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Robust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting

dc.contributor.editorAlam M.S.
dc.creatorSierra E.
dc.creatorBarrios E.
dc.creatorMarrugo A.G.
dc.creatorMillán M.S.
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering; Vol. 10995
dc.description.abstractRetinal images are acquired with eye fundus cameras which, like any other camera, can suffer from dust particles attached to the sensor and lens. These particles impede light from reaching the sensor, and therefore they appear as dark spots in the image which can be mistaken as small lesions like microaneurysms. We propose a robust method for detecting dust artifacts from more than one image as input and, for the removal, we propose a sparse-based inpainting technique with dictionary learning. The detection is based on a closing operation to remove small dark features. We compute the difference with the original image to highlight the artifacts and perform a filtering approach with a filter bank of artifact models of different sizes. The candidate artifacts are identified via non-maxima suppression. Because the artifacts do not change position in the images, after processing all input images, the candidate artifacts which are not in the same approximate position in different images are rejected and kept unchanged in the image. The experimental results show that our method can successfully detect and remove artifacts, while ensuring the continuity of retinal structures, such as blood vessels. © 2019 SPIE. Downloading of the abstract is permitted for personal use only.eng
dc.description.sponsorshipUniversitat Politècnica de València, UPV ARC Centre of Excellence in Cognition and its Disorders, CCD
dc.description.sponsorshipThe Society of Photo-Optical Instrumentation Engineers (SPIE)
dc.format.mediumRecurso electrónico
dc.titleRobust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting
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dc.source.eventPattern Recognition and Tracking XXX 2019
dc.subject.keywordsArtifact detection
dc.subject.keywordsDictionary learning
dc.subject.keywordsDust particle
dc.subject.keywordsRetinal image
dc.subject.keywordsSensor artifact.
dc.subject.keywordsBlood vessels
dc.subject.keywordsArtifact detection
dc.subject.keywordsDictionary learning
dc.subject.keywordsDust particle
dc.subject.keywordsRetinal image
dc.subject.keywordsPattern recognition
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.description.notesThe authors acknowledge the financial s upport f rom t he C entre d e C ooperació i D esenvolupament (CCD) at the Universitat Politècnica de Catalunya under project ref. CCD2018-U005, and from the Universidad Tec-nológica de Bol´ıvar. Authors are grateful to Juan Lu´ıs Fuentes from the Miguel Servet University Hospital (Zaragoza, Spain) for providing the images.
dc.relation.conferencedate15 April 2019 through 16 April 2019

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