Title: Improving microaneurysm detection from non-dilated diabetic retinopathy retinal images using feature optimisation

Authors: Akara Thammastitkul; Bunyarit Uyyanonvara; Sarah A. Barman

Addresses: Faculty of Humanities and Social Sciences, Burapha University, 169 Long-Hard Bangsaen Road, Saensook Municipality, Muang District, Chonburi 20131, Thailand ' Department of Information Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand ' Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK

Abstract: Diabetic retinopathy usually does not presents symptoms in an early stage until it gets to a severe stage. An early stage of diabetic retinopathy is associated with the presence of microaneurysms (MAs). The occurrence of blindness can be reduced significantly if MAs are detected. This paper presented an approach to improve automatic MAs detection using feature optimisation. Candidate MAs are detected using mathematic morphological techniques. Originally 20 features are presented. To verify the relevance of all original features, a feature optimisation process is performed. The optimal feature set is searched by a machine learning approach, like naïve Bayes and support vector machine classifier. Hand-drawn ground-truth images from expert ophthalmologists are used to measure the performance evaluation. The results showed that the proposed optimal feature set could significantly improve MA detection.

Keywords: diabetic retinopathy; microaneurysms; machine learning approach; feature optimisation.

DOI: 10.1504/IJCAET.2020.106238

International Journal of Computer Aided Engineering and Technology, 2020 Vol.12 No.3, pp.355 - 369

Received: 30 Sep 2017
Accepted: 01 May 2018
Published online: 02 Apr 2020 *

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