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

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

Azam Asilian Bidgoli, Hossein Ebrahimpour-Komleh, Seyed Jalaleddin Mousavirad
Copyright: © 2018 |Pages: 31
ISBN13: 9781522551959|ISBN10: 1522551956|EISBN13: 9781522551966
DOI: 10.4018/978-1-5225-5195-9.ch009
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MLA

Bidgoli, Azam Asilian, et al. "A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques." Ophthalmology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2018, pp. 122-152. https://doi.org/10.4018/978-1-5225-5195-9.ch009

APA

Bidgoli, A. A., Ebrahimpour-Komleh, H., & Mousavirad, S. J. (2018). A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques. In I. Management Association (Ed.), Ophthalmology: Breakthroughs in Research and Practice (pp. 122-152). IGI Global. https://doi.org/10.4018/978-1-5225-5195-9.ch009

Chicago

Bidgoli, Azam Asilian, Hossein Ebrahimpour-Komleh, and Seyed Jalaleddin Mousavirad. "A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques." In Ophthalmology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 122-152. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5195-9.ch009

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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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