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ISSN      1003-9775
CN        11-2925/TP
邮发代号:82-456
单    价:80.00元
全年订价:960.00元
在线期刊

聚合多通道特征的青光眼自动检测

赵荣昌1,2), 陈再良1,2), 段宣初2,4), 陈奇林1,2), 刘 可2,4), 朱承璋2,3)*
1) (中南大学信息科学与工程学院 长沙 410083)2) (中南大学眼科医学影像处理中心 长沙 410083)3) (中南大学文学与新闻传播学院 长沙 410083)4) (中南大学湘雅二医院眼科 长沙 410083)
分类号: TP391.41
出版年,卷(期):页码: 2017 , 29 ( 6 ): 998-1006 赵荣昌
摘要: 眼底影像的自动分析是计算机辅助青光眼筛查和诊断的关键基础. 为提高青光眼辅助诊断的准确性, 基于彩色眼底图, 提出一种聚合多通道特征的青光眼自动检测算法. 首先基于多尺度分析技术, 通过聚合多通道图像特征, 从颜色分布、多尺度Gabor滤波和梯度方向分布等方面表示视盘形态和结构在彩色眼底图上的细微变化; 然后设计基于随机森林的分类器, 在青光眼数据集上训练分类器模型, 并利用集成学习技术鉴别青光眼, 从而实现一种基于图像特征的青光眼自动检测算法; 最后在2个具有挑战性的青光眼公开数据集(RIM-ONE r2和Drishti_GS)上对青光眼检测算法进行测试和验证, 分别得到了0.869 0和0.800 4的曲线下面积值. 实验结果表明, 该算法在保证青光眼检测敏感性的同时能够显著提高其特异性, 对青光眼辅助筛查和诊断具有很好的参考价值.
关键词: 计算机辅助检测; 青光眼; 形态分析; 随机森林; 多通道特征聚合
Automated Glaucoma Detection Based on Multi-channel Features from Color Fundus Images
Zhao Rongchang1,2), Chen Zailiang1,2), Duan Xuanchu2,4), Chen Qilin1,2), Liu Ke2,4), and Zhu Chengzhang2,3)*
1) (School of Information Science and Engineering, Central South University, Changsha 410083) 2) (Center for Ophthalmic Image Analysis, Central South University, Changsha 410083)3) (The College of Literature and Journalism, Central South University, Changsha 410083) 4) (Ophthalmology Department of the Second Xiangya Hospital, Central South University, Changsha 410083) 1) (School of Information Science and Engineering, Central South University, Changsha 410083) 2) (Center for Ophthalmic Image Analysis, Central South University, Changsha 410083)3) (The College of Literature and Journalism, Central South University, Changsha 410083) 4) (Ophthalmology Department of the Second Xiangya Hospital, Central South University, Changsha 410083)
abstract: Automatic analysis of fundus images is the foundation of computer-aided glaucoma screening and diagnosis. To improve the accuracy of glaucoma screening, a novel glaucoma detection method is proposed based on the multi-channel feature aggregation in fundus images. Firstly, multi-channel features are computed based on color distribution, multi-scale Gabor filters and oriented gradient histogram, and they describe the tiny changes in the morphology and structure of the optic disc. Secondly, a random forest classifier is developed to detect glaucoma based on the multi-channel features and ensemble learning technology. Finally, the glaucoma detection algorithm is tested on two challenging glaucoma datasetss and obtains values of the area under the curve with 0.869 0 and 0.820 4, respectively. The experimental results show that the proposed method can improve sensitivity and specificity simultaneously.
keyword: computer-aided diagnosis; glaucoma; morphological analysis; random forest; multi-channel features aggregation
 
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