@InProceedings{PiresJeliWainRoch:2012:ReImQu,
author = "Pires, Ramon and Jelinek, Herbert F. and Wainer, Jacques and
Rocha, Anderson",
affiliation = "University of Campinas, UNICAMP, Campinas, Brazil and Charles
Sturt University, CSU, Albury, Australia and University of
Campinas, UNICAMP, Campinas, Brazil and University of Campinas,
UNICAMP, Campinas, Brazil",
title = "Retinal Image Quality Analysis for Automatic Diabetic Retinopathy
Detection",
booktitle = "Proceedings...",
year = "2012",
editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno,
Roberto and Silva, Luciano",
organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Retinal Quality Assessment, Field Definition, Blur Detection.",
abstract = "Sufficient image quality is a necessary prerequisite for reliable
automatic detection systems in several healthcare environments.
Specifically for Diabetic Retinopathy (DR) detection, poor quality
fundus 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 fundus 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%.",
conference-location = "Ouro Preto, MG, Brazil",
conference-year = "22-25 Aug. 2012",
doi = "10.1109/SIBGRAPI.2012.39",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2012.39",
language = "en",
ibi = "8JMKD3MGPBW34M/3CA2EH2",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3CA2EH2",
targetfile = "sibgrapi-2012-camera-ready-paper-101896.pdf",
urlaccessdate = "2024, Apr. 28"
}