Dust particle artifact detection and removal in retinal images
Retinal fundus cameras suffer from dust particles attaching to the sensor and lens, which manifest as small artifacts on the images. We propose a new strategy for the detection and removal of dust particle artifacts in retinal images. We consider as input two or more color fundus images acquired wit...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8741
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8741
- Palabra clave:
- Artifact localization
Dust particle artifacts
Inpainting
Retinal image
Retinal image enhancement
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Dust particle artifact detection and removal in retinal images |
| title |
Dust particle artifact detection and removal in retinal images |
| spellingShingle |
Dust particle artifact detection and removal in retinal images Artifact localization Dust particle artifacts Inpainting Retinal image Retinal image enhancement |
| title_short |
Dust particle artifact detection and removal in retinal images |
| title_full |
Dust particle artifact detection and removal in retinal images |
| title_fullStr |
Dust particle artifact detection and removal in retinal images |
| title_full_unstemmed |
Dust particle artifact detection and removal in retinal images |
| title_sort |
Dust particle artifact detection and removal in retinal images |
| dc.subject.keywords.none.fl_str_mv |
Artifact localization Dust particle artifacts Inpainting Retinal image Retinal image enhancement |
| topic |
Artifact localization Dust particle artifacts Inpainting Retinal image Retinal image enhancement |
| description |
Retinal fundus cameras suffer from dust particles attaching to the sensor and lens, which manifest as small artifacts on the images. We propose a new strategy for the detection and removal of dust particle artifacts in retinal images. We consider as input two or more color fundus images acquired within the same session, in which we assume the artifacts remain in the same position. Our method consists in detecting candidate artifacts via normalized cross correlation with an artifact template, performing segmentation via region growing, and comparing the segmentations in all images. This guarantees that all detections are consistent for all images. The removal stage consists in an inpainting procedure so that the new region does not stand out from the neighboring regions. Encouraging experimental results show the localization of artifacts is effective and the artifacts are successfully removed, while not introducing new artifacts in the color retinal images. © Sociedad Española de Óptica. |
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2017 |
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2017 |
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2019-11-06T19:05:15Z |
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Optica Pura y Aplicada; Vol. 50, Núm. 4; pp. 379-387 |
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0030-3917 |
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10.7149/OPA.50.4.49075 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Optica Pura y Aplicada; Vol. 50, Núm. 4; pp. 379-387 0030-3917 10.7149/OPA.50.4.49075 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8741 |
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eng |
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eng |
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Sociedad Espanola de Optica |
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Sociedad Espanola de Optica |
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2019-11-06T19:05:15Z2019-11-06T19:05:15Z2017Optica Pura y Aplicada; Vol. 50, Núm. 4; pp. 379-3870030-3917https://hdl.handle.net/20.500.12585/874110.7149/OPA.50.4.49075Universidad Tecnológica de BolívarRepositorio UTBRetinal fundus cameras suffer from dust particles attaching to the sensor and lens, which manifest as small artifacts on the images. We propose a new strategy for the detection and removal of dust particle artifacts in retinal images. We consider as input two or more color fundus images acquired within the same session, in which we assume the artifacts remain in the same position. Our method consists in detecting candidate artifacts via normalized cross correlation with an artifact template, performing segmentation via region growing, and comparing the segmentations in all images. This guarantees that all detections are consistent for all images. The removal stage consists in an inpainting procedure so