TY - GEN
T1 - Automatic Classification of Artery/Vein from Single Wavelength Fundus Images
AU - Raj, P. Kevin
AU - Manjunath, Aniketh
AU - Kumar, J. R.Harish
AU - Seelamantula, Chandra Sekhar
N1 - Funding Information:
This work was supported by the Ministry of Human Resource Development under the IMPRINT India Initiative (Domain: Healthcare; Project No.: 6013).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4
Y1 - 2020/4
N2 - Vessels are regions of prominent interest in retinal fundus images. Classification of vessels into arteries and veins can be used to assess the oxygen saturation level, which is one of the indicators for the risk of stroke, condition of diabetic retinopathy, and hypertension. In practice, dual-wavelength images are obtained to emphasize arteries and veins separately. In this paper, we propose an automated technique for the classification of arteries and veins from single-wavelength fundus images using convolutional neural networks employing the ResNet-50 backbone and squeeze-excite blocks. We formulate the artery-vein identification problem as a three-class classification problem where each pixel is labeled as belonging to an artery, vein, or the background. The proposed method is trained on publicly available fundus image datasets, namely RITE, LES-AV, IOSTAR, and cross-validated on the HRF dataset. The standard performance metrics, such as average sensitivity, specificity, accuracy, and area under the curve for the datasets mentioned above, are 92.8%, 93.4%, 93.4%, and 97.5%, respectively, which are superior to the state-of-the-art methods.
AB - Vessels are regions of prominent interest in retinal fundus images. Classification of vessels into arteries and veins can be used to assess the oxygen saturation level, which is one of the indicators for the risk of stroke, condition of diabetic retinopathy, and hypertension. In practice, dual-wavelength images are obtained to emphasize arteries and veins separately. In this paper, we propose an automated technique for the classification of arteries and veins from single-wavelength fundus images using convolutional neural networks employing the ResNet-50 backbone and squeeze-excite blocks. We formulate the artery-vein identification problem as a three-class classification problem where each pixel is labeled as belonging to an artery, vein, or the background. The proposed method is trained on publicly available fundus image datasets, namely RITE, LES-AV, IOSTAR, and cross-validated on the HRF dataset. The standard performance metrics, such as average sensitivity, specificity, accuracy, and area under the curve for the datasets mentioned above, are 92.8%, 93.4%, 93.4%, and 97.5%, respectively, which are superior to the state-of-the-art methods.
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U2 - 10.1109/ISBI45749.2020.9098580
DO - 10.1109/ISBI45749.2020.9098580
M3 - Conference contribution
AN - SCOPUS:85085866821
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1262
EP - 1265
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Y2 - 3 April 2020 through 7 April 2020
ER -
