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.

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system

Background:The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule(AM)ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.Methods:The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head.Two fundus photos of a total of 19 eyes from 10 subjects were imaged.Retinal vessels were analyzed to obtain the AN ratio.In addition,the vessel tree was extracted using deep learning U-NET,and vessel density was processed by the percentage of pixels within vessels over the entire image.Results:All images were successfully processed for the AN ratio and vessel density.There was no significant difference of averaged AN ratio between the first(0.77±0.09)and second(0.77±0.10)measurements(P=0.53).There was no significant difference of averaged vessel density(%)between the first(6.11±1.39)and second(6.12±1.40)measurements(P=0.85).Conclusions:Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope.The device appears to provide sufficient image quality for analyzing AN ratio and vessel density with the benefit of portability,easy data transferring,and low cost of the device,which could be used for pre-clinical screening of systemic,cerebral and ocular diseases.

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system

Background:The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule(A/V)ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.Methods:The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head.Two fundus photos of a total of 19 eyes from 10 subjects were imaged.Retinal vessels were analyzed to obtain the A/V ratio.In addition,the vessel tree was extracted using deep learning U-NET,and vessel density was processed by the percentage of pixels within vessels over the entire image.Results:All images were successfully processed for the A/V ratio and vessel density.There was no significant difference of averaged A/V ratio between the first(0.77±0.09)and second(0.77±0.10)measurements(P=0.53).There was no significant difference of averaged vessel density(%)between the first(6.11±1.39)and second(6.12±1.40)measurements(P=0.85).Conclusions:Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope.The device appears to provide sufficient image quality for analyzing A/V ratio and vessel density with the benefit of portability,easy data transferring,and low cost of the device,which could be used for pre-clinical screening of systemic,cerebral and ocular diseases.