Geometric Multiscale Representations and Applications to the Analysis to Retinal Fundus Images



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Systematic diseases, such as diabetes, are known to cause quantifiable changes to the geometry of the retinal microvasculature. This microvasculature is the only part of the human circulation that can be visualized non-invasively in vivo so that it can be readily photographed and processed with the tools of digital image analysis. As the treatment of serious pathological conditions such as diabetic retinopathy can be significantly improved with early detection, retinal image analysis has been the subject of extensive studies. Thanks to the advances in image processing and machine learning during the last decade, a remarkable progress is being made towards developing automated diagnostic systems for diabetic retinopathy and related conditions. Despite this progress though, significant challenges remain. In this thesis, we develop and apply a novel method based on directional multiscale representations to the analysis of retinal fundus images. Namely, we construct a multiscale geometric feature descriptor to quantify the morphology of retinal vascularization and apply this descriptor within a supervised machine learning environment for problems of retinal image classification. By combining multiscale analysis and geometric sensitivity, our method provide a very competitive for the quantification of changes to the geometry of the retinal microvasculature. With respect to state-of-the-art methods based on deep learning, our approach is easily interpretable since the features we compute are morphological descriptors of retinal vascularization.



Fundus Images, Feature Extraction, Diabetic Rietibopathy classification.