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Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images

机译:玻璃疣的自动分割方法诊断与年龄相关的视网膜黄斑变性筛查

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摘要

Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi's entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, p < 0.01), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.
机译:现有的玻璃疣测量很难在临床中使用,因为它需要大量的时间和精力进行视觉检查。为了解决这个问题,我们提出了一种自动玻璃疣检测方法,以帮助临床诊断与年龄有关的黄斑变性。首先,我们将眼底图像更改为绿色通道,并根据视盘提取黄斑区域的ROI。接下来,我们使用ROI中的中值滤波器的差异图像检测了候选组。我们还分割了血管并将其从图像中删除。最后,我们通过人一的熵阈值算法检测了玻璃疣。为了验证有效性,我们对30例手动检测结果和自动检测结果进行了比较和统计分析。结果,平均灵敏度为93.37%(80.95%〜100%),平均DSC为0.73(0.3〜0.98)。另外,ICC值为0.984(CI:0.967〜0.993,p <0.01),表明该自动方法具有很高的可靠性。我们期望自动玻璃疣检测可以帮助临床医生提高眼底图像上玻璃疣检测的诊断性能。

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