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Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images

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Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage. To counter this, regular screening for DR is essential in diabetic patients. Due to lack of enough skilled medical professionals, this task can become tedious as the number of images to be screened becomes high with regular screening of diabetic patients. An automated DR screening system can help in early diagnosis without the need for a large number of medical professionals. To improve detection, several pattern recognition techniques are being developed. In our study, we used trace transforms to model a human visual system which would replicate the way a human observer views an image. To classify features extracted using this technique, we used support vector machine (SVM) with quadratic, polynomial, radial basis function kernels and probabilistic neural network (PNN). Genetic algorithm (GA) was used to fine tune classification parameters. We obtained an accuracy of 99.41 and 99.12 % with PNN–GA and SVM quadratic kernels, respectively.

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Acknowledgments

The study was funded by the NHG CSCS/12006 grant.

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Authors and Affiliations

  1. Department of ECE, Ngee Ann Polytechnic, Clementi Road, Clementi, 599489, Singapore

    Karthikeyan Ganesan, Roshan Joy Martis, U. Rajendra Acharya, Chua Kuang Chua & Lim Choo Min

  2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang, 639798, Singapore

    E. Y. K. Ng

  3. National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Novena, 308433, Singapore

    Augustinus Laude

  4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

Authors
  1. Karthikeyan Ganesan
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  2. Roshan Joy Martis
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  3. U. Rajendra Acharya
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  4. Chua Kuang Chua
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  5. Lim Choo Min
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  6. E. Y. K. Ng
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  7. Augustinus Laude
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Correspondence to Karthikeyan Ganesan.

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Ganesan, K., Martis, R.J., Acharya, U.R. et al. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput 52, 663–672 (2014). https://doi.org/10.1007/s11517-014-1167-5

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  • DOI: https://doi.org/10.1007/s11517-014-1167-5

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