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A feature selection strategy to optimize retinal vasculature segmentation

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اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • بيانات النشر:
      Tech Science Press
      United States
    • الموضوع:
      2022
    • Collection:
      REDICUC - Repositorio Universidad de La Costa
    • نبذة مختصرة :
      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.
    • File Description:
      15 páginas; application/pdf
    • ISSN:
      1546-2218
      1546-2226
    • Relation:
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    • الرقم المعرف:
      10.32604/cmc.2022.020074
    • الدخول الالكتروني :
      https://doi.org/10.32604/cmc.2022.020074
      https://hdl.handle.net/11323/9061
      https://repositorio.cuc.edu.co/
    • Rights:
      © 1997-2020 TSP (Henderson, USA) unless otherwise stated ; Atribución 4.0 Internacional (CC BY 4.0) ; https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess ; http://purl.org/coar/access_right/c_abf2
    • الرقم المعرف:
      edsbas.4F522A8F