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Cataract Detection and Classification Systems Using Computational Intelligence: A Survey

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Abstract

One of the most powerful tools in image processing solving complex problems is computational intelligence. There are several image classification methods but is uncertain which methods are most helpful for analyzing and classify images like ophthalmic. Particularly in the area of cataract, as a leading cause of blindness around the globe, only a few comprehensive reviews have summarized the ongoing efforts of computational intelligence in cataract detection and grading. By timely detection, it is possible to prevent cataract surgery in the initial stage of it. In this work, we compare the main characteristics of different algorithms in grading and classification, going from the classical medical methods to the actuals based on computational intelligence. These methods are explained in a simple manner trying to provide a mean to understand the operating principles and summarizing their relevant applications. This review may be considered as a useful guide for researchers and medicians in selecting a suitable method for improving cataract detection and grading, and to assist them in diagnosing this ophthalmic disease.

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References

  1. Kirkpatrick H (1922) The etiology of primary cataract. Br Med J 1(3195):467

    Article  Google Scholar 

  2. Ramlee RA, Ramli AR, Sulaiman HA (2015) A review on diseases manifestation by ocular diseases using computer aided diagnosis (cad). Int J Eng Technol 7(4):1343–1348

    Google Scholar 

  3. Zhou Z, Huang C-C, Shung KK, Tsui P-H, Fang J, Ma H-Y, Wu S, Lin C-C (2014) Entropic imaging of cataract lens: an in vitro study. PLoS ONE 9(4):e96195

    Article  Google Scholar 

  4. Davison JA, Chylack LT Jr (2003) Clinical application of the lens opacities classification system iii in the performance of phacoemulsification. J Cataract Refract Surg 29(1):138–145

    Article  Google Scholar 

  5. Falcão MS, Gonçalves NM, Freitas-Costa P, Beato JB, Rocha-Sousa A, Carneiro A, Brandão EM, Falcão-Reis FM (2014) Choroidal and macular thickness changes induced by cataract surgery. Clin Ophthalmol 8:55–60

    Google Scholar 

  6. Brian G, Taylor H (2001) Cataract blindness: challenges for the 21st century. Bull World Health Organ 79(3):249–256

    Google Scholar 

  7. He W, Goodkind D, Kowal P (2016, 2015) An aging world. US Census Bureau, editor. International Population Reports

  8. Supriyanti R, Habe H, Kidode M, Nagata S (2009) Compact cataract screening system: design and practical data acquisition. In: 2009 International conference on instrumentation, communications, information technology, and biomedical engineering (ICICI-BME). IEEE, pp 1–6

  9. KachewaR SSG, KulKaRni DDSS (2014) An imaging review of intra-ocular calcifications. J Clin Diagn Res JCDR 8(1):203

    Google Scholar 

  10. Gao X, Lin S, Wong TY (2015) Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 62(11):2693–2701

    Article  Google Scholar 

  11. Trokielewicz M, Czajka A, Maciejewicz P (2014) Cataract influence on iris recognition performance. In: Symposium on photonics applications in astronomy, communications, industry and high-energy physics experiments. International Society for Optics and Photonics, pp 929020–929020

  12. Klein BEK, Klein R, Linton KLP, Magli YL, Neider MW (1990) Assessment of cataracts from photographs in the beaver dam eye study. Ophthalmology 97(11):1428–1433

    Article  Google Scholar 

  13. Foster A, Johnson GJ (1990) Magnitude and causes of blindness in the developing world. Int Ophthalmol 14(3):135–140

    Article  Google Scholar 

  14. Hall AB, Thompson JR, Deane JS, Rosenthal AR (1997) Locs iii versus the oxford clinical cataract classification and grading system for the assessment of nuclear, cortical and posterior subcapsular cataract. Ophthalmic Epidemiol 4(4):179–194

    Article  Google Scholar 

  15. Huang W, Li H, Chan KL, Lim JH, Liu J, Wong TY (2009) A computer-aided diagnosis system of nuclear cataract via ranking. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 803–810

  16. Li H, Lim JH, Liu J, Mitchell P, Tan AG, Wang JJ, Wong TY (2010) A computer-aided diagnosis system of nuclear cataract. IEEE Trans Biomed Eng 57(7):1690–1698

