基于生成对抗网络的彩色眼底图像硬性渗出检测方法

doi:10.3969/j.issn.0258-8021.2019.02.004

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Abstract Diabetic retinopathy (DR) is a serious complication of diabetes and one of the most common causes of visual impairment. Hard exudates (HE) are early symptoms of DR, and its detection plays a key role in screening DR using fundus images. In this study, an automatic computational method based on Generative Adversarial Networks (GANs) for HE detection was proposed and evaluated on a publicly available database (e-ophtha EX). Compared with the general Convolutional Neural Network (CNN),GANs was composed of the generator G and the discriminator D, the mutual game and competition between them made the GANs detect HE in color fundus images more accurately. Firstly, the center of optic disc (OD) was located by considering its vessels features and brightness information, and masked to avoid interference with HE detection. Secondly, the generator G and the discriminator D were trained on e-ophtha EX dataset alternately, with the optimal model determined, which had the best segmentation effect on the validation dataset. The proposed algorithm was validated on e-ophtha EX database on pixel level, achieving the overall average sensitivity, PPV and F-score of 88.6%, 84.3% and 86.4%. It was then tested on another independent database (DIARETDB1) with the overall average sensitivity, specificity and accuracy of 100%, 96.2% and 97.8%, respectively. In summary, the evaluation results on both retinal image databases demonstrated the effectiveness of using GANs for automatic HE detection.

Key words： retinal image hard exudates detection generative adversarial networks convolutional neural network

Received: 14 March 2018

PACS:

R318

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	Articles by authors
	Long Shengchun
	Chen Jiali
	Huang Xiaoxiao
	Chen Zhiqing

Cite this article:

Long Shengchun,Chen Jiali,Huang Xiaoxiao, et al. Hard Exudates Detection Method Based on Generative Adversarial Networks in Color Fundus Images[J]. Chinese Journal of Biomedical Engineering, 2019, 38(2): 157-165.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.02.004 OR http://cjbme.csbme.org/EN/Y2019/V38/I2/157

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