Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine. Issue 6 (18th February 2019)

• Record Type: Journal Article
• Authors: Sreng, Syna; Maneerat, Noppadol; Hamamoto, Kazuhiko; Panjaphongse, Ronakorn
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 14:Issue 6(2019)
• Page Start: 884
• Page End: 893
• Publication Date: 2019-02-18
• Subjects: cotton wool spots -- diabetic retinopathy -- retinal image -- adaptive thresholding -- ant colony optimization -- support vector machine; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22878
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : Diabetic retinopathy is the major issue of diabetes‐induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One‐hundred and sixty‐two features from five feature sets, namely morphologies, first‐order statistics, gray‐level co‐occurrence matrix, gray‐level run length matrix, and lacunarity, are extracted, and then four feature selection methods, namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high‐resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.