Fleming Alan D, Philip Sam, Goatman Keith A, Williams Graeme J, Olson John A, Sharp Peter F
Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD, UK.
Phys Med Biol. 2007 Dec 21;52(24):7385-96. doi: 10.1088/0031-9155/52/24/012. Epub 2007 Dec 5.
Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13,219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.
人们一直在广泛寻求自动化图像分析，以减轻糖尿病视网膜病变筛查项目中图像分级所需的工作量。视网膜图像中渗出物的识别是自动化分析的一个重要目标，因为这些渗出物是疾病已发展到需要转诊至眼科医生阶段的指标之一。使用多尺度形态学过程检测候选渗出物。基于局部特性，确定候选物属于渗出物、玻璃膜疣或背景类别的可能性。这就得出了图像中含有渗出物的可能性，可通过阈值处理做出二元决策。与临床参考标准相比，在13219张图像的测试集中，其中300张含有渗出物，检测出含有渗出物图像的灵敏度为95.0%，特异性为84.6%。根据需求，该方法可构成自动化系统的一部分，以检测显示任何糖尿病视网膜病变或可转诊糖尿病视网膜病变的图像。
