Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L*a*b color space.
Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L*a*b color space.
复制标题
使用 L*a*b 颜色空间自动检测糖尿病视网膜病变眼底图像中的微动脉瘤。
DOI：
10.1364/josaa.33.000074
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Kostas Stathis
中科院分区：
文献类型：
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作者：
P. Navarro;Diego Alonso;Kostas Stathis
我们开发了一个自动图像处理系统，用于检测糖尿病患者的微动脉瘤（MA）。糖尿病视网膜病变是工作年龄糖尿病患者可预防失明的主要原因之一，MA的存在是第一个迹象之一。我们将眼底图像变换到L*a*B* 颜色空间，以便分别处理L* 和a* 通道，在每个通道中寻找MA。然后，我们将结果进行融合，最后将MA候选者发送到k-最近邻分类器进行最终评估。该方法的性能，测量对50个图像与眼科医生的手绘地面真相，显示高灵敏度（100%）和准确性（84%），和运行时间约10秒。鉴于需要定期筛查的潜在患者数量众多，这种自动图像处理应用程序对于减轻与糖尿病视网膜病变诊断相关的公共卫生系统的负担非常重要。
We develop an automated image processing system for detecting microaneurysm (MA) in diabetic patients. Diabetic retinopathy is one of the main causes of preventable blindness in working age diabetic people with the presence of an MA being one of the first signs. We transform the eye fundus images to the L*a*b* color space in order to separately process the L* and a* channels, looking for MAs in each of them. We then fuse the results, and last send the MA candidates to a k-nearest neighbors classifier for final assessment. The performance of the method, measured against 50 images with an ophthalmologist's hand-drawn ground-truth, shows high sensitivity (100%) and accuracy (84%), and running times around 10 s. This kind of automatic image processing application is important in order to reduce the burden on the public health system associated with the diagnosis of diabetic retinopathy given the high number of potential patients that need periodic screening.