论文标题
使用形态级联决策树检测视网膜图像中糖尿病异常
Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree
论文作者
论文摘要
这项研究旨在开发一种有效的系统来筛查糖尿病性视网膜病变。糖尿病性视网膜病是失明的主要原因。糖尿病性视网膜病的严重程度被某些特征所认识到，例如血管区域，渗出液，出血和微型干扰素。要对疾病进行评分，筛查系统必须有效地检测到这些特征。在本文中，我们提出了一种简单而快速的方法来检测糖尿病性视网膜病变。我们对灰度图像进行预处理，并在图像中找到所有标记的连接组件（BLOB），无论它是出血，渗出液，容器，光盘还是其他任何东西。然后，我们应用一些约束，例如紧凑性，斑点面积，强度和对比度，以筛选负责糖尿病性视网膜病的候选连接组件。我们通过进行一些后处理来获得我们的最终结果。结果与地面真相进行了比较。通过查找召回（灵敏度）来衡量性能。我们拍摄了10张尺寸500 * 752的图像。平均召回率为90.03％。
This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages, exudates, vessels, optic disc or anything else. Then we apply some constraints such as compactness, area of blob, intensity and contrast for screening of candidate connectedcomponent responsible for diabetic retinopathy. We obtain our final results by doing some post processing. The results are compared with ground truths. Performance is measured by finding the recall (sensitivity). We took 10 images of dimension 500 * 752. The mean recall is 90.03%.
