计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 203-207.
蔡震震, 唐鹏, 胡建斌, 金炜东
CAI Zhen-zhen, TANG Peng, HU Jian-bin, JIN Wei-dong
摘要： 为实现硬性渗出的自动检测,构建糖网病计算机辅助诊断系统,文中提出了一种基于深度卷积神经网络的硬性渗出提取方法。该方法主要分为两个部分:线下训练硬性渗出分类模型和在线检测硬性渗出。线下训练分类模型是利用深度卷积神经网络自动提取特征训练出硬性渗出的分类模型;在线检测硬性渗出使用训练好的分类模型对眼底影像中的硬性渗出进行检测,并获取硬性渗出的概率图以及伪彩色图。利用文中方法在标准数据集DIARETDB1和自选数据集上进行验证,结果表明所提方法行之有效,鲁棒性较好,具有很强的临床实践意义。
中图分类号:
| [1]美丽巴努·玉素甫,陈雪艺.视力损害的流行病学研究[J].国际眼科杂志,2010,10(2):304-307. [2]丁山,宋丽晓.一种改进的视网膜图像中微小动脉瘤的检测算法[J].计算机科学,2014,42(12):269-274. [3]GREENSPAN H,GINNEKEN B,SUMMERS R M.Guest Editorial Deep Learning in Medical Imaging:Overview and Future Promise of an Exciting New Technique [J].IEEE Transactions on Medical Imaging,2016,5(35):1153-1159. [4]RAVISHANKAR S,JAIN S,MITTAL A.Automated feature extraction for early detection of diabetic retinopathy in fundus images[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.Anchorage Alaska,America:CVPR,2009:210-217. [5]GANDHI M,DHANASEKARAN R.Diagnosis of diabetic retinopathy using morphological process and SVM classifier[C]∥IEEE International Conference on Communication and Signal Processing.Washington,America:ICCSP,2013:873-877. [6]LI H,CHUTATAPE O.Automated feature extraction in color retinal images by a model based approach [J].IEEE Transactions on Biomedical Engineering,2004,51(2):246-254. [7]TAMILARASI M,DURAISWAMY K.Genetic based Fuzzy Seeded Region Growing Segmentation for Diabetic Retinopathy Images [C]∥International Conference on Computer Communication and Informatics.Tamil Nadu,India:ICCCI,2013. [8]高玮玮,沈建新,程武山,等.基于改进的模糊C-均值聚类算法及支持向量机的眼底图像中硬性渗出检测方法[J].北京生物医学工程,2017,36(4):331-337. [9]张磊,卜巍,邬向前,等.基于背景估计和集成分类的眼底硬性渗出检测[J].智能计算机与应用,2017,7(5):66-69. [10]肖志涛,王雯,耿磊,等.基于背景估计和SVM分类器的眼底图像硬性渗出物检测方法[J].中国生物医学工程学报,2015,34(6):720-728. [11]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolution neural networks[J].Advances in Neural Information Processing System,2012,25(2):1097-1105. [12]ZHANG N,DONAHUE J,GIRSHICK R,et al.Part-Based R-CNNs for Fine-Grained Category Detection[C]∥European Conference on Computer Vision.Springer,Cham,2014:834-849. [13]GIRSHICK R.Fast R-CNN[C]∥IEEE International Confe-rence on Computer Vision.IEEE Computer Society.2015:1440-1448. [14]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[C]∥International Conference on Neural Information Processing Systems.MIT Press,2015:91-99. [15]蔡震震,唐鹏,胡建斌,等.基于形态学轮廓分析的眼底影像中视盘的定位[C]∥中国控制会议.2016:9434-9438. [16]KAUPPI T,KALESNYKIENE V,KAMARAINEN J K,et al.DIARETDB1 diabetic retinopathy database and evaluation protocol[C]∥British Machine Vision Conference.2007. |
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