基于卷积神经网络的眼底图像微血管瘤检测方法

眼底微血管瘤是糖尿病诱发视网膜病变的初期症状，实现基于彩色眼底图像的微血管瘤的自动检测有助于辅助医生判断患者的视网膜是否正常，同时也是糖网病变分级评估中最重要的预处理手段。但由于视网膜结构复杂，同时眼底图像的成像由于患者、环境、采集设备等因素的不同会存在不同的亮度和对比度，现有的微血管瘤检测算法难以实现微血管瘤的精确检测，检测结果中存在大量的非微血管瘤候选区，如血管、背景噪声。由于卷积神经网络具有非常强的表达能力，能通过模型训练自动学习到目标的特征，该文提出了基于卷积神经网络的微血管瘤检测方法，仿真结果表明，该方法的检测效果优于传统的微血管瘤检测方法，在复杂的糖网图像下能实现微血管瘤的精确提取，将部分血管、背景噪声排除在外，使基于卷积神经网络提取的候选区数量更少且形态规则，有利于后续的特征提取和分类。

CNN-Based Microaneurysm Detection in Fundus Images

Fundus image microaneurysm is the initial symptom of diabetic retinopathy. Automatic detection of microaneurysm based on color fundus image is helpful to assist doctors in determining whether the patient's retina is normal, and it is also the most important pre-treatment method in the grading evaluation of diabetic retinopathy. Because of the complex structure of the retina and the different brightness and contrast existed in the imaging of fundus images due to different factors such as patients, environment and collection devices, the existing microaneurysm detection algorithm is difficult to realize the accurate detection of microaneurysm, and test results include a large amounts of the microaneurysm candidate section, such as blood vessels, background noise. Convolutional neural network has very strong expression ability, it can automatically learn the characteristics of the target by training model, and thus this paper puts forward the microaneurysm detection based on convolutional neural network method. The simulation results show that the proposed method is superior to the traditional test method of microaneurysm, the accurate extraction of microaneurysm can be realized under the complex diabetic retinopathy, but some blood vessels and background noise were excluded. The number of candidate regions extracted based on convolutional neural network is less and the form is regular, which is conducive to subsequent feature extraction and classification.