Retinal image quality analysis for automatic diabetic retinopathy detection

R. Pires, Herbert Jelinek, J. Wainer, A. Rocha

Research output: Book chapter/Published conference paperConference paperpeer-review

45 Citations (Scopus)
5 Downloads (Pure)


Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fund us makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fund us images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%
Original languageEnglish
Title of host publicationIEEE Conference on Graphics, Patterns and Images
Place of PublicationUnited States
Number of pages8
Publication statusPublished - 2012
EventSIBGRAPI 2012: 25th Conference - Ouro Preto, Brazil, Brazil
Duration: 22 Aug 201225 Aug 2012

Publication series

ISSN (Print)1530-1834


ConferenceSIBGRAPI 2012: 25th Conference


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