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Method and apparatus to detect lesions of diabetic retinopathy in fundus images

机译:在眼底图像中检测糖尿病性视网膜病变的方法和设备

摘要

The present invention relates to the design and implementation of a three stage computer-aided screening system that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. In the first stage, bright and red regions are extracted from the fundus image. An optic disc has similar structural appearance as bright lesions, and the blood vessel regions have similar pixel intensity properties as the red lesions. Hence, the region corresponding to the optic disc is removed from the bright regions and the regions corresponding to the blood vessels are removed from the red regions. This leads to an image containing bright candidate regions and another image containing red candidate regions. In the second stage, the bright and red candidate regions are subjected to two-step hierarchical classification. In the first step, bright and red lesion regions are separated from non-lesion regions. In the second step, the classified bright lesion regions are further classified as hard exudates or cotton-wool spots, while the classified red lesion regions are further classified as hemorrhages and micro-aneurysms. In the third stage, the numbers of bright and red lesions per image are combined to generate a DR severity grade. Such a system will help in reducing the number of patients requiring manual assessment, and will be critical in prioritizing eye-care delivery measures for patients with highest DR severity.
机译:本发明涉及三阶段计算机辅助筛选系统的设计和实现,该系统分析具有变化的照明度和视野的眼底图像,并使用机器学习生成糖尿病性视网膜病(DR)的严重度等级。在第一阶段,从眼底图像中提取明亮和红色区域。视盘具有与明亮病变相似的结构外观,并且血管区域具有与红色病变相似的像素强度特性。因此,将与视盘相对应的区域从亮区域去除,将与血管相对应的区域从红色区域去除。这导致包含明亮候选区域的图像和包含红色候选区域的另一图像。在第二阶段,对明亮和红色候选区域进行两步分层分类。第一步,将明亮和红色病变区域与非病变区域分开。在第二步骤中,将分类的明亮病变区域进一步分类为硬性渗出液或棉斑点,而分类的红色病变区域进一步分类为出血和微动脉瘤。在第三阶段,将每个图像的亮和红色病变的数量相结合,以生成DR严重程度等级。这样的系统将有助于减少需要人工评估的患者数量,并且对于优先考虑具有最高DR严重性的患者的眼保健措施至关重要。

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