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Automatic optic disc (OD) detection is an essential step for screening of eye diseases. An OD localization method is proposed in this paper, which aims to locate OD robustly in retinal image with pathological changes. There are mainly three steps in this approach: region-of-interest (ROI) detection, candidate pixel detection, and confidence score calculation. The features of vessel direction, intensity, OD edges, and size of bright regions were extracted and employed in the proposed OD locating approach. Compared with the OD locating method based on vessel direction only, the proposed method could handle the following cases better: OD partially appears in retinal image, retinal vessels are not obvious in retinal image, or there are bright lesions in retinal images. Four public databases with total 340 retinal images were tested to evaluate the performance of our method. The proposed method can achieve an accuracy of 100

作者:Li, Xiong;Huiqi, Li

来源:Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2016 年 47卷

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作者:
Li, Xiong;Huiqi, Li
来源:
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2016 年 47卷
标签:
Localization Optic disc Retinal images
Automatic optic disc (OD) detection is an essential step for screening of eye diseases. An OD localization method is proposed in this paper, which aims to locate OD robustly in retinal image with pathological changes. There are mainly three steps in this approach: region-of-interest (ROI) detection, candidate pixel detection, and confidence score calculation. The features of vessel direction, intensity, OD edges, and size of bright regions were extracted and employed in the proposed OD locating approach. Compared with the OD locating method based on vessel direction only, the proposed method could handle the following cases better: OD partially appears in retinal image, retinal vessels are not obvious in retinal image, or there are bright lesions in retinal images. Four public databases with total 340 retinal images were tested to evaluate the performance of our method. The proposed method can achieve an accuracy of 100