International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 920 Automated Measurement of AVR Feature in Fundus Images using Image Processing and Machine Learning Abhay N. Nadkarni 1 , Ashish A. Phadatare 1 , Aditya B. Shah 1 , Prof. Sushma Kadge 2 1 Department of Electronics Engineering, K. J. Somaiya College of Engineering (Autonomous), (Affiliated to University of Mumbai), Mumbai, India. 2 Assistant Professor, Department of Electronics Engineering, K. J. Somaiya College of Engineering (Autonomous), (Affiliated to University of Mumbai), Mumbai, India. ---------------------------------------------------------------------***---------------------------------------------------------------------- ABSTRACT— Arteriolar-to-venal ratio (AVR) is an important parameter to predict whether a person may suffer from cardiovascular diseases, retinopathy of prematurity, diabetic retinopathy, cerebral atrophy and stroke among many others. However, adequate measures can be taken to avoid the harmful effects of these diseases or even cure them if changes in AVR can be detected early on. Manual and semi- automatic methods are not only difficult to implement, but they also prove to be inefficient and less accurate while dealing with thousands of images available. This paper explores and examines a proposed fully automatic method of grouping of blood vessels into arteries or veins, computation of arteriolar-to-venular ratio i.e. AVR and all other intermediate steps involved in the process. This paper corresponds to the project our team has undertaken and expounds on the stages completed. The software used in the project are MATLAB and Python. Images have been retrieved from DRIVE database [1]. INDEX TERMS—Artery vein ratio, retina, ROI, Vessels, Artery, Vein. 1) INTRODUCTION Cardiovascular diseases (CVDs) are one of the major causes of mortality around the globe. They are largely caused by heart strokes and heart related diseases. Such diseases can severely affect the thickness of retinal vessels, alter their curves, and/or transform their reflectance of light. Ophthalmologists have recognized this fact for years, viz. shrink of the thickness of arteries and expand the thicknesses of veins is possible. However, with recent advancements of technology, it is achievable to obtain accurate and precise measurement of retinal vessel parameters. This led to the possibility of recording subtle changes in the arteriolar-to-venal diameter ratio (AVR). These deviations are directly correlated with rises in the threat for cerebral atrophy, stroke myocardial infarct, and cognitive decline. An early discovery of the abnormality in the ratio can prevent severe damages in the future. Unfortunately, significant deviations in AVR are too refined to be identified by ophthalmologists through regular clinical inspection. Moreover, manually assessing the AVR for every individual through a digital retinal fundus image is not pragmatic and very cumbersome for clinical practice. Hence, an automatic process is favorable to calculate the AVR as it will revolutionize medical clinics, which will steer the process to an efficient evaluation of patients all around the globe with such diseases. Our paper attempts to calculate AVR automatically on any given color fundus image. It involves six core stages: Preprocessing, Vessel Segmentation, ROI detection, Vessel Width Measurement, Classification of vessels, and finally calculation of the ratio. The goal is to make all these stages automated where no input from the user is required. This paper has been divided into five sections. The first section describes the literature survey done in order to facilitate this project, highlighting the work which was previously done in this field and discussing their significance in solving the problem at hand. In the next section, there are details on the software used for this project, viz. MATLAB and Python wherein its merits and advantages over other frameworks are discussed. In the subsequent section, there is an in-depth discussion on the stages of the project which have been completed. In the final section, we talk about the future scope of this project and how to achieve the targets we have highlighted in the previous sections. 2) LITERATURE SURVEY The primary source of information while undertaking this project was the work by Meindert Niemeijer et al. [2]. This study highlighted various stages involved in actually automating the entire process of calculating the AVR of numerous fundus images, right from preprocessing of the image till classification of the blood vessels into arteries and veins and then