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结合FCM聚类和边缘感知模型的眼底渗出物检测 被引量:1

Exudates Detection in Fundus Image Based on Fuzzy C-Means Clustering and Edge-Aware Model
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摘要 眼底图像中渗出物是构成糖尿病视网膜病变(Diabetic Retinopathy,DR)的早期症状之一,提出一种结合模糊C-均值(Fuzzy C-Means,FCM)聚类和边缘感知模型的方法实现对渗出物的检测。为保证后期检测精度和效率,对眼底图像进行增强对比度和均衡亮度等预处理操作,用FCM聚类分割出渗出物候选区域,利用基于判断邻域灰度差异的边缘感知模型对候选区域进行筛选,通过移除视盘区域,从而得到真实的渗出物区域。在公开的数据集上进行实验,算法的灵敏度为86.65%,特异性为94.79%,阳性预测值为95.14%,准确度为92.09%。结果表明,该方法能够有效实现对眼底渗出物的自动检测。 Exudates in the fundus image is one of the early symptoms of Diabetic Retinopathy(DR),a method for detecting exudates by combining Fuzzy C-Means(FCM)clustering and edge-aware model is proposed.In order to ensure the accuracy and efficiency of post-detection,the fundus image is firstly subjected to preprocessing such as enhanced contrast and equalized brightness,and then the exudates candidate region is segmented by FCM clustering,after that the candidate is determined by the edge-aware model based on the judgment of neighborhood grayscale difference.Finally,the real exudates area is obtained by removing the optic disc area.The approach is evaluated on the public fundus image data set,the sensitivity of the algorithm is 86.65%,the specificity is 94.79%,the positive predictive value is 95.14%,and the accuracy is 92.09%.The results show that the method can effectively realize the automatic detection of the fundus exudates.
作者 刘俊涛 王素娟 林晓明 刘祚时 谭俭辉 宋丹 LIU Juntao;WANG Sujuan;LIN Xiaoming;LIU Zuoshi;TAN Jianhui;SONG Dan(College of Mechanical and Electrical Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China;Guangdong Shunde Innovation Design Institute,Shunde,Guangdong 528311,China;College of Automation,Guangdong University of Technology,Guangzhou 510006,China;College of Information Science and Engineering,Guilin University of Technology,Guilin,Guangxi 541006,China)
出处 《计算机工程与应用》 CSCD 北大核心 2020年第11期192-199,共8页 Computer Engineering and Applications
基金 2018年广东省科技创新战略专项资金(No.2018FS05020102)。
关键词 渗出物检测 图像预处理 模糊C均值聚类 边缘感知模型 exudates detection image preprocessing fuzzy C-means clustering edge-aware model
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  • 1杨悦,郭树旭,任瑞治,于永力.基于核函数及空间邻域信息的FCM图像分割新算法[J].吉林大学学报(工学版),2011,41(S2):283-287. 被引量:10
  • 2伍忠东,高新波,谢维信.基于核方法的模糊聚类算法[J].西安电子科技大学学报,2004,31(4):533-537. 被引量:75
  • 3张天序.一种新的边缘检测计算模型和算法[J].自动化学报,1994,20(4):436-444. 被引量:8
  • 4章毓晋.图像工程(上册)图像处理和分析[M].北京:清华大学出版社,1999.1-20.
  • 5王备,王继成.图像分割中模糊聚类数目的确定[J].计算机技术与发展,2007,17(10):162-164. 被引量:7
  • 6Daisne J F, Sibomana M, Bol A, et al.Evaluation of a mul- timodality image(CT,MRI and PET) coregistration pro- cedure on phantom and head and neck cancer patients: accuracy,reproducibility and consistency[J].European So- ciety for Therapeutic Radiology and Oncology, 2003,69 (3) :237-245.
  • 7Zijdenbos A P, Dawant B M.Brain segmentation and white matter lesion detection in MR images[J].Critical Reviews in Biomedical Engineering, 1994,22(5/6) : 401-465.
  • 8Dunn J C.A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973,3(3) :32-57.
  • 9Bezedek J C.Pattern recognition with fuzzy objective func- tion algorithm[M].New York:Plenum Press,1981.
  • 10Nikhil P R,Bezdek J C.On cluster validity for the fuzzy C-means model[J].IEEE Transactions on Fuzzy Systems, 1995,3(3) :370-379.

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