Abstract: The quality of retina images is important for the diagnosis and treatment of eye diseases. Blur, distortion, low contrast, among other artifacts, inhibit the viewing of regions of interest. This work proposes a global descriptor based on two extinction values (number of descendants and...

Abstract: The quality of retina images is important for the diagnosis and treatment of eye diseases. Blur, distortion, low contrast, among other artifacts, inhibit the viewing of regions of interest. This work proposes a global descriptor based on two extinction values (number of descendants and topological height) for direct analysis of fundus photographs and automatic classification as "good" or "poor" quality. The method does not require any special filtering, image segmentation or internal retina structure location. It is not substantially affected by rotating, resizing or data set type, and is computed in quadratic time. The algorithm achieved an area under ROC curve of 96.13% for UNICAMP, 88.59% for UNIFESP DR2, 99.27% for DRIMDB, and 92.07% for HRF data sets

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ

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