摘要
由于高血压性视网膜病变(hypertensive retinopathy,HR)病灶特征不明显,传统分类算法难以对其进行有效分类。针对这一问题,提出一种具有整体特征和局部特征的区域特征融合HR分类方法,即在整体HR分类模型的基础上,融合局部特征动静脉交叉压迫(arteriovenous nicking,AVN)分类模型来增强HR分类效果。在AVN分类中,提出一种新型的交叉点检测算法,该算法对分类后的动静脉进行逻辑与运算以求出交叉点位置,利用感兴趣区域提取方法从HR眼底图像中提取AVN图像块。提出的融合模型在私有数据集上进行了评估,准确率、敏感性和特异性分别为93.50%、69.83%和98.33%。在单阶段分类模型中分别与已有的方法进行对比,实验结果证明所提出模型效果较好。
Due to the lack of distinctive clinical features of hypertensive retinopathy(HR), it is difficult to effectively classify using conventional methods. Targeting on this problem, it proposes a regional feature fusion HR classification method with global and local characteristic, that is, based on the global model, merging its local characteristic arteriovenous nicking(AVN)classification model to enhance the HR classification effect. On AVN classification, it proposes a novel method to detect arteriovenous intersections. The proposed algorithm calculates the locations of these intersections through logical computing, AVN images are then extracted in regions of interest from HR affected rear of an eye photographs. Tested the proposed merging model with private databases, the accuracy, sensitivity and specificity are 93.5%,69.83% and 98.33%, respectively. The experimental results show that this new model works well and gets better effect compared with the existing methods in the single-stage classification model.
作者
王伟
浦一雯
WANG Wei;PU Yiwen(Foundation Department,Liaoning Technical University,Huludao,Liaoning 125105,China;School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China)
出处
《计算机工程与应用》
CSCD
北大核心
2022年第8期230-236,共7页
Computer Engineering and Applications
基金
国家自然科学基金面上项目(61772249)
国家重点研发计划项目(2018YFB1403303)。
关键词
区域特征融合
高血压性视网膜病变分类
动静脉交叉压迫分类
交叉点检测
regional feature fusion
hypertensive retinopathy classification
arteriovenous nicking classification
cross point detection
