A Study on Retinal Image Segmentation and Registration Methods

Authors

  • B. Sivaranjani  Ph.D Scholar, Tiruppur Kumaran College for Women, Tiruppur, Tamil Nadu, India and Assistant Professor, Department of Information Technology, Dr. N. G. P. Arts and Science college, Tamil Nadu, India
  • Dr. C. Kalaiselvi  Head and Professor, Department of Computer Applications, Tiruppur Kumaran College for Women, Tiruppur, Tamil Nadu, India

DOI:

https://doi.org/10.32628/CSEIT21713

Keywords:

Retinal images, Registration, Segmentation, Motion Parameter Estimation, Real Time Tracking, template matching

Abstract

Diagnosis and treatment of several disorders affecting the retina and the choroid behind it require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image template matching is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. This paper introduces a survey for the image preprocessing, dimensionality reduction, template matching and registration techniques that were reported as being well for retinal images.

References

  1. B. JL. “Photo physical Processes in Recent Medical Laser Developments”. Lasers Med Sci., vol. 1, pp.47-66. 1986.
  2. B. GM. “Lasers in Medicine and Surgery”, JAMA, vol. 256, pp. 900-907, 1986.
  3. N. M. Bressler, S.B. Bressler, and E.S. Gragoudas, “Clincal characteristics of choroidal neovascular membranes,” Arch. Ophthalmol., vol. 105, pp. 209-213, 1987.
  4. P. N. Monahan, K. A. Gitter, and G. Cohen, “Evaluation of Persistence of Subretinal Neovascular Membranes Using Digitized Angiographic Analysis,” Retina-J. Retinal, Vitreous Diseases, vol. 13, pp. 196-201, 1993.
  5. S. Fine, “Observations Following Laser Treatment for Choroidal Neovascularization,” Arch. Ophthalm., vol. 106, pp. 1524-1525, 1988.
  6. Marco Foracchia, Enrico Grison and Alfredo Ruggeri, ‘Luminosity and Contrast Normalization in Retinal Images’, Medical Image Analysis (Elsevier), Vol. 9, 2005, pp 179-190.
  7. Youssif, Aliaa & Ghalwash, Atef & Ghoneim, Amr. (2006). Comparative Study of Contrast Enhancement and Illumination Equalization Methods for Retinal Vasculature Segmentation. Cairo International Biomedical Engineering Conference (CIBEC).
  8. Peng Feng , Ying-jun Pan, Biao Wei, Wei Jin and Deling Mi , ‘Enhancing Retinal Image by the Contourlet Transform’, Pattern Recognition Letters ( Elsevier) , Vol. 28, 2007, pp 516-522.
  9. Salvatelli A., Bizai G., Barbosa G., Drozdowicz and Delrieux , ‘A Comparative Analysis of Pre-processing Techniques in Color Retinal Images’, Journal of Physics: Conference series 90, 2007
  10. George K.M., Pantelis A.A., Konstantinos K.D., Nikolaos A.M., Thierry G.Z., ‘Detection of Glaucomatous Change Based on Vessel Shape Analysis’, Computerized Medical Imaging and Graphics, Vol. 32, 2008, pp 183-192.
  11. Joshi, Gopal Datt, and Jayanthi Sivaswamy. "Colour retinal image enhancement based on domain knowledge." 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE, 2008.
  12. Yuan, Y., & Chung, A. C. (2008, August). Multi-scale model-based vessel enhancement using local line integrals. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2225-2228). IEEE.
  13. Jian Chen, Jie Tian, Zichun Tang, Jian Xue, Yakang Dai, and Jian Zheng. "Retinal vessel enhancement and extraction based on directional field." Journal of X-Ray Science and Technology 16, no. 3 (2008): 189-201.
  14. Farnell, Damian JJ, F. N. Hatfield, P. Knox, M. Reakes, S. Spencer, D. Parry, and Simon P. Harding. "Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators." Journal of the Franklin institute345, no. 7 (2008): 748-765.
  15. Zadeh L.A, Fuzzy sets. Inf. Control (8), (1965), pp. 338–353.
  16. A Sathya, Anudevi Samuel, and M.S. Sheeba, Robust Fuzzy C-Means based Minimal Spanning tree method For Segmentation of Breast MRI, International Conference on Mathematical Sciences, Elsevier Publications, (2014) pp. 495-501.
  17. Ye Xing et al., Simultaneous Estimation and Segmentation of T1 Map for Breast parenchyma Measurement, 4th IEEE International Symposium on Biomedical Imaging, (2007),pp. 332 – 335.
