Abstract
Cataract is the most common ocular disease mainly developed during old age. It occurs due to the buildup of protein at lens over a long period of time which makes the lens cloudy. Early and accurate diagnosis of cataract helps prevent vision loss. To alleviate the burden of ophthalmologist, many researchers working in the field of biomedical imaging developed a number of techniques for the automatic detection and grading of cataract. Imaging modalities used for this purpose includes slit-lamp images, retro-illumination images, digital/optical eye images, retinal images, and ultrasonic Nakagami images. In this paper, we review cataract detection and grading methodologies using these imaging modalities. For each imaging type, we analyze the possible methods and techniques applied so far. We also investigated the advantages and shortcomings of these techniques and methods and suggested the ways to improve the existing methods.
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References
Zhang, Z., Srivastava, R., Liu, H., Chen, X., Duan, L., Wong, D. W. K., Kwoh, C. K., Wong, T. Y., & Liu, J. (2014). A survey on computer aided diagnosis for ocular diseases. BMC Medical Informatics and Decision Making. Retrieved from http://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-14-80
Shirole, T. (2014, November 4). About Cataract. Retrieved from http://www.medindia.net/patients/patientinfo/cataract.htm
Kinard, E. T. A Closer Look at Cataract. Retrieved from http://www.athenseyecare.net/conditions/cataracts/?
All 3 Article What is cataract? (2010, August 4). Retrieved from http://www.parentyourparents.com/pyp_article/cataracts/?
Seddon, J., Fong, D., West, S. K., & Valmadrid, C. T. (1995). Epidemiology of risk factors for age-related cataract. Survey of Ophthalmology, 39(4), 323–334.
Pizzarello, L., Abiose, A., Ffytche, T., Duerksen, R., Thulasiraj, R., Taylor, H., & Resnikoff, S. (2004). VISION 2020: The right to sight: A global initiative to eliminate avoidable blindness. Archives of Ophthalmology, 122(4), 615–620.
Figure 1 Normal Vs Cataract Vision. Retrieved from http://www.eyecenter.com.ph/what-we-do.html#ripen. ©Copyright 2011. American Eye Center.
Delcourt, C., Cristol, J. P., Tessier, F., Léger, C. L., Michel, F., Papoz, L., & POLA Study Group. (2000). Risk factors for cortical, nuclear, and posterior sub-capsular cataracts: the POLA study. American Journal of Epidemiology, 151(5), 497–504.
Chylack, L. T., Wolfe, J. K., Singer, D. M., Leske, M. C., Bullimore, M. A., Bailey, I. L., Friend, J., McCarthy, D., & Wu, S. Y. (1993). The lens opacities classification system III. Archives of Ophthalmology, 111(6), 831–836.
Panchapakesan, J., Cumming, R. G., & Mitchell, P. (1997). Reproducibility of the Wisconsin cataract grading system in the Blue Mountains Eye Study. Ophthalmic Epidemiology, 4(3), 119–126.
Li, H., Lim, J. H., Liu, J., Wong, D. W. K., Tan, N. M., Lu, S., Zhang, Z., & Wong, T. Y. (2009, September). An automatic diagnosis system of nuclear cataract using slit-lamp images. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual international conference of the IEEE (pp. 3693–3696). IEEE.
Srivastava, R., Gao, X., Yin, F., Wong, D. W., Liu, J., Cheung, C. Y., & Wong, T. Y. (2014). Automatic nuclear cataract grading using image gradients. Journal of Medical Imaging, 1(1), 014502–014502.
Gao, X., Lin, S., & Wong, T. Y. (2015). Automatic feature learning to grade nuclear cataracts based on deep learning. Biomedical engineering, IEEE transactions on, 62(11), 2693–2701.
Jagadale, A. B., & Jadhav, D. V. (2016, April). Early detection and categorization of cataract using slit-lamp images by hough circular transform. In Communication and Signal Processing (ICCSP), 2016 international conference on (pp. 0232–0235). IEEE.
Liu, X., Jiang, J., Zhang, K., Long, E., Cui, J., Zhu, M., An, Y., Zhang, J., Liu, Z., Lin, Z., & Li, X. (2017). Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PloS one, 12(3), e0168606.
