LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting
In the field of ophthalmology, retinal images are essential for the diagnosis of many diseases. These images are acquired with a device called the retinal camera. However, often small dust particles in the sensor produce image artifacts that can be confused with small lesions, such as micro-aneurysm...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9157
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9157
- Palabra clave:
- Dictionary Learning
Inpainting
Retinal image
Sparse representation
Blood vessels
Diagnosis
Dust
Ophthalmology
Vision
Clinical settings
Dictionary learning
Inpainting
Inpainting process
Inpainting techniques
Retinal image
Retinal structure
Sparse representation
Image processing
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| title |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| spellingShingle |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting Dictionary Learning Inpainting Retinal image Sparse representation Blood vessels Diagnosis Dust Ophthalmology Vision Clinical settings Dictionary learning Inpainting Inpainting process Inpainting techniques Retinal image Retinal structure Sparse representation Image processing |
| title_short |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| title_full |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| title_fullStr |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| title_full_unstemmed |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| title_sort |
LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpainting |
| dc.subject.keywords.none.fl_str_mv |
Dictionary Learning Inpainting Retinal image Sparse representation Blood vessels Diagnosis Dust Ophthalmology Vision Clinical settings Dictionary learning Inpainting Inpainting process Inpainting techniques Retinal image Retinal structure Sparse representation Image processing |
| topic |
Dictionary Learning Inpainting Retinal image Sparse representation Blood vessels Diagnosis Dust Ophthalmology Vision Clinical settings Dictionary learning Inpainting Inpainting process Inpainting techniques Retinal image Retinal structure Sparse representation Image processing |
| description |
In the field of ophthalmology, retinal images are essential for the diagnosis of many diseases. These images are acquired with a device called the retinal camera. However, often small dust particles in the sensor produce image artifacts that can be confused with small lesions, such as micro-aneurysms. The digital removal of artifacts can be understood as an inpainting process in which a set of pixels are replaced with a value obtained from the surrounding area. In this paper, we propose a methodology based on the sparse representations and dictionary learning for the removal of artifacts in retinal images. We test our method on real retinal images coming from the clinical setting with actual dust artifacts. We compare our restoration results with a diffusion-based inpainting technique. Encouraging experimental results show that our method can successfully remove the artifacts, while assuring the continuity of the retinal structures, like blood vessels. © 2019 IEEE. |
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2019 |
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2019 |
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2020-03-26T16:33:05Z |
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2020-03-26T16:33:05Z |
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2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings |
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9781728114910 |
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10.1109/STSIVA.2019.8730253 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57209542195 24329839300 7201466399 |
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2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings 9781728114910 10.1109/STSIVA.2019.8730253 Universidad Tecnológica de Bolívar Repositorio UTB 57209542195 24329839300 7201466399 |
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https://hdl.handle.net/20.500.12585/9157 |
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eng |
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eng |
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24 April 2019 through 26 April 2019 |
