Abstract
Retinal fundus images are widely used for screening, diagnosis and prognosis purposes in ophthalmology. Additionally these can also be used in retinal identification/recognition systems, for identification/authentication of an identity. In this paper the aim is to explain how norms in function spaces can be used to set up, automatically, classes of different retinal fundus images. These classifications rely on crucial and unique retinal features, such as the vascular network, whose location and measurement are appropriately quantified by weighted norms in function spaces. These quantifications can be understood as retinal pattern assessments and used for improving the efficiency and speed of retinal identification/recognition frameworks. The proposed methods are evaluated in a large dataset of retinal fundus images, and, besides being very fast, they achieve a reduction of the search in the dataset (for identification/recognition purposes), by 70% on average.
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Figueiredo, I.N., Neves, J.S., Moura, S., Oliveira, C.M., Ramos, J.D. (2014). Pattern Classes in Retinal Fundus Images Based on Function Norms. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_9
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DOI: https://doi.org/10.1007/978-3-319-09994-1_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09993-4
Online ISBN: 978-3-319-09994-1
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