Articles producció científica> Enginyeria Informàtica i Matemàtiques

A feature selection strategy to optimize retinal vasculature segmentation

  • Identification data

    Identifier: imarina:9229576
    Authors:
    Escorcia-Gutierrez JTorrents-Barrena JGamarra MMadera NRomero-Aroca PValls APuig D
    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.
  • Others:

    Author, as appears in the article.: Escorcia-Gutierrez J; Torrents-Barrena J; Gamarra M; Madera N; Romero-Aroca P; Valls A; Puig D
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Escorcia Gutierrez, José Rafael / Puig Valls, Domènec Savi / Romero Aroca, Pedro / Valls Mateu, Aïda
    Keywords: Support vector machines Retinal vasculature segmentation Feature selection Diabetic retinopathy Decision trees Blood-vessel segmentation Artificial neural networks support vector machines retinal vasculature segmentation matched-filter intelligence images gray-level fundus feature selection diabetic-retinopathy decision trees complications artificial neural networks algorithm
    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.
    Thematic Areas: Modeling and simulation Mechanics of materials Mathematics, interdisciplinary applications Materials science, multidisciplinary Ensino Engineering, multidisciplinary Engenharias iv Electrical and electronic engineering Computer science, information systems Computer science applications Biomaterials Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: domenec.puig@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
    Author identifier: 0000-0002-0562-4205 0000-0002-7061-8987 0000-0003-3616-7809
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.techscience.com/cmc/v70n2/44677
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Cmc-Computers Materials & Continua. 70 (2): 2971-2989
    APA: Escorcia-Gutierrez J; Torrents-Barrena J; Gamarra M; Madera N; Romero-Aroca P; Valls A; Puig D (2022). A feature selection strategy to optimize retinal vasculature segmentation. Cmc-Computers Materials & Continua, 70(2), 2971-2989. DOI: 10.32604/cmc.2022.020074
    Article's DOI: 10.32604/cmc.2022.020074
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Biomaterials,Computer Science Applications,Computer Science, Information Systems,Electrical and Electronic Engineering,Engineering, Multidisciplinary,Materials Science, Multidisciplinary,Mathematics, Interdisciplinary Applications,Mechanics of Materials,Modeling and Simulation
    Support vector machines
    Retinal vasculature segmentation
    Feature selection
    Diabetic retinopathy
    Decision trees
    Blood-vessel segmentation
    Artificial neural networks
    support vector machines
    retinal vasculature segmentation
    matched-filter
    intelligence
    images
    gray-level
    fundus
    feature selection
    diabetic-retinopathy
    decision trees
    complications
    artificial neural networks
    algorithm
    Modeling and simulation
    Mechanics of materials
    Mathematics, interdisciplinary applications
    Materials science, multidisciplinary
    Ensino
    Engineering, multidisciplinary
    Engenharias iv
    Electrical and electronic engineering
    Computer science, information systems
    Computer science applications
    Biomaterials
    Astronomia / física
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