1007/978-3-540-77226-2_47.

    Chapter  Google Scholar 

  8. Suzuki N. Distinction between manifestations of diabetic retinopathy and dust artifacts using three-dimensional HSV color space (Version 10005546). 2016. https://doi.org/10.5281/zenodo.1126874.

  9. Semary N. A proposed HSV-based pseudo coloring scheme for enhancing medical image. 2018:81–92. https://doi.org/10.5121/csit.2018.80407.

  10. Salem NM, Nandi AK. Novel and adaptive contribution of the red channel in pre-processing of colour fundus images. J Franklin Inst. 2007;344(3–4):243–56. ISSN 0016-0032. https://doi.org/10.1016/j.jfranklin.2006.09.001.

    Article  Google Scholar 

  11. Zhao Y, Liu Y, Wu X, Harding S, Zheng Y. Retinal vessel segmentation: an efficient graph cut approach with Retinex and local phase. PLoS One. 2015;10(4):1–22.

    Google Scholar 

  12. Kolar R, Odstrcilik J, Jan J, Harabis V. Illumination correction and contrast equalization in colour fundus images. European Signal Processing Conference, 2011. p. 298–302.

    Google Scholar 

  13. Foracchia M, Grisan E, Ruggeri A. Luminosity and contrast normalization in retinal images. Med Image Anal. 2005;9(3):179–90.

    Article  Google Scholar 

  14. Narasimha-Iyer H, Can A, Roysam B, Stewart V, Tanenbaum HL, Majerovics A, Singh H. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans Biomed Eng. 2006;53(6):1084–98.

    Article  Google Scholar 

  15. Grisan E, Giani A, Ceseracciu E, Ruggeri A. Model-based illumination correction in retinal images. IEEE International Symposium on Biomedical Imaging: Nano to Macro. 2006. p. 984–7.

    Google Scholar 

  16. Walter T, Massin P, Erginay A, et al. Automatic detection of microaneurysms in color fundus images. Med Image Anal. 2007;11:555–66. https://doi.org/10.1016/j.media.2007.05.001.

    Article  PubMed  Google Scholar 

  17. Fleming A, Philip S, Goatman K, Olson J, Sharp P. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 2006;25(9):1223–32.

    Article  Google Scholar 

  18. Qidwai U, Qidwai U. Blind deconvolution for retinal image enhancement. IEEE EMBS Conference on Biomedical Engineering and Sciences. 2010. p. 20–25.

    Google Scholar 

  19. Sivaswamy J, Agarwal A, Chawla M, Rani A, Das T. Extraction of capillary non-perfusion from fundus fluorescein angiogram. In: Fred A, Filipe J, Gamboa H, editors. Biomedical engineering systems and technologies. Berlin: Springer; 2009. p. 176–88.

    Google Scholar 

  20. Rajan K, Sreejith C. Retinal image processing and classification using convolutional neural networks. In: Pandian D, Fernando X, Baig Z, Shi F, editors. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol. 30. Cham: Springer; 2019. https://doi.org/10.1007/978-3-030-00665-5_120.

    Chapter  Google Scholar 

  21. Meitav N, Ribak EN. Improving retinal image resolution with iterative weighted shift-and-add. J Opt Soc Am A. 2011;28(7):1395–402. https://doi.org/10.1364/JOSAA.28.001395.

    Article  Google Scholar 

  22. Hernandez-Matas C, Zabulis X. Super resolution for fundoscopy based on 3D image registration. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014. p. 6332–8. https://doi.org/10.1109/EMBC.2014.6945077.

  23. Can A, Stewart CV, Roysam B, Tanenbaum HL. A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina. IEEE Trans Pattern Anal Mach Intell. 2002;24(3):412–9. https://doi.org/10.1109/34.990145.

  24. Ryan N, Heneghan C, de Chazal P. Registration of digital retinal images using landmark correspondence by expectation maximization. Image Vis Comput. 2004;22(11):883–98. https://doi.org/10.1016/j.imavis.2004.04.004.

    Article  Google Scholar 

  25. Narasimha-Iyer H, Can A, Roysam B, Tanenbaum HL, Majerovics A. Integrated analysis of vascular and nonvascular changes from color retinal fundus image sequences. IEEE Trans Biomed Eng. 2007;54(8):1436–45. https://doi.org/10.1109/TBME.2007.900807.

    Article  PubMed  Google Scholar 

  26. Troglio G, Benediktsson JA, Moser G, Serpico SB, Stefansson E. Unsupervised change detection in multitemporal images of the human retina. In: Multi modality state-of-the-art medical image segmentation and registration methodologies, vol. 1. Boston, MA: Springer US; 2011. p. 309–37.

    Google Scholar 

  27. Reel PS, Dooley LS, Wong KCP, Börner A. Robust retinal image registration using expectation maximisation with mutual information. IEEE International Conference on Acoustics, Speech and Signal Processing. 2013. p. 1118–1122. https://doi.org/10.1109/ICASSP.2013.6637824.

  28. Cideciyan AV, Jacobson SG, Kemp CM, Knighton RW, Nagel JH. Registration of high resolution images of the retina. SPIE Med Imaging. 1652;1992:310–22. https://doi.org/10.1117/12.59439.

    Article  Google Scholar 

  29. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.

    Article  Google Scholar 

  30. Lin Y, Medioni G. Retinal image registration from 2D to 3D. IEEE Conference on Computer Vision and Pattern Recognition. 2008. p. 1–8. https://doi.org/10.1109/CVPR.2008.4587705.

  31. Hernandez-Matas C, Zabulis X, Argyros AA. Retinal image registration through simultaneous camera pose and eye shape estimation. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016. p. 3247–51. https://doi.org/10.1109/EMBC.2016.7591421.

  32. Rublee E, Rabaud V, Konolige K, Bradski G. ORB: an efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision. 2011. p. 2564–71. https://doi.org/10.1109/ICCV.2011.6126544.

  33. Suthaharan S, Rossi EA, Snyder V, Chhablani J, Lejoyeux R, Sahel J-A, Dansingani K. Laplacian feature detection and feature alignment for multi-modal ophthalmic image registration using phase correlation and Hessian affine feature space. Sig Process. 2020; https://doi.org/10.1016/j.sigpro.2020.107733.

  34. Zago GT, Andreão RV, Dorizzi B, Salles EOT. Retinal image quality assessment using deep learning. Comp Biol Med. 2018;103:64–70. ISSN 0010-4825. https://doi.org/10.1016/j.compbiomed.2018.10.004.

    Article  Google Scholar 

  35. Mahapatra D, Roy PK, Sedai S, Garnavi R. Retinal image quality classification using saliency maps and CNNs. In: International Workshop on Machine Learning in Medical Imaging. Springer; 2016. p. 172–9.

    Chapter  Google Scholar 

  36. Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. 2019. arXiv preprint arXiv:1904.08764.

    Google Scholar 

  37. Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med. 2019;2(1):1–9.

    Article  Google Scholar 

  38. Lam C, Yi D, Guo M, Lindsey T. Automated detection of diabetic retinopathy using deep learning. AMIA Summits Transl Sci Proc. 2018;2018:147.

    PubMed Central  Google Scholar 

  39. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernetics. 1979;9(1):62–6. https://doi.org/10.1109/TSMC.1979.4310076.

    Article  Google Scholar 

  40. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland

    Tomasz Krzywicki

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  1. Tomasz Krzywicki
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Correspondence to Tomasz Krzywicki .

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Editors and Affiliations

  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

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Krzywicki, T. (2021). Selected Image Analysis Methods for Ophthalmology. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-78601-4_6

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