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计算机系统应用:2019,28(1):251-255
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基于纹理特征的糖网临床前期眼底自发荧光图像识别
(1.温州医科大学 生物医学工程学院, 温州 325035;2.瑞安市人民医院 设备科, 温州 325200;3.瑞安市人民医院 眼科, 温州 325200)
Identification of Fundus Autofluorescence Images Based on Texture Features in Preclinical Diabetic Retinopathy
(1.School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China;2.Department of Equipment, Rui'an People's Hospital, Wenzhou 325200, China;3.Department of Eyes, Rui'an People's Hospital, Wenzhou 325200, China)
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投稿时间:2018-07-17    修订日期:2018-08-09
中文摘要: 及时发现和干预潜在的糖尿病视网膜病变患者,对帮助提升糖尿病患者的整体视觉质量和降低医疗成本具有十分积极的意义.由于糖尿病视网膜病变临床前期和正常人的眼底荧光图像在视觉感观基本上没有差别,为此本文通过目前应用较广的纹理特征算法和支持向量机对这两组图像进行了模式识别.通过将185张眼底荧光图片十折交叉检验发现,LBP算法对其具有很好的识别效果.等价模式下的59维LBP算子的十折交叉准确率达到了91.89%,同时在测试集和训练集以1:1随机划分的情况下,由训练集数据所生成的模型对测试集中92张眼底荧光图像的识别准确率达到了88.12%,AUC值为0.943.
Abstract:Timely diagnosis and intervention for potential diabetic retinopathy patients is very positive in improving the overall visual quality of diabetic patients and reducing medical costs. Because the fundus fluorescence images of preclinical diabetic retinopathy and normal people have no obvious difference in visual perception, this study recognizes the two groups of images through the widely used texture feature algorithm and support vector machine. Through the 10-fold cross validation of 185 fundus autofluorescence images, the LBP algorithm has a sound recognition effect. The 10-fold cross-validation accuracy of the 59-dimensional LBP operator with "Uniform" patterns reaches 91.89%. And in the case that the test set and the training set are randomly divided by 1:1, the recognition accuracy of 92 fundus fluorescence images in the test set reaches 88.12%, and the AUC is 0.943.
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基金项目:国家自然科学基金(11005081);温州市公益性科技计划(2017Y0132)
引用文本:
傅志翔,张元勋,王历辉,陈嘉玮,柯大观.基于纹理特征的糖网临床前期眼底自发荧光图像识别.计算机系统应用,2019,28(1):251-255
FU Zhi-Xiang,ZHANG Yuan-Xun,WANG Li-Hui,CHEN Jia-Wei,KE Da-Guan.Identification of Fundus Autofluorescence Images Based on Texture Features in Preclinical Diabetic Retinopathy.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):251-255

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