    Article  Google Scholar 

  17. Khan A, Li J-P, Shaikh RA (2015) Medical image processing using fuzzy logic. In: 2015 12th International computer conference on wavelet active media technology and information processing (ICCWAMTIP). IEEE, pp 163–167

  18. Huang W, Chan KL, Li H, Lim JH, Liu J, Wong TY (2011) A computer assisted method for nuclear cataract grading from slit-lamp images using ranking. IEEE Trans Med Imaging 30(1):94–107

    Article  Google Scholar 

  19. Kolhe S, Guru SK (2015) Cataract classiication and grading: a survey. Int J Innov Res Comput Commun Eng 3(11):10749–10755

    Google Scholar 

  20. Sreejaya MK, Vijayan A, Krishnan A, Sreedharan D (2017) Various cataract detection methods-a survey. Int Res J Eng Technol 4:1517–1519

    Google Scholar 

  21. Chylack LT, Leske MC, McCarthy D, Khu P, Kashiwagi T, Sperduto R (1989) Lens opacities classification system ii (locs ii). Arch Ophthalmol 107(7):991–997

    Article  Google Scholar 

  22. Sparrow JM, Bron AJ, Brown NAP, Ayliffe W, Hill AR (1986) The oxford clinical cataract classification and grading system. Int Ophthalmol 9(4):207–225

    Article  Google Scholar 

  23. Chylack LT, Wolfe JK, Singer DM, Leske MC, Bullimore MA, Bailey IL, Friend J, McCarthy D, Wu S-Y (1993) The lens opacities classification system iii. Arch Ophthalmol 111(6):831–836

    Article  Google Scholar 

  24. Goldmann H, Niesel P (1964) Studien über die abspaltungsstreifen und das linsenwachstum. Ophthalmologica 147(2):134–142

    Article  Google Scholar 

  25. Newhall SM, Nickerson D, Judd DB (1943) Final report of the osa subcommittee on the spacing of the munsell colors. josa 33(7):385–418

    Article  Google Scholar 

  26. Colour M (1991) Munsell soil colour charts. Geoderma 48(199):199

    Google Scholar 

  27. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  28. Li H, Lim JH, Liu J, Wong DWK, Wong TY (2008) Computer aided diagnosis of nuclear cataract. In: 3rd IEEE Conference on industrial electronics and applications, 2008. ICIEA 2008. IEEE, pp 1841–1844

  29. Caixinha M, Velte E, Santos M, Santos JB (2014) New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers. In: 2014 IEEE international ultrasonics symposium (IUS). IEEE, pp 2402–2405

  30. Caxinha M, Velte E, Santos M, Perdigão F, Amaro J, Gomes M, Santos J (2015) Automatic cataract classification based on ultrasound technique using machine learning: a comparative study. Phys Procedia 70:1221–1224

    Article  Google Scholar 

  31. Yang J-J, Li J, Shen R, Zeng Y, He J, Bi J, Li Y, Zhang Q, Peng L, Wang Q (2016) Exploiting ensemble learning for automatic cataract detection and grading. Comput Methods Programs Biomed 124:45–57

    Article  Google Scholar 

  32. Fausett LV et al (1994) Fundamentals of neural networks: architectures, algorithms, and applications, vol 3. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  33. Yang M, Yang J-J, Zhang Q, Niu Y, Li J (2013) Classification of retinal image for automatic cataract detection. In: 2013 IEEE 15th international conference on e-Health networking, applications and services (Healthcom). IEEE, pp 674–679

  34. Harini V, Bhanumathi V (2016) Automatic cataract classification system. In: 2016 International conference on communication and signal processing (ICCSP). IEEE, pp 0815–0819

  35. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  36. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. arXiv preprint arXiv:1702.05747

  37. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312

    Article  Google Scholar 

  38. Salla KR, Kadam SS (2010) A fuzzy inference system based approach for detection, classification and grading of cataract. Int J Comput Sci Appl Issue 47:18–23

    Google Scholar 

  39. Gao X, Wong DWK, Aryaputera AW, Sun Y, Cheng C-Y, Cheung C, Wong TY (2012) Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4434–4437

