Relationship of Gensini score with retinal vessel diameter and arteriovenous ratio in senile CHD

Background This study aimed to find the correlation of Gensini score with retinal vessel diameter and arteriovenous ratio in elderly patients with coronary heart disease (CHD). Methods This study included 120 senile CHD patients as the CHD group and 100 healthy individuals as the normal group (NG). Gensini score was used to evaluate the severity of coronary artery lesions. Central retinal artery equivalents (CRAE), central retinal venular equivalents (CRVE), and arteriovenous ratio (AVR) were measured. Results CHD group has lower CRAE and AVR than NG, while higher CRVE was observed in NG. CRAE and AVR in UAP (unstable angina pectoris) and AMI (acute myocardial infarction) groups showed reduction (stable angina pectoris); however, enhanced CRVE and Gensini scores in UA and AMI groups were observed as compared to the SAP group. CRAE and AVR in moderate and severe groups were reduced to a greater extent compared to the mild groups, while enhanced CRVE and Gensini scores were observed more often in the severe group than the mild group. CRAE and AVR were negatively correlated with the Gensini score; however, CRVE was positively correlated with the Gensini score. Conclusion AVR is expected to be a noninvasive index to diagnose and predict senile CHD, which has a certain evaluation value. Diabetes, smoking history, and TC are independent risk factors of senile CHD.

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 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.