Hard Exudate Detection Using Local Texture Analysis and Gaussian Processes

Adrián Colomer*, Pablo Ruiz, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Exudates are the most noticeable sign in the first stage of diabetic retinopathy. This disease causes about five percent of world blindness. Making use of retinal fundus images, exudates can be detected, which helps the early diagnosis of the pathology. In this work, a novel method for automatic hard exudate detection is presented. After an exhaustive pre-processing step, Local Binary Patterns Variance (LBPV) histograms are used to locally extract texture information. We then use Gaussian Processes to distinguish between healthy and pathological retinal patches. The proposed methodology is validated using the E-OPHTA exudates database. The experimental results demonstrate that Gaussian Process classifiers outperform the current state of the art classifiers for this problem.

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
EditorsBart ter Haar Romeny, Fakhri Karray, Aurelio Campilho
PublisherSpringer Verlag
Pages639-649
Number of pages11
ISBN (Print)9783319929996
DOIs
StatePublished - 2018
Event15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal
Duration: Jun 27 2018Jun 29 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10882 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Image Analysis and Recognition, ICIAR 2018
CountryPortugal
CityPovoa de Varzim
Period6/27/186/29/18

Keywords

  • Bayesian modeling
  • Gaussian Processes
  • Hard exudate
  • Local Binary Patterns
  • Variational inference

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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