Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

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 sixtytwo 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%. , since 2009. He is currently a Dean of the School of Information and Telecommunication Engineering, Tokai University. His current research interests include information design. Especially, he is interested in medical information, human interface design, and virtual reality. He has about 60 papers published in Transactions and Journals and about 85 papers presented at international conferences. Prof. Hamamoto is a member of the IEEE and many national societies in Japan. He also takes active part in national societies as a committee member. RonakornPanjaphongse (Non-member) received the M.D. degree from the Faculty of Medicine Siriraj Hospital, Bangkok, Thailand, in 2001. He is currently the Head of the Retinal Unit, Ophthalmology Department, Bhumibol Adulyadej Hospital, Royal Thai Air Force, Bangkok, Thailand. His interests include studies related to retinal diseases, especially in diabetic retinopathy, including applying artificial intelligence for medical imaging.

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

“…Similarly, for EXs researchers relied on approaches like clustering (Osareh et al, 2009), model-based (Sánchez et al, 2009;Harangi and Hajdu, 2014), ant colony optimization (ACO) (Pereira et al, 2015) and contextual information (Sánchez et al, 2012). Whereas, for SEs researchers utilized Scale Invariant Feature Transform (SIFT) (Naqvi et al, 2018), adaptive thresholding and ACO (Sreng et al, 2019). Further, several approaches were devised for multiple lesion detection such as multiscale amplitude-modulation-frequency-modulation (Agurto et al, 2010), machine learning (Roychowdhury et al, 2014), a combination of Hessian multiscale analysis, variational segmentation and texture features (Figueiredo et al, 2015).…”

Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

“…Exudate detection using the Genetic Algorithm a fuzzy c means clustering utilizes retinal color information to identify variation in exudate color [69]. SVM with glowworm swarm and ant colony optimization can detect microaneurysms and cotton wool spots [70,71]. Artificial neural networks can identify hemorrhages, microaneurysms, hard exudates, and cotton wool spots [72].…”

Detecting Diabetic Retinopathy in Fundus Images using Combined Enhanced Green and Value Planes (CEGVP) with k-NN

“…In DR, blood vessels leak fluid and blood on the retina. These vessels form features such as microaneurysms, hemorrhages, hard exudates, and soft exudates or cotton-wool spots [5]. The microaneurysms are hypercellular saccular out pouching of the capillary wall.…”