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Application of higher-order spectra for automated grading of diabetic maculopathy

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Abstract

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39 % for MESSIDOR dataset and 95.93 and 93.33 % for local dataset, respectively.

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Acknowledgments

Authors thank Social Innovation Research Fund (Project ID: T1202), Singapore, for providing grant for this research. Also authors would like to thank National Medical Research Council (NMRC/CSA/045/2012) and National Healthcare Group Clinician Scientist Career Scheme/12006 grant. We express our sincere thanks to Mr. Kevin Noronha, Manipal University, Manipal, India, for sharing the images for this study.

Conflict of interest

The authors do not have any related conflict of interest.

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore

    Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Jen Hong Tan, Joel E. W. Koh & Chua Kuang Chua

  2. Department of Biomedical Engineering, School of Science and Technology, SIM University, Clementi, 599491, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia

    U. Rajendra Acharya

  4. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia

    Vinod Chandran

  5. Department of Electronics and Communication Engineering, St. Joseph Engineering College, Vamanjoor, Mangalore, 575028, Karnataka, India

    Roshan Joy Martis

  6. Ocular Surface Research Group, Singapore Eye Research Institute, Singapore, 168751, Singapore

    Louis Tong

  7. Singapore National Eye Center, Singapore, 168751, Singapore

    Louis Tong

  8. Duke-NUS Graduate Medical School, Singapore, 169857, Singapore

    Louis Tong

  9. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore

    Louis Tong

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

    Augustinus Laude

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  1. Muthu Rama Krishnan Mookiah
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Correspondence to Muthu Rama Krishnan Mookiah.

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Mookiah, M.R.K., Acharya, U.R., Chandran, V. et al. Application of higher-order spectra for automated grading of diabetic maculopathy. Med Biol Eng Comput 53, 1319–1331 (2015). https://doi.org/10.1007/s11517-015-1278-7

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  • DOI: https://doi.org/10.1007/s11517-015-1278-7

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