Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

Classifiers fusion for improved vessel recognition with application in quantification of generalized arteriolar narrowing

This paper attempts to estimate diagnostically relevant measure,i.e.,Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme.The features exploited for retinal vessel characterization are based on statistical measures of histogram,different filter responses of images and local gradient in-formation.The feature selection process is based on two feature ranking approaches(Pearson Correlation Coefficient technique and Relief-F method)to rank the features followed by use of maximum classification accuracy of three supervised classifiers(κ-Nearest Neighbor,Support Vector Machine and Naïve Bayes)as a threshold for feature subset selection.Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme,i.e.,decision fusion of multiple classifiers.The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance.The system is tested on three databases,a local dataset of 44 images and two publically available databases,INSPIRE-AVR containing 40 images and VICAVR containing 58 images.The local database also contains images with pathologically diseased structures.The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies.Overall,an accuracy of 90.45%,93.90%and 87.82%is achieved in retinal blood vessel separation with 0.0565,0.0650 and 0.0849 mean error in Arte-riovenous Ratio calculation for Local,INSPIRE-AVR and VICAVR dataset,respectively.

基于改进U-Net的视网膜血管动静脉分割和分类算法

针对视网膜图像动静脉血管分割和分类存在的血管分割不足、连通性差、分类准确率低等问题,提出了一种基于改进U-Net模型的视网膜动静脉血管分割和分类的方法。首先,在U-Net模型的每次卷积操作之后加入批量归一化处理,减少网络中的梯度消失现象;其次,针对网络深度增加,模型性能下降的问题,在全连接层加入额外的跳跃连接,提高模型训练性能;最后,将这种改进后的模型进行4次串联训练,并通过多次的编码和解码对特征进行提取和融合,组成了处理视网膜动静脉血管分割和分类任务的FUnet模型。在DRIVE和HRF数据集上进行了模型有效性的验证,实验结果表明:与传统U-Net相比,本文方法在DRIVE数据集上AUC、精确度和特异度分别提升了4.11%、 7.61%和0.06%,在HRF数据集上AUC、精确度和特异度分别提升了17.24%、 0.6%和0.18%。该方法对视网膜动静脉自动准确的分割和分类具有较好的效果。

基于分类回归树和AdaBoost的眼底图像视网膜血管分割

提出一种能有效分割眼底图像中视网膜血管的监督学习方法,为眼底图中的每个像素点构造一个包括局部特征、形态学特征和Gabor特征在内的39维特征向量,用以判定其是否为血管上的像素.在进行分类计算时,以分类回归树作为弱分类器对样本集分类,然后对AdaBoost分类器进行训练得到强分类器,并由此完成各个像素点的分类判定.基于国际公共数据库DRIVE的实验结果表明,该方法的平均精确度达到0.960 7,且敏感度和特异性均优于已有的基于监督学习的方法,适用于眼底图像的计算机辅助定量分析和疾病诊断.

基于眼底图像视网膜血管分割、分类方法的中医目诊研究

中医目诊是通过观察眼部的神、色、形、态来诊断全身疾病的一种方法。对眼底血管的观察是目诊的主要内容。视网膜血管结构的变化与许多慢性疾病密切相关。图像处理技术在眼底图像研究的应用可以为系统性疾病提供早期诊断。眼底图像血管分割是眼底血管研究的基础,目前大体可分为基于匹配滤波、血管跟踪、形态学处理、形变模型以及机器学习的分割算法5类。眼底血管分类,如动静脉分类,为临床诊断提供重要价值。在此类研究的基础上,提出了眼底血管分割、分类方法在中医目诊中的实际应用方法。

语义融合眼底图像动静脉分类方法

目的眼底图像中的动静脉分类是许多系统性疾病风险评估的基础步骤。基于传统机器学习的方法操作复杂,且往往依赖于血管提取的结果,不能实现端到端的动静脉分类,而深度语义分割技术的发展使得端到端的动静脉分类成为可能。本文结合深度学习强大的特征提取能力,以提升动静脉分类精度为目的,提出了一种基于语义融合的动静脉分割模型SFU-Net(semantic fusion based U-Net)。方法针对动静脉分类任务的特殊性,本文采用多标签学习的策略来处理该问题,以降低优化难度。针对动静脉特征的高度相似性,本文以DenseNet-121作为SFU-Net的特征提取器,并提出了语义融合模块以增强特征的判别能力。语义融合模块包含特征融合和通道注意力机制两个操作:1)融合不同尺度的语义特征从而得到更具有判别能力的特征;2)自动筛选出对目标任务更加重要的特征,从而提升性能。针对眼底图像中血管与背景像素之间分布不均衡的问题,本文以focal loss作为目标函数,在解决类别不均衡问题的同时重点优化困难样本。结果实验结果表明,本文方法的动静脉分类的性能优于现有绝大多数方法。本文方法在DRIVE(digital retinal images for vessel extraction)数据集上的灵敏性(sensitivity)与目前最优方法相比仅有0.61%的差距,特异性(specificity)、准确率(accuracy)和平衡准确率(balanced-accuracy)与目前最优方法相比分别提高了4.25%,2.68%和1.82%;在WIDE数据集上的准确率与目前最优方法相比提升了6.18%。结论语义融合模块能够有效利用多尺度特征并自动做出特征选择,从而提升性能。本文提出的SFU-Net在动静脉分类任务中表现优异,性能超越了现有绝大多数方法。