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Identification of Cataract and Post-cataract Surgery Optical Images Using Artificial Intelligence Techniques

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Abstract

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

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Acknowledgements

Authors thank Ophthalmology department, Kasturba Medical College, Manipal, India for providing the eye images for this study.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    Rajendra Udyavara Acharya, Kuanyi Zhu & Joey Yiptong Chan

  2. Graduate School of Medical System Engineering, Chiba University, Chiba, Japan

    Wenwei Yu

  3. Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal, India

    Jagadish Nayak

  4. School of Science and Technology, SIM University (UniSIM), Singapore, Singapore

    Teik-Cheng Lim

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  1. Rajendra Udyavara Acharya
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  2. Wenwei Yu
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Correspondence to Rajendra Udyavara Acharya.

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Acharya, R.U., Yu, W., Zhu, K. et al. Identification of Cataract and Post-cataract Surgery Optical Images Using Artificial Intelligence Techniques. J Med Syst 34, 619–628 (2010). https://doi.org/10.1007/s10916-009-9275-8

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  • DOI: https://doi.org/10.1007/s10916-009-9275-8

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