that the new region does not stand out from the neighboring regions. Encouraging experimental results show the localization of artifacts is effective and the artifacts are successfully removed, while not introducing new artifacts in the color retinal images. © Sociedad Española de Óptica.Universitat Politècnica de València: 2017-U009Recurso electrónicoapplication/pdfengSociedad Espanola de Opticahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85039953172&doi=10.7149%2fOPA.50.4.49075&partnerID=40&md5=ea8de9a65103126204d0ecbaa9cc2273Scopus 56682678200Scopus 24329839300Scopus 7201466399Dust particle artifact detection and removal in retinal imagesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Artifact localizationDust particle artifactsInpaintingRetinal imageRetinal image enhancementSierra, E.Marrugo Hernández, Andrés GuillermoMillán, M.S.Abramoff, M.D., Garvin, M., Sonka, M., Retinal Imaging and Image Analysis (2010) Biomedical Engineering, IEEE Reviews in, 3, pp. 169-208Willson, R.G., Maimone, M., Johnson, A., Scherr, L., (2005) An optical model for image artifacts produced by dust particles on lenses, pp. 1-8Zhou, C., Lin, S., (2007) "Removal of Image Artifacts Due to Sensor Dust,", pp. 1-8. , AprMora, A.D., Soares, J., Fonseca, J.M., "A template matching technique for artifacts detection in retinal images,", pp. 717-722. , presented at the 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)Niemeijer, 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, pp. 888-898Marrugo, A.G., Millan, M.S., Cristóbal, G., Gabarda, S., Abril, H.C., No-reference Quality Metrics for Eye Fundus Imaging (2011) CAIP 2011 LNCS, 6854, pp. 486-493Köhler, T., Budai, A., Kraus, M., Odstrcilik, J., Michelson, G., Hornegger, J., "Automatic no-reference quality assessment for retinal fundus images using vessel segmentation.," (2013) presented at the IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), pp. 95-100Ali Shah, S.A., Laude, A., Faye, I., Tang, T.B., Automated microaneurysm detection in diabetic retinopathy using curvelet transform (2016) J. Biomed. Opt, 21Yang, P., Chen, L., Tian, J., Xu, X., "Dust particle detection in surveillance video using salient visual descriptors," (2016) Computers & Electrical EngineeringChen, L., Zhu, D., Tian, J., Liu, J., "Dust particle detection in traffic surveillance video using motion singularity analysis," (2016) Digit Signal Process, pp. 1-7Marrugo, A.G., Sierra, E., Millan, M.S., (2016) "Dust Particle Detection and Correction in Retinal Images,", , presented at the RIAO-OPTILAS 2016, Pucón, Chile, 268Radke, R., Andra, S., Al-Kofahi, O., Roysam, B., Image change detection algorithms: a systematic survey (2005) Image Processing, IEEE Transactions on, 14, pp. 294-307Marrugo, A.G., Sorel, M., Sroubek, F., Millan, M.S., Retinal image restoration by means of blind deconvolution (2011) J. Biomed. Opt, 16Marrugo, A.G., Millan, M.S., Retinal Image Analysis Oriented to the Clinical Task (2014) Electronic Letters on Computer Vision and Image Analysis, 13, pp. 54-55Gonzalez, R.C., Woods, R.E., Eddins, S.L., (2010) Digital Image Processing Using MATLAB®, , McGraw Hill EducationTsai, D.-M., Lin, C.-T., Fast normalized cross correlation for defect detection (2003) Pattern Recognition Letters, 24, pp. 2625-2631Marrugo, A.G., Millan, M.S., Optic disc segmentation in retinal images (2010) Opt. Pura Apl, 43, pp. 79-86Neubeck, A., Van Gool, L., "Efficient non-maximum suppression," (2006) presented at the Pattern Recognition, 2006.ICPR 2006. 18th International Conference on, 3, pp. 850-855Haralick, R.M., Sternberg, S.R., Zhuang, X., Image analysis using mathematical morphology (1987) Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, pp. 532-550http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_7149OPA_50_4_49075.pdfapplication/pdf9350562https://repositorio.utb.edu.co/bitstream/20.500.12585/8741/1/DOI10_7149OPA_50_4_49075.pdf117f1e2a77bc8b7e482f2bf07909799eMD51TEXTDOI10_7149OPA_50_4_49075.pdf.txtDOI10_7149OPA_50_4_49075.pdf.txtExtracted texttext/plain24992https://repositorio.utb.edu.co/bitstream/20.500.12585/8741/4/DOI10_7149OPA_50_4_49075.pdf.txte53e1e8f5a39cca00c9e9833bf421e9dMD54THUMBNAILDOI10_7149OPA_50_4_49075.pdf.jpgDOI10_7149OPA_50_4_49075.pdf.jpgGenerated Thumbnailimage/jpeg81938https://repositorio.utb.edu.co/bitstream/20.500.12585/8741/5/DOI10_7149OPA_50_4_49075.pdf.jpgaddacd09a160a6f7d2d20cf6b381dc39MD5520.500.12585/8741oai:repositorio.utb.edu.co:20.500.12585/87412023-05-26 16:24:58.92Repositorio Institucional UTBrepositorioutb@utb.edu.co |