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An unsupervised learning approach based on a Hopfield-like network for assessing posterior capsule opacification

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Abstract

Posterior capsule opacification (PCO) is the most common complication of cataract surgery, occurring in up to 50% of patients by 2–3 years after the operation [Spalton in Eye 13(Pt 3b):489–492, 1999]. This paper proposes a new approach for the assessment of PCO digital images. The approach deploys an unsupervised learning technique for clustering image pixels into different regions based on chromatic attributes. The innovative aspect of this paper lies in proposing the number of regions in a clustered image as a measurement tool for assessing the PCO. Experiments using synthetic data confirmed the plausibility of this approach. A series of experiments conducted on real PCO images demonstrated the robustness and stability of the proposed algorithm. Finally, the comparison of our method’s assessment with medical expert evaluation reveals a very reasonable concordance.

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Acknowledgments

We would like to thank Dr. Tariq Aslam from the Manchester Eye Hospital, UK, for providing the PCO images and Dr. P. Bhatia, Dr. A. Rao, Dr. P. Warhekar, Dr K.H. Sathish and Dr. B.S. Chidamber from the Ophthalmology Department at Welcare Hospital, for their kind collaboration and feedback.

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Authors and Affiliations

  1. Department of Computer Engineering, Khalifa University for Sciences, Technology and Research, Sharjah, UAE

    Naoufel Werghi

  2. Department of Computer Sciences, University of Sharjah, Sharjah, UAE

    Rachid Sammouda & Fatma AlKirbi

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  1. Naoufel Werghi
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  2. Rachid Sammouda
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  3. Fatma AlKirbi
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Correspondence to Rachid Sammouda.

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Werghi, N., Sammouda, R. & AlKirbi, F. An unsupervised learning approach based on a Hopfield-like network for assessing posterior capsule opacification. Pattern Anal Applic 13, 383–396 (2010). https://doi.org/10.1007/s10044-010-0181-y

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  • DOI: https://doi.org/10.1007/s10044-010-0181-y

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