  40. Marin D, Gegundez-Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Programs Biomed 118(2):173–185

    Article  Google Scholar 

  41. Li H, Gao X, Tan MH, Chow YC, Lim JH, Sun Y, Cheung CC, Wong TY (2011) Lens image registration for cataract detection. In: 2011 6th IEEE conference on industrial electronics and applications (ICIEA). IEEE, pp 132–135

  42. Li H, Lim JH, Liu J, Wong TY (2007) Towards automatic grading of nuclear cataract. In: 29th Annual international conference of the IEEE engineering in medicine and biology society, 2007. EMBS 2007. IEEE, pp 4961–4964

  43. Li H, Ko L, Lim JH, Liu J, Wong DWK, Wong TY, Sun Y (2008) Automatic opacity detection in retro-illumination images for cortical cataract diagnosis. In: 2008 IEEE international conference on multimedia and expo. IEEE, pp 553–556

  44. Li H, Ko L, Lim JH, Liu J, Wong DWK, Wong TY (2008) Image based diagnosis of cortical cataract. In: 30th Annual international conference of the IEEE engineering in medicine and biology society, 2008. EMBS 2008. IEEE, pp 3904–3907

  45. Li H, Lim JH, Liu J, Wong DWK, Tan NM, Lu S, Zhang Z, Wong TY (2009) An automatic diagnosis system of nuclear cataract using slit-lamp images. In: Annual international conference of the IEEE engineering in medicine and biology society, 2009. EMBC 2009. IEEE, pp 3693–3696

  46. Li H, Lim JH, Liu J, Wong DWK, Tan NM, Lu S, Zhang Z, Wong TY (2009) Computerized systems for cataract grading. In: 2nd International conference on biomedical engineering and informatics, 2009. BMEI’09. IEEE, pp 1–4

  47. Acharya RU, Yu W, Zhu K, Nayak J, Lim T-C, Chan JY (2010) Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst 34(4):619–628

    Article  Google Scholar 

  48. Li H, Lim JH, Liu J, Wong DWK, Foo Y, Sun Y, Wong TY (2010) Automatic detection of posterior subcapsular cataract opacity for cataract screening. In: 2010 Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5359–5362

  49. Gao X, Li H, Lim JH, Wong TY (2011) Computer-aided cataract detection using enhanced texture features on retro-illumination lens images. In: 2011 18th IEEE international conference on image processing (ICIP). IEEE, pp 1565–1568

  50. Song W, Wang P, Zhang X, Wang Q (2016) Semi-supervised learning based on cataract classification and grading. In: 2016 IEEE 40th annual computer software and applications conference (COMPSAC), vol 2. IEEE, pp 641–646

  51. Morales-Lopez HI, Cruz-Vega I, Ramirez-Cortes JM, Peregrina-Barreto H, Rangel-Magdaleno J (2018) Som-like neural network and differential evolution for multi-level image segmentation and classification in slit-lamp images. In: IEEE Colombian conference on applications in computational intelligence. Springer, pp 26–37

  52. Bhadra AA, Jain M, Shidnal S (2016) Automated detection of eye diseases. In: International conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 1341–1345

  53. Pathak S, Kumar B (2016) A robust automated cataract detection algorithm using diagnostic opinion based parameter thresholding for telemedicine application. Electronics 5(3):57

    Article  Google Scholar 

  54. Jagadale AB, Jadhav DV (2016) Early detection and categorization of cataract using slit-lamp images by hough circular transform. In: 2016 International conference on communication and signal processing (ICCSP). IEEE, pp 0232–0235

  55. Pamplona VF, Passos EB, Zizka J, Oliveira MM, Lawson E, Clua E, Raskar R (2011) Catra: interactive measuring and modeling of cataracts. In: ACM transactions on graphics (TOG), vol 30. ACM, pp 47

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Morales-Lopez, H., Cruz-Vega, I. & Rangel-Magdaleno, J. Cataract Detection and Classification Systems Using Computational Intelligence: A Survey. Arch Computat Methods Eng 28, 1761–1774 (2021). https://doi.org/10.1007/s11831-020-09440-2

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