A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques

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Author(s): Azam Asilian Bidgoli (University of Kashan, Iran), Hossein Ebrahimpour-Komleh (University of Kashan, Iran)and Seyed Jalaleddin Mousavirad (University of Kashan, Iran)
Copyright: 2018
Pages: 31
Source title: Ophthalmology: Breakthroughs in Research and Practice
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-5195-9.ch009

Keywords: Medical Diagnosis and Practice / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialist are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic retinopathy is a difficult computer vision problem –with the goal of detecting features of retinopathy. The present chapter is written with the purpose of analyzing and comparing different feature extraction methods to evaluate the best algorithm for detection retinopathy with least error. Extracted features using these methods are used to classify images into normal and altered groups.

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A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques

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