Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images
Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images
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DOI：
10.1007/s11517-021-02484-x
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发表时间：
2022-01-06
影响因子：
3.2
通讯作者：
Haneishi, Hideaki
中科院分区：
文献类型：
--
作者：
Khaing, Tin Tin;Aimmanee, Pakinee;Haneishi, Hideaki
精确检测视盘（OD）是糖尿病视网膜病变诊断的重要任务。为了管理庞大的糖尿病人群，对高效和远程视网膜成像技术存在显著需求。在这方面，使用附接到智能手机的手持式移动的相机是一种有前途的方法。然而，与标准设备上获得的图像相比，智能手机视网膜图像通常质量较低。它们还具有狭窄的视野和不完整/不平衡的血管结构。因此，我们提出了一种新的，全自动的混合OD定位方法（HLM）。它是专为移动的相机/智能手机视网膜图像设计和验证的。当图像中血管系统完整时，HLM采用排除法分析血管结构并确定OD位置，当图像中血管系统不完整时，采用新提出的直线检测方法。对于OD分割，一个活动轮廓模型，其次是圆拟合的方法被集成到HLM。所提出的方法进行了测试，三个移动的相机数据集和四个数据集获得的标准设备。对于移动的相机数据集，HLM实现了OD定位的平均准确度为98%。分割例程获得的平均准确率为92.64%，平均召回率为82.38%。在标准数据集上对最新的最先进的方法进行测试，显示出相当的性能。
Precise detection of the optic disk (OD) is an important task in the diagnosis of diabetic retinopathy. To manage the massive diabetic population, there is a significant demand for efficient and remote retinal imaging techniques. In this regard, the use of handheld mobile cameras attached to a smartphone is a promising approach. However, smartphone retinal images are often of low quality, compared to those obtained on standard equipment. They also have a narrow field of view and an incomplete/unbalanced vessel structure. Hence, we propose a new, fully automatic hybrid method for OD localization (HLM). It is designed for and verified on mobile camera/smartphone retinal images. The HLM analyzes the vessel structure and finds the OD locations by using the exclusion method when an image has a complete vessel system, and a newly proposed line detection method, otherwise. For OD segmentation, an active contour model followed by the circle fitting approach is integrated into the HLM. The proposed method was tested on three mobile camera datasets and four datasets obtained by standard equipment. For mobile camera datasets, the HLM achieves an average accuracy of 98% for OD localization. The segmentation routine obtains an average precision of 92.64% and an average recall of 82.38%. Testing against the recent state-of-the-art methods on the standard datasets shows comparable performance.