Detection of Exudates on Color Fundus Images using Texture Based Feature Extraction

Nugroho, Hanung Adi; Oktoeberza, Widhia K.Z.; Adji, Teguh Bharata; Najamuddin, Faisal

doi:10.14716/ijtech.v6i2.958

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…In comparison, our method outperforms many recent SOTA glaucoma classification methods on majority of the performance metrics while maintaining competitive results in rest of them (see Table 1). To keep the comparison fair, we tested the performance of our method against other well-known SOTA DeepCDR [8], CWT (wavelet) [10], GLCM [16], and Gabor-based [9] techniques on DRISHTI-GS1and ORIGA datasets as well (Fig 6 & Table 2). Consulting the Fig.…”

Section: Training and Resultsmentioning

confidence: 99%

Compact Scattering Features for Glaucoma Detection

Hussain

Guo

Shi

et al. 2022

J. Phys.: Conf. Ser.

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In the field of medical image diagnostics, automatic glaucoma classification is a daunting task that requires carefully crafted features (Machine Learning) or tuning hyperparameters over a huge search space (Deep Learning). In this paper, we proposed a wavelet scattering based approach that extracts features from fundus images at different sub-stages, reduces the feature space using principal component analysis (PCA) and represents the classifying features in terms of projection loss along the subspace for each class. This simple yet elegant approach yields fast-paced results with great interpretability, that deep learning-based approaches lack, and excels in most performance metrics when compared to other machine learning based methods.

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Section: Training and Resultsmentioning

confidence: 99%

Compact Scattering Features for Glaucoma Detection

Hussain

Guo

Shi

et al. 2022

J. Phys.: Conf. Ser.

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“…An accuracy of 78.23%, sensitivity of 72.15%, and specificity of 79.40% were reported. Besides that, some authors attempted to detect bright lesions, but they did not address the differentiation between hard exudates and CWSs.…”

Section: Introductionmentioning

confidence: 99%

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

Sreng

Maneerat

Hamamoto

et al. 2019

IEEJ Transactions Elec Engng

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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.

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