Open Access

ARTICLE

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

José Escorcia-Gutierrez^1,4,*, Jordina Torrents-Barrena⁴, Margarita Gamarra², Natasha Madera¹, Pedro Romero-Aroca³, Aida Valls⁴, Domenec Puig⁴

1 Electronic and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 080001, Colombia
2 Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, 080001, Colombia
3 Ophthalmology Service, Universitari Hospital Sant Joan, Institut de Investigacio Sanitaria Pere Virgili, Reus, 43201, Spain
4 Departament d’Enginyeria Informàtica i Matemàtiques, Escola Tècnica Superior d’Enginyeria, Universitat Rovira i Virgili, Tarragona, 43007, Spain

* Corresponding Author: José Escorcia-Gutierrez. Email:

Computers, Materials & Continua 2022, 70(2), 2971-2989. https://doi.org/10.32604/cmc.2022.020074

Received 08 May 2021; Accepted 24 June 2021; Issue published 27 September 2021

Abstract

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.

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Keywords

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