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The development of an automatic system for screening and grading of diabetic retinopathy depends on the detection of red lesions in retinal fundus images. In this paper, a novel method for automatic detection of both microa-neurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to dis-criminate between lesions and blood vessel segments. The method is validated per-lesion and per image using five databases, three of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor data-base, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

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R.Jayanthi, V.Balaji, R.Bairavi, S.Mahendran, & S.Mayilkodi. (2017). Red lesion detection in retinal fundus image for dynamic shape features using diabetic retinopathy grading . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(2), 1343–1353. Retrieved from