Automatic Detection of Pathological Retinal Images Using Color and Shape Features

Authors

  • Flávio Henrique Duarte de Araújo Universidade Federal do Piauí, Campus Senador Helvídio Nunes de Barros, Picos. Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza
  • Rodrigo de Melo Souza Veras Departamento de Computação, Universidade Federal do Piauí, Teresina.
  • Romuere Rodrigues Veloso e Silva Universidade Federal do Piauí, Campus Senador Helvídio Nunes de Barros, Picos. Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza
  • André Macedo Santana Departamento de Computação, Universidade Federal do Piauí, Teresina.
  • Fátima Nelsizeuma Sombra de Medeiros Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza

Keywords:

Machine Learning, diagnostic Imaging, exudates.

Abstract

Objective: We propose an algorithm for exudate detection and pathological retinal images identification. Method: We improved an existing algorithm that detects exudates in a retinal image replacing the k-means clustering by fuzzy k-means and applied an additional step to detect optical disc (OD). Furthermore, our approach added a classification process to eliminate remaining false exudates regions. Finally, we classify the retinal image as pathological or non-pathological by measuring the ratio of candidate exudate regions before classification and the number of regions removed by the classification step. Results: Tests were performed on DIARETDB1 database, and the results obtained were; Fmeasure – 90%, area under the ROC curve – 88% and the Kappa coefficient – 77% (very good). Conclusion: The success of the algorithm is due mostly to the OD detection approach and the classification step. The obtained results confirmed that the proposed algorithm outperformed the others.

Published

2017-11-27

How to Cite

Duarte de Araújo, F. H., Souza Veras, R. de M., Veloso e Silva, R. R., Santana, A. M., & Sombra de Medeiros, F. N. (2017). Automatic Detection of Pathological Retinal Images Using Color and Shape Features. Journal of Health Informatics, 9(4). Retrieved from https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/519

Issue

Section

Original Articles

Similar Articles

<< < 3 4 5 6 7 8 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)