Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48801
Title: Automated classification of exudates from digital fundus images
Authors: Rekhi, Ravitej Singh
Issac, Ashish
Dutta, Malay Kishore
Travieso, Carlos M. 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Medical Imaging
Diabetic Macular Edema
Fundus image
Exudates
Anisotropic Diffusion
SVM
Issue Date: 2017
Journal: 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings
Abstract: Diabetic Retinopathy and Diabetic Macular Edema are diseases that affect vision and eventually may lead to blindness. Early detection is a must to prevent the progression of the disease imploring the need for effective computer-aided diagnostic techniques. In the following research paper, a robust method has been proposed to segment hard exudates from digital, color fundus images using anisotropic diffusion and adaptive thresholding followed by a support vector machine for classification. The geometrical, shape and orientation features have been used to correctly classify the segmented objects as exudates or false pixels. The proposed technique has a high specificity and eliminates false positives correctly when applied across a wide range of images. The exudates segmented have a high degree of accuracy and no false positives are generated in case of non-diseased images. The proposed method has been tested on a total 189 images of the DIARETDB1 and MESSIDOR database and achieves an accuracy of 92.13% and 90% respectively. The proposed method can be used in the development for some computer aided technology for ocular diseases detection from fundus images.
URI: http://hdl.handle.net/10553/48801
ISBN: 9781538608500
DOI: 10.1109/IWOBI.2017.7985527
Source: 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings (7985527)
Appears in Collections:Actas de congresos
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