Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

By Meindert Niemeijer, B. van Ginneken, S. Russell, M. Suttorp-Schulten, Michael David Abràmoff. Investigative Ophthalmology and Visual Science, 2007

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A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction (2015, methodology): “Zhang et al. [4] use mathematical morphological and contextual features for candidate extraction followed by random forest based classiﬁcation method for exudate detection.”
Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review (2012, background): “Automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed [52].”
Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets (2011, methodology): “, 2009) or additional types of bright lesions, such as drusen and cotton wool spots (Niemeijer et al., 2007; Fleming et al., 2010).”
A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images (2009, background): “Indeed, the reported lesion-based accuracies were often based on 10× 10 or 20× 20 patches (patch resolution) [8]–[10], and no pixel resolution validation was accomplished except in a couple of works [11], [12].”