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 information. 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 (k-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 Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

中文翻译：

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分类器融合用于改进血管识别并在广义小动脉狭窄量化中的应用

本文试图通过使用特征排序策略和多分类器决策组合方案的改进的视网膜血管分类来估计诊断相关的测量值，即动静脉比率。用于视网膜血管表征的特征基于直方图的统计测量、图像的不同滤波器响应和局部梯度信息。特征选择过程基于两种特征排序方法（Pearson 相关系数技术和 Relief-F 方法）对特征进行排序，然后使用三个监督分类器（k-最近邻、支持向量机和朴素贝叶斯）的最大分类精度作为特征子集选择的阈值。使用选定的特征子集和提出的混合分类方案标记视网膜血管，即 多分类器的决策融合。比较分析显示血管分类准确度以及动静脉比计算性能有所提高。该系统在三个数据库上进行了测试，一个包含 44 个图像的本地数据集和两个公开可用的数据库，包含 40 个图像的 INSPIRE-AVR 和包含 58 个图像的 VICAVR。本地数据库还包含具有病理病变结构的图像。通过将实验结果与金标准估计以及先前方法的结果进行比较来评估所提出系统的性能。总体而言，视网膜血管分离的准确度分别为 90.45%、93.90% 和 87.82%，Local、INSPIRE-AVR 和 VICAVR 数据集的动静脉比计算平均误差分别为 0.0565、0.0650 和 0.0849。