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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.date.accessioned2020-03-26T16:33:09Z
dc.date.available2020-03-26T16:33:09Z
dc.date.issued2019
dc.identifier.citationProceedings of SPIE - The International Society for Optical Engineering; Vol. 10995
dc.identifier.isbn9781510626553
dc.identifier.issn0277786X
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9186
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.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSPIE
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072595580&doi=10.1117%2f12.2519053&partnerID=40&md5=4929692788b6e66ba264a2136cd81838
dc.sourceScopus2-s2.0-85072595580
dc.titleRobust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting
dcterms.bibliographicCitationAbrámoff, M.D., Garvin, M.K., Sonka, M., Retinal imaging and image analysis (2010) IEEE Reviews in Biomedical Engineering, 3, pp. 169-208
dcterms.bibliographicCitationMarrugo, A.G., Retinal image analysis oriented to the clinical task (2014) Electronic Letters on Computer Vision and Image Analysis, 13 (2), pp. 54-55
dcterms.bibliographicCitationMarrugo, A.G., Millan, M.S., Retinal image analysis: Image processing and feature extraction oriented to the clinical task (2017) Optica Pura y Aplicada, 50 (1), pp. 49-62
dcterms.bibliographicCitationSuzuki, N., Distinction between manifestations of diabetic retinopathy and dust artifacts using threedimensional hsv color space (2016) World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 10 (3), pp. 153-159
dcterms.bibliographicCitationNarasimha-Iyer, H., Can, A., Roysam, B., Stewart, V., Tanenbaum, H.L., Majerovics, A., Singh, H., Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy (2006) IEEE Transactions on Biomedical Engineering, 53 (6), pp. 1084-1098
dcterms.bibliographicCitationWillson, R.G., Maimone, M.W., Johnson, A.E., Scherr, L.M., An optical model for image artifacts produced by dust particles on lenses (2005) 8th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (I-SAIRAS)
dcterms.bibliographicCitationMora, A.D., Soares, J., Fonseca, J.M., A template matching technique for artifacts detection in retinal images (2013) 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 717-722. , IEEE
dcterms.bibliographicCitationNiemeijer, M., Abramoff, M.D., Van Ginneken, B., Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening (2006) Medical Image Analysis, 10 (6), pp. 888-898
dcterms.bibliographicCitationMarrugo, A.G., Millán, M.S., Cristóbal, G., Gabarda, S., Abril, H.C., No-reference quality metrics for eye fundus imaging (2011) Computer Analysis of Images and Patterns, pp. 486-493. , Springer
dcterms.bibliographicCitationKöhler, T., Budai, A., Kraus, M.F., Odstrcilik, J., Michelson, G., Hornegger, J., Automatic noreference quality assessment for retinal fundus images using vessel segmentation (2013) Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95-100. , IEEE
dcterms.bibliographicCitationShah, S.A.A., Laude, A., Faye, I., Tang, T.B., Automated microaneurysm detection in diabetic retinopathy using curvelet transform (2016) Journal of Biomedical Optics, 21 (10), p. 101404
dcterms.bibliographicCitationYang, P., Chen, L., Tian, J., Xu, X., Dust particle detection in surveillance video using salient visual descriptors (2017) Computers & Electrical Engineering, 62, pp. 224-231
dcterms.bibliographicCitationChen, L., Zhu, D., Tian, J., Liu, J., Dust particle detection in traffic surveillance video using motion singularity analysis (2016) Digital Signal Processing, 58, pp. 127-133
dcterms.bibliographicCitationHu, L., Chen, L., Cheng, J., Gray spot detection in surveillance video using convolutional neural network (2018) 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2806-2810. , IEEE
dcterms.bibliographicCitationSierra, E., Marrugo, A.G., Millán, M.S., Dust particle artifact detection and removal in retinal images (2017) Ó Ptica Pura y Aplicada, 50 (4), pp. 379-387
dcterms.bibliographicCitationGonzalez, W., Woods, R.E., (2004) Eddins, Digital Image Processing Using Matlab, , Third New Jersey: Prentice Hall
dcterms.bibliographicCitationLewis, J., Fast normalized cross-correlation (1995) Vision Interface, 10 (1), pp. 120-123
dcterms.bibliographicCitationZhou, C., Lin, S., Removal of image artifacts due to sensor dust (2007) Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference On, pp. 1-8. , IEEE
dcterms.bibliographicCitationElad, M., From exact to approximate solutions (2010) Sparse and Redundant Representations, pp. 79-109. , Springer
dcterms.bibliographicCitationGuillemot, C., Le Meur, O., Image inpainting: Overview and recent advances (2014) IEEE Signal Processing Magazine, 31 (1), pp. 127-144
dcterms.bibliographicCitationEngan, K., Aase, S.O., Husoy, J.H., Method of optimal directions for frame design (1999) Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference On, 5, pp. 2443-2446. , IEEE
dcterms.bibliographicCitationAharon, M., Elad, M., Bruckstein, A., K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation (2006) IEEE Transactions on Signal Processing, 54 (11), p. 4311
dcterms.bibliographicCitationManat, S., Zhang, Z., Matching pursuit in a time-frequency dictionary (1993) IEEE Trans Signal Processing, 12, pp. 3397-3451
datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.eventPattern Recognition and Tracking XXX 2019
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1117/12.2519053
dc.subject.keywordsArtifact detection
dc.subject.keywordsDictionary learning
dc.subject.keywordsDust particle
dc.subject.keywordsInpainting
dc.subject.keywordsRetinal image
dc.subject.keywordsSensor artifact.
dc.subject.keywordsBlood vessels
dc.subject.keywordsCameras
dc.subject.keywordsDust
dc.subject.keywordsOphthalmology
dc.subject.keywordsArtifact detection
dc.subject.keywordsDictionary learning
dc.subject.keywordsDust particle
dc.subject.keywordsInpainting
dc.subject.keywordsRetinal image
dc.subject.keywordsPattern recognition
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
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
dc.type.spaConferencia
dc.identifier.orcid56682678200
dc.identifier.orcid57209542195
dc.identifier.orcid24329839300
dc.identifier.orcid7201466399


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