基于区域建议策略的视盘定位方法

doi:10.3969/j.issn.0258-8021.2019.01.002

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Abstract The localization of optic disc (OD) is very important for computer-aided diagnosis of the ophthalmology diseases with fundus images. In this paper, a method of OD localization based on regional proposal strategy was proposed. First, the fundus image was mapped from the pixel domain to the feature domain, and candidate regions of OD were generated by using the regional proposal strategy in the obtained feature maps. Next, the candidate regions were sampled according to the certain criteria, and a fully connected layer was constructed to perform deep feature extraction. The location refinement of the candidate region was achieved by using the constraint of the loss function. At last, OD visibility was judged by filtering of the confidence threshold. If the OD was visible, the center of the candidate region with the highest degree of confidence was regarded as OD coordinate of the fundus image. The correct position of OD was obtained. Experiments were conducted in three public fundus image databases (DRIVE (40 images), MESSIDOR (1200 images) and STARE (400 images)). Testing results were 100%, 99.9% and 98.8%. Experimental results showed that the proposed method could reach the OD localization fast, accurately and robust, which was superior to existing OD localization methods. The pre-judgment of OD visibility was more consistent with the requirements of practical application. The proposed method was expected to contribute to the diagnosis of fundus diseases.

Key words： optic disc localization region proposal strategy fundus images deep learning convolutional neural network

Received: 08 December 2017

PACS:

R318

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	Articles by authors
	Tang Yiping
	Wang Liran
	He Xia
	Chen Peng
	Yuan Gongping

Cite this article:

Tang Yiping,Wang Liran,He Xia, et al. Optic Disc Localization Based on Regional Proposal Strategy[J]. Chinese Journal of Biomedical Engineering, 2019, 38(1): 9-17.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.01.002 OR http://cjbme.csbme.org/EN/Y2019/V38/I1/9

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