Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/6031
Title: Retinal image quality assessment by mean-subtracted contrast-normalized coefficients
Authors: Adrian Galdran
Teresa Finisterra Araújo
Ana Maria Mendonça
Aurélio Campilho
Issue Date: 2018
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.
URI: http://repositorio.inesctec.pt/handle/123456789/6031
http://dx.doi.org/10.1007/978-3-319-68195-5_92
metadata.dc.type: article
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Appears in Collections:C-BER - Articles in International Journals

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