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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyPiresJeliWainRoch:2012:ReImQu
TitleRetinal Image Quality Analysis for Automatic Diabetic Retinopathy Detection
FormatDVD, On-line.
Access Date2021, Jan. 28
Number of Files1
Size647 KiB
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Author1 Pires, Ramon
2 Jelinek, Herbert F.
3 Wainer, Jacques
4 Rocha, Anderson
Affiliation1 University of Campinas, UNICAMP, Campinas, Brazil
2 Charles Sturt University, CSU, Albury, Australia
3 University of Campinas, UNICAMP, Campinas, Brazil
4 University of Campinas, UNICAMP, Campinas, Brazil
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:29 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsRetinal Quality Assessment, Field Definition, Blur Detection.
AbstractSufficient 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%.
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