Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/69748
Título: Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy
Autores/as: Issac, Ashish
Dutta, Malay Kishore
Travieso González, Carlos Manuel 
Clasificación UNESCO: 320109 Oftalmología
3307 Tecnología electrónica
Fecha de publicación: 2020
Publicación seriada: Neural Computing and Applications 
Resumen: Diabetic retinopathy (DR) is one of the complications of diabetes affecting the eyes. If not treated at an early stage, then it can cause permanent blindness. The present work proposes a method for automatic detection of pathologies that are indicative parameters for DR and use them strategically in a framework to grade the severity of the disease. The bright lesions are highlighted using a normalization process followed by anisotropic diffusion and intensity threshold for detection of lesions which makes the algorithm robust to correctly reject false positives. SVM-based classifier is used to reject false positives using 10 distinct feature types. Red lesions are accurately detected from a shade-corrected green channel image, followed by morphological flood filling and regional minima operations. The rejection of false positives using geometrical features makes the system less complex and computationally efficient. A comprehensive quantitative analysis to grade the severity of the disease has resulted in an average sensitivity of 92.85 and 86.03% on DIARETDB1 and MESSIDOR databases, respectively.
URI: http://hdl.handle.net/10553/69748
ISSN: 0941-0643
DOI: 10.1007/s00521-018-3443-z
Fuente: Neural Computing and Applications [ISSN 0941-0643], n. 32, p. 15687–15697
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