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Automatic Classification of Artery/Vein from Single Wavelength Fundus Images

Raj, PK and Manjunath, A and Kumar, JRH and Seelamantula, CS (2020) Automatic Classification of Artery/Vein from Single Wavelength Fundus Images. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 3-7 April 2020, Iowa City, IA, USA, USA, pp. 1262-1265.

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Official URL: https://dx.doi.org/10.1109/ISBI45749.2020.9098580


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. © 2020 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Symposium on Biomedical Imaging
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference Date: 3 April 2020 Through 7 April 2020; Conference Code:160183
Keywords: Convolutional neural networks; Eye protection; Medical imaging; Risk assessment, Area under the curves; Automatic classification; Average sensitivities; Identification problem; Oxygen saturation levels; Retinal fundus images; State-of-the-art methods; Three-class classification, Image classification
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 21 Oct 2020 06:13
Last Modified: 21 Oct 2020 06:13
URI: http://eprints.iisc.ac.in/id/eprint/65868

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