Open Access

ARTICLE

Detection and Classification of Hemorrhages in Retinal Images

Ghassan Ahmed Ali¹, Thamer Mitib Ahmad Al Sariera^2,*, Muhammad Akram¹, Adel Sulaiman¹, Fekry Olayah¹

1 College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
2 Department of Studies in Computer Science, University of Mysore, Mysore, India

* Corresponding Author: Thamer Mitib Ahmad Al Sariera. Email:

Computer Systems Science and Engineering 2023, 44(2), 1601-1616. https://doi.org/10.32604/csse.2023.026119

Received 16 December 2021; Accepted 02 March 2022; Issue published 15 June 2022

Abstract

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy (DR). Hemorrhages is the first clinically visible symptoms of DR. This paper presents a new technique to extract and classify the hemorrhages in fundus images. The normal objects such as blood vessels, fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages. For masking blood vessels, thresholding that separates blood vessels and background intensity followed by a new filter to extract the border of vessels based on orientations of vessels are used. For masking optic disc, the image is divided into sub-images then the brightest window with maximum variance in intensity is selected. Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques. Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features. Three different types of Support Vector Machine (SVM), Linear SVM, Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemorrhages or healthy. The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico. The performance of the method is measured based on average sensitivity, specificity, F-score and accuracy. Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.

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Keywords

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