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27 March 2019 Arteriovenous classification method using convolutional neural network for early detection of retinal vascular lesion
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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110501M (2019) https://doi.org/10.1117/12.2521528
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore
Abstract
Early detection of hypertension is important because hypertension leads to stroke and cardiovascular diseases. Hypertensive changes in the retina are diagnosed by measuring the arteriovenous ratio near the optic disc. Therefore, classification of arteries and veins is necessary for ratio measurement, and previous studies classified them by using pixel-based features, such as pixel values, texture features, and shape features etc. For simplification of the classification process, a convolutional neural network (CNN) was applied in this study. For evaluation of the classification process, CNN was tested using centerlines extracted manually in this study. As a result of a fourfold cross-validation with 40 retinal images, the mean classification ratio of the arteries and veins was 98%. Furthermore, CNN was tested using the centerlines of blood vessels automatically extracted using the CNN-based method for testing the fully automatic method. CNN classified 90% of blood vessels into arteries and veins in the arteriovenous ratio measurement zone. CNN had 30 trained and 10 tested retinal images. This result may work as an important processing for abnormality detection.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hibiki Ikawa, Yuji Hatanaka, Wataru Sunayama, Kazunori Ogohara, Chisako Muramatsu, and Hiroshi Fujita "Arteriovenous classification method using convolutional neural network for early detection of retinal vascular lesion", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501M (27 March 2019); https://doi.org/10.1117/12.2521528
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