Chow, Y. C., Gao, X., Li, H., Lim, J. H., Sun, Y., & Wong, T. Y. (2011, August). Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images. In Engineering in Medicine and Biology Society, EMBC, 2011 annual international conference of the IEEE (pp. 5044–5047). IEEE.
Gao, X., Wong, D. W. K., Aryaputera, A. W., Sun, Y., Cheng, C. Y., Cheung, C., & Wong, T. Y. (2012, August). Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system. In Engineering in Medicine and Biology Society (EMBC), 2012 annual international conference of the IEEE (pp. 4434–4437). IEEE.
Zhang, W., & Li, H. (2017). Lens opacity detection for serious posterior subcapsular cataract. Medical & Biological Engineering & Computing, 55(5), 769–779.
Akram, M. U., Tariq, A., Khan, S. A., & Javed, M. Y. (2014). Automated detection of exudates and macula for grading of diabetic macular edema. Computer Methods and Programs in Biomedicine, 114(2), 141–152.
Akram, M. U., Khalid, S., Tariq, A., Khan, S. A., & Azam, F. (2014). Detection and classification of retinal lesions for grading of diabetic retinopathy. Computers in Biology and Medicine, 45, 161–171.
Akram, M. U., Khalid, S., Tariq, A., & Javed, M. Y. (2013). Detection of neovascularization in retinal images using multivariate m-Mediods based classifier. Computerized Medical Imaging and Graphics, 37(5), 346–357.
Yang, M., Yang, J. J., Zhang, Q., Niu, Y., & Li, J. (2013, October). Classification of retinal image for automatic cataract detection. In e-Health Networking, Applications & Services (Healthcom), 2013 IEEE 15th international conference on (pp. 674–679). IEEE.
Guo, L., Yang, J. J., Peng, L., Li, J., & Liang, Q. (2015). A computer-aided health-care system for cataract classification and grading based on fundus image analysis. Computers in Industry, 69, 72–80.
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. Computer Methods and Programs in Biomedicine, 124, 45–57.
Xiong, L., Li, H., & Xu, L. (2017). An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis. Journal of Healthcare Engineering, 2017, 5645498.
Jamal, A., Hazim Alkawaz, M., Rehman, A., & Saba, T. (2017). Retinal imaging analysis based on vessel detection. Microscopy Research and Technique., 80(7), 799–811.
Supriyanti, R., & Ramadhani, Y. (2011, June). The Achievement of Various Shapes of Specular Reflections for Cataract Screening System Based on Digital Images. In International Conference on Biomedical Engineering and Technology (ICBET). Kualalumpur, Malaysia.
Patwari, M. A. U., Arif, M. D., Chowdhury, M. N., Arefin, A., & Imam, M. I. (2011). Detection, categorization, and assessment of eye cataracts using digital image processing. In The first international conference on interdisciplinary research and development, 31 May–1 June.
Fuadah, Y. N., Setiawan, A. W., & Mengko, T. L. R. (2015, May). Performing high accuracy of the system for cataract detection using statistical texture analysis and K-Nearest Neighbor. In Intelligent Technology and Its Applications (ISITIA), 2015 international seminar on (pp. 85–88). IEEE.
Tsui, P. H., Huang, C. C., Chang, C. C., Wang, S. H., & Shung, K. K. (2007). Feasibility study of using high-frequency ultrasonic Nakagami imaging for characterizing the cataract lens in vitro. Physics in Medicine and Biology, 52(21), 6413.
Caixinha, M., Jesus, D. A., Velte, E., Santos, M. J., & Santos, J. B. (2014). Using ultrasound backscattering signals and Nakagami statistical distribution to assess regional cataract hardness. Biomedical Engineering, IEEE Transactions on, 61(12), 2921–2929.
Caixinha, M., Velte, E., Santos, M., & Santos, J. B. (2014, September). New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers. In Ultrasonics Symposium (IUS), 2014 IEEE International (pp. 2402–2405). IEEE.
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. Physics Procedia, 70, 1221–1224.
Pathak, S., & Kumar, B. (2016). A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application. Electronics, 5(3), 57.
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Shaheen, I., Tariq, A. (2019). Survey Analysis of Automatic Detection and Grading of Cataract Using Different Imaging Modalities. In: Khan, F., Jan, M., Alam, M. (eds) Applications of Intelligent Technologies in Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-96139-2_4
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DOI: https://doi.org/10.1007/978-3-319-96139-2_4
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