Fundus image texture features analysis in diabetic retinopathy diagnosis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

This paper investigates texture feature capabilities from fundus images to differentiate between diabetic retinopathy (DR), age-related macular degeneration (AMD) screening and normal. Our proposed method using improvement of local binary pattern (LBP) with calculation of LBP original value and magnitude value of fundus images. This method is compared with Local Line Binary Pattern (LLBP). In this study, four experiments (DR-Normal, DR-AMD, AMD-Normal, Multiclass) were designed for two databases, DIARETDB0 database and STARE. Kernel PCA is choosed as feature selection method, and three classifiers are tested (Naive Bayes, SVM, and KNN). The experimental results show that our proposed method has higher accuracy than LLBP, with accuracy of binary classification 100% for DR-Normal and AMD-Normal. While, multiclass classification (DR-AMD-Normal) achieves an accuracy 80-84%. These results suggest that our proposed method in this paper can be useful in a diagnosis aid system for diabetic retinopathy.

Original languageEnglish
Title of host publication2017 11th International Conference on Sensing Technology, ICST 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
ISBN (Electronic)9781509065264
DOIs
Publication statusPublished - 27 Feb 2018
Event11th International Conference on Sensing Technology, ICST 2017 - Sydney, Australia
Duration: 4 Dec 20176 Dec 2017

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
Volume2017-December
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073

Conference

Conference11th International Conference on Sensing Technology, ICST 2017
Country/TerritoryAustralia
CitySydney
Period4/12/176/12/17

Keywords

  • Diabetes mellitus
  • Diabetic retinopathy
  • Fundus images
  • LBP

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