摘要
目的视网膜血管健康状况的自动分析对糖尿病、心脑血管疾病以及多种眼科疾病的快速无创诊断具有重要参考价值。视网膜图像中血管网络结构复杂且图像背景亮度不均使得血管区域的准确自动提取具有较大难度。本文通过使用具有对称全卷积结构的U-net深度神经网络实现视网膜血管的高精度分割。方法基于U-net网络中的层次化对称结构和Dense-net网络中的稠密连接方式,提出一种改进的适用于视网膜血管精准提取的深度神经网络模型。首先使用白化预处理技术弱化原始彩色眼底图像中的亮度不均,增强图像中血管区域的对比度;接着对数据集进行随机旋转、Gamma变换操作实现数据增广;然后将每一幅图像随机分割成若干较小的图块,用于减小模型参数规模,降低训练难度。结果使用多种性能指标对训练后的模型进行综合评定,模型在DRIVE数据集上的灵敏度、特异性、准确率和AUC(area under the curve)分别达到0. 740 9、0. 992 9、0. 970 7和0. 917 1。所提算法与目前主流方法进行了全面比较,结果显示本文算法各项性能指标均表现良好。结论本文针对视网膜图像中血管区域高精度自动提取难度大的问题,提出了一种具有稠密连接方式的对称全卷积神经网络改进模型。结果表明该模型在视网膜血管分割中能够达到良好效果,具有较好的研究及应用价值。
Objective The automatic analysis of retinal vascular health status is a fundamental research topic in the area of fundus image processing.Analysis results can supply significant reference information for ophthalmologists to diagnose rapidly and noninvasively a variety of retinal pathologies,such as diabetes,glaucoma,hypertension,and diseases related to the brain and heart stocks.Although great progress has been achieved in the past decades,accurate automatic retinal vessel extraction remains a challenging problem due to the complex vascular network structure of retina vessels,uneven image background illumination,and random noises introduced by optical apparatuses.The traditional unsupervised retinal vessel segmentation methods generally identify retinal vessels with matched filters,vessel tractors,or templates designed artificially according to the vessel shape or prior information of a retinal image.Conversional supervised learning-based retinal vessel extraction algorithms generally consider artifact features as input and train shallow models,such as support vector machine,K-nearest neighbor classifiers,and traditional artificial neural networks.These models perform effectively in the case of normal retinal images with high-quality illumination and contrast.However,because of the representation limit of artificially designed features,these traditional vessel extraction methods fail when fundus vessels have low contrast with respect to the retinal background or are near nonvascular structures,such as the optic disk and fovea region.Recently,deep learning technology with multifarious convolutional neural networks has been widely applied to medical image processing and has achieved the most state-of-the-art performance due to its efficient and robust self-learned features.A series of new advances in retinal image processing has been achieved with deep learning networks.To help advance the research in this field,we adopt a deep neural network called U-net,which has a symmetrical full convolutional structure,and a d
作者
徐光柱
胡松
陈莎
陈鹏
周军
雷帮军
Xu Guangzhu;Hu Song;Chen Sha;Chen Peng;Zhou Jun;Lei Bangjun(College of Computer and Information Technology,China Three Gorges University,Yichang443002,China;Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,Yichang443002,China;Department of Diagnostic Utrasound,the First College of Clinical Medical Science,China Three Gorges University,Yichang443002,China)
出处
《中国图象图形学报》
CSCD
北大核心
2019年第9期1569-1580,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(61402259,U1401252)
宜昌市科技局项目(A19-302-13)
湖北省水电工程智能视频监测重点实验室开放基金项目(2018SDSJ08)
三峡大学2018年硕士学位论文培优基金项目(2018SSPY090)~~
