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Localising Microaneurysms in Fundus Images Through Singular Spectrum Analysis

Wang, S, Tang, HL, Al turk, LI, Hu, Y, Sanei, S, Saleh, GM and Peto, T (2016) Localising Microaneurysms in Fundus Images Through Singular Spectrum Analysis IEEE Transactions on Biomedical Engineering (TBME)..

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Abstract

Goal: Reliable recognition of microaneurysms is an essential task when developing an automated analysis system for diabetic retinopathy detection. In this work, we propose an integrated approach for automated microaneurysm detection with high accuracy. Methods: Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical microaneurysm profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true microaneurysms and other non-microaneurysm candidates. A set of statistical features of those profiles is then extracted for a K-Nearest Neighbour classifier. Results: Experiments show that by applying this process, microaneurysms can be separated well from the retinal background, the most common interfering objects and artefacts. Conclusion: The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity. Significance: The approach proposed in the evaluated system has great potential when used in an automated diabetic retinopathy screening tool or for large scale eye epidemiology studies.

Item Type: Article
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Wang, SUNSPECIFIEDUNSPECIFIED
Tang, HLUNSPECIFIEDUNSPECIFIED
Al turk, LIUNSPECIFIEDUNSPECIFIED
Hu, YUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Saleh, GMUNSPECIFIEDUNSPECIFIED
Peto, TUNSPECIFIEDUNSPECIFIED
Date : 27 June 2016
Identification Number : 10.1109/TBME.2016.2585344
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Computer-aided diagnosis, image classification, microaneurysm detection, retinal image, singular spectrum analysis, diabetic retinopathy.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 29 Jun 2016 14:12
Last Modified : 04 Aug 2016 08:48
URI: http://epubs.surrey.ac.uk/id/eprint/811074

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