Retinal image quality assessment by mean-subtracted contrast-normalized coefficients

dc.contributor.author Adrian Galdran en
dc.contributor.author Teresa Finisterra Araújo en
dc.contributor.author Ana Maria Mendonça en
dc.contributor.author Aurélio Campilho en
dc.date.accessioned 2018-01-13T18:41:46Z
dc.date.available 2018-01-13T18:41:46Z
dc.date.issued 2018 en
dc.description.abstract The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost. © 2018, Springer International Publishing AG. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6031
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-68195-5_92 en
dc.language eng en
dc.relation 6071 en
dc.relation 6825 en
dc.relation 6381 en
dc.relation 6320 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Retinal image quality assessment by mean-subtracted contrast-normalized coefficients en
dc.type article en
dc.type Publication en
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