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

Xiaoxia Yin,Yanchun Zhang,Samra Irshad

doi:10.1142/s1793545819500214

Xiaoxia Yin, Yanchun Zhang + Show 1 more

Open Access

https://doi.org/10.1142/s1793545819500214

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Abstract

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.

Highlights

Cardiovascular diseases including coronary heart disease, stroke and hypertension are characterized by the deviations in blood vascular structure.[1,2] In hypertension, arteries are altered from the regular pattern and the inner lining of arteries is damaged and as a result, they become thick and sti®.3 Due to this thickness of artery-walls in hypertension, the normal blood °ow pressure is a®ected.[4]
We aim to investigate if the proposed feature selection and classier fusion technique improves the retinal vessel classication task and subsequent Arteriovenous Ratio (AVR) calculation
The proposed methodology includes seven modules: (a) Automatic detection and segmentation of retinal vessels; (b) Extraction of novel feature set to categorize vessels; (c) Ranking of features by Pearson Correlation Coe±cient and Relief-F method; (d) Selection of features from ranked feature lists based on classication accuracy of three classiers; (e) Classication of vessels by hybrid classication framework using selected feature subset; (f) Calculation of width of vessels and (g) Calculation of Arteriovenous Ratio

Summary

Introduction

Cardiovascular diseases including coronary heart disease, stroke and hypertension are characterized by the deviations in blood vascular structure.[1,2] In hypertension, arteries are altered from the regular pattern and the inner lining of arteries is damaged and as a result, they become thick and sti®.3 Due to this thickness of artery-walls in hypertension, the normal blood °ow pressure is a®ected.[4]. The alteration of retinal vessel width is considered as an earliest biomarker of HR.[6] the width of arteries is narrowed in initial stages, that is why \arteriolar narrowing" is placed at initial stage in all the three scales proposed so far for HR diagnosis.[7,8,9,10] For assessment of arteriolar narrowing, a parameter called Arteriovenous Ratio (AVR), suggested by Stokoe and Turner,[11] is used It is the ratio of average diameter of retinal Arterioles (arteries) to average Venules (veins) diameter and its calculation involves the use of two other parameters known as Central Retinal Artery Equivalent and Central Retinal Venular Equivalent.[12,13] The deviation of this parameter from a normal range indicates the presence of HR so this biomarker is considered crucial for HR severity assessment.

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