  18. S. Ramathilagam et al., Journal of Intelligent and Fuzzy Systems, (2014) 27(5): 2573-2595.
  19. X. Zhang, G. Thibault, E. Decenci`ere, B. Marcotegui, B. La¨y, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin et al., “Exudate detection in color retinal images for mass screening of diabetic retinopathy,”Medical image analysis, vol. 18, no. 7, pp. 1026–1043, 2014.
  20. A. D. Mora, J. Soares, and J. M. Fonseca, “A template matching technique for artifacts detection in retinal images,” in Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on. IEEE, 2013, pp. 717–722.
  21. C. V. Stewart, C.-L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE transactions on medical imaging, vol. 22, no. 11, pp. 1379–1394, 2003.
  22. Y. Wang, J. Shen, W. Liao, and L. Zhou, “Automatic fundus images mosaic based on sift feature,” in Image and Signal Processing (CISP), 2010 3rd International Congress on, vol. 6. IEEE, 2010, pp. 2747–2751.
  23. C. Hernandez-Matas, X. Zabulis, and A. A. Argyros, “Retinal image registration based on keypoint correspondences, spherical eye modeling and camera pose estimation,” in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 2015, pp. 5650–5654.
  24. K. J. Friston, J. Ashburner, C. D. Frith, J.-B. Poline, J. D. Heather, and R. S. Frackowiak, “Spatial registration and normalization of images,” Human brain mapping, vol. 3, no. 3, pp. 165–189, 1995.
  25. A. V. Cideciyan, “Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 1, pp. 52–58, 1995.
  26. Y.-M. Zhu, “Mutual information-based registration of temporal and stereo retinal images using constrained optimization,” Computer methods and programs in biomedicine, vol. 86, no. 3, pp. 210–215, 2007.
  27. Lamminen et al., 2003 H. Lamminen, V. Voipio, K. Ruohonen and H. Uusitalo, Telemedicine in ophthalmology, Acta Ophthalmol. Scand. 81 (2003), pp. 105–109. Abstract-MEDLINE | Abstract-EMBASE | Full Text via CrossRef
  28. Yogesan et al., 2000 K. Yogesan, M. Cuypers, C. Barry, I. Constable and L. Jitskaia, Tele-ophthalmology screening for retinal and anterior segment diseases, J. Telemed. Telecare 6 (2000) (Suppl. 1), pp. S96–S98. Abstract-MEDLINE
  29. Yogesan et al., 2001 K. Yogesan, C. Henderson, C. Barry and I. Constable, Online eye care in prisons in Western Australia, J. Telemed. Telecare 7 (2001) (Suppl. 2), pp. 63–64.
  30. Kawasaki et al., 2003 S. Kawasaki, S. Ito, Y. Mori, T. Saito, H. Fukushima, S. Kato and H. Sekihara, Use of telemedicine in periodic screening of diabetic retinopathy, Telemed. J. E Health 9 (2003), pp. 235–239.
  31. Lin et al., 2002 D. Lin, M. Blumenkranz, R. Brothers and D. Grosvenor, The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography, Am. J. Ophthalmol. 134 (2002), pp. 204–213. SummaryPlus | Full Text + Links | PDF (175 K)
  32. Luzio et al., 2004 S. Luzio, S. Hatcher, G. Zahlmann, L. Mazik, M. Morgan, B. Liesenfeld, T. Bek, H. Schuell, S. Schneider and D. Owens et al., Feasibility of using the TOSCA telescreening procedures for diabetic retinopathy, Diabet. Med. 21 (2004), pp. 1121–1128.
  33. Yen et al., 2002 K. Yen, D. Hess, B. Burke, R. Johnson, W. Feuer and J. Flynn, Telephotoscreening to detect retinopathy of prematurity: preliminary study of the optimum time to employ digital fundus camera imaging to detect ROP, J. AAPOS 6 (2002), pp. 64–70. Abstract | PDF (174 K)
  34. Eikelboom et al., 2000 R. Eikelboom, K. Yogesan, C. Barry, I. Constable, M. Tay-Kearney, L. Jitskaia and P. House, Methods and limits of digital image compression of retinal images for telemedicine, Invest. Ophthalmol. Vis. Sci. 41 (2000), pp. 1916–1924.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-02-28

Issue

Volume 7, Issue 1, January-February-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Sivaranjani, Dr. C. Kalaiselvi, " A Study on Retinal Image Segmentation and Registration Methods" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 1, pp.25-33, January-February-2021. Available at doi : https://doi.org/10.32628/CSEIT21713