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

Mookiah, M.R.K. and Acharya, U.R. and Chandran, V. and Martis, R.J. and Tan, J.H. and Koh, J.E.W. and Chua, C.K. and Tong, L. and Laude, A. (2015) Application of higher-order spectra for automated grading of diabetic maculopathy. Medical & Biological Engineering & Computing, 53 (12, SI). pp. 1319-1331. ISSN 0140-0118,

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Official URL: DOI: 10.1007/s11517-015-1278-7


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.

Item Type: Article
Uncontrolled Keywords: Retina; Diabetic maculopathy; Higher-order spectra; Spectral regression discriminant analysis; Computer-aided diagnosis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User: Mrs. Siti Mawarni Salim
Date Deposited: 29 Sep 2016 01:25
Last Modified: 29 Sep 2016 01:25

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