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

Mookiah, Muthu Rama Krishnan, Acharya, U. Rajendra, Chandran, Vinod, Martis, Roshan Joy, Tan, Jen Hong, Koh, Joel E.W., Chua, Chua Kuang, Tong, Louis, & Laude, Augustinus (2015) Application of higher-order spectra for automated grading of diabetic maculopathy. Medical and Biological Engineering and Computing, 53(12), pp. 1319-1331.

View at publisher

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.

Impact and interest:

3 citations in Scopus
1 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

6 since deposited on 13 May 2015
5 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 84131
Item Type: Journal Article
Refereed: Yes
Keywords: Retina, Diabetic maculopathy, Higher-order spectra, Spectral regression discriminant analysis, Computer-aided diagnosis
DOI: 10.1007/s11517-015-1278-7
ISSN: 1741-0444
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 by Springer Berlin Heidelberg
Deposited On: 13 May 2015 22:57
Last Modified: 04 Jan 2017 02:05

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page