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Metadata

@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",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "sibgrapi-2012-camera-ready-paper-101896.pdf",
        urlaccessdate = "2021, Jan. 24"
}


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