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2020-03-26T16:33:05Z2020-03-26T16:33:05Z20192019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings9781728114910https://hdl.handle.net/20.500.12585/915710.1109/STSIVA.2019.8730253Universidad Tecnológica de BolívarRepositorio UTB57209542195243298393007201466399In the field of ophthalmology, retinal images are essential for the diagnosis of many diseases. These images are acquired with a device called the retinal camera. However, often small dust particles in the sensor produce image artifacts that can be confused with small lesions, such as micro-aneurysms. The digital removal of artifacts can be understood as an inpainting process in which a set of pixels are replaced with a value obtained from the surrounding area. In this paper, we propose a methodology based on the sparse representations and dictionary learning for the removal of artifacts in retinal images. We test our method on real retinal images coming from the clinical setting with actual dust artifacts. We compare our restoration results with a diffusion-based inpainting technique. Encouraging experimental results show that our method can successfully remove the artifacts, while assuring the continuity of the retinal structures, like blood vessels. © 2019 IEEE.CCD2018-U005IEEE Colombia Section;IEEE Signal Processing Society Colombia Chapter;Universidad Industrial de SantanderThe authors acknowledge the financial support of Centre de Cooperació i Desenvolupament at the Univer-sitat Politècnica de Catalunya (project CCD2018-U005). Authors are grateful to J. L. Fuentes from the Miguel Servet University Hospital (Zaragoza, Spain) for providing images, and to E. Sierra for providing the code for inpainting by diffusion for the comparison experiments.Recurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068035770&doi=10.1109%2fSTSIVA.2019.8730253&partnerID=40&md5=44dee9e1fd305fcd98e966b26cb4f4cbScopus2-s2.0-8506803577022nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019LRemoving Dust Artifacts in Retinal Images via Dictionary Learning and Sparse-Based Inpaintinginfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fDictionary LearningInpaintingRetinal imageSparse representationBlood vesselsDiagnosisDustOphthalmologyVisionClinical settingsDictionary learningInpaintingInpainting processInpainting techniquesRetinal imageRetinal structureSparse representationImage processing24 April 2019 through 26 April 2019Barrios E.M.Marrugo A.G.Millán M.S.Abràmoff, M.D., Garvin, M.K., Sonka, M., Retinal imaging and image analysis (2010) IEEE Reviews in Biomedical Engineering, 3, pp. 169-208Guillemot, C., Le Meur, O., Image inpainting: Overview and recent advances (2014) IEEE Signal Processing Magazine, 31 (1), pp. 127-144Elad, M., From exact to approximate solutions (2010) Sparse and Redundant Representations, pp. 79-109. , SpringerToic, I., Frossard, P., Dictionary learning: What is the right representation for my signal (2011) IEEE Signal Processing Magazine, 28 (2), pp. 27-38Ogawa, T., Haseyama, M., Image inpainting based on sparse representations with a perceptual metric (2013) EURASIP Journal on Advances in Signal Processing, 2013 (1), p. 179Sierra, E., Marrugo, A.G., Millan, M.S., Dust particle artifact detection and removal in retinal images (2017) Opt. Pura Apl., 50, pp. 379-387. , DecBruckstein, A.M., Donoho, D.L., Elad, M., From sparse solutions of systems of equations to sparse modeling of signals and images (2009) SIAM Review, 51 (1), pp. 34-81Manat, S., Zhang, Z., Matching pursuit in a time-frequency dictionary (1993) IEEE Trans Signal Processing, 12, pp. 3397-3451. , Fig. 8: [From left column to right column] Original image (a, d, )), restored image (b, e, h), and the absolute difference (c, f, i). Other restoration results in RGB color fundus imagesTibshirani, R., Regression shrinkage and selection via the lasso (1996) Journal of the Royal Statistical Society. Series B (Methodological), pp. 267-288Rubinstein, R., Peleg, T., Elad, M., Analysis k-SVD: A dictionary-learning algorithm for the analysis sparse model (2013) IEEE Transactions on Signal Processing, 61 (3), pp. 661-677Aharon, M., Elad, M., Bruckstein, A., RMK-SVD: An algorithm for designing overcomplete dictionaries for sparse representation (2006) IEEE Transactions on Signal Processing, 54 (11), pp. 4311-4322Elad, M., Aharon, M., Image denoising via sparse and redundant representations over learned dictionaries (2006) IEEE Transactions on Image Processing, 15 (12), pp. 3736-3745Elad, M., Starck, J.-L., Querre, P., Donoho, D.L., Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) (2005) Applied and Computational Harmonic Analysis, 19 (3), pp. 340-358Shen, B., Hu, W., Zhang, Y., Zhang, Y.-J., Image inpainting via sparse representation (2009) Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pp. 697-700. , IEEEYang, J., Wright, J., Huang, T.S., Ma, Y., Image super-resolution via sparse representation (2010) IEEE Transactions on Image Processing, 19 (11), pp. 2861-2873Zhang, J., Zhao, D., Gao, W., Group-based sparse representation for image restoration (2014) IEEE Transactions on Image Processing, 23 (8), pp. 3336-3351Rubinstein, R., Zibulevsky, M., Elad, M., (2009) Learning Sparse Dictionaries for Sparse Signal Approximation, , tech. rep., Computer Science Department, TechnionTrucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J.P., Al-Diri, B., Abramoff, M., Validating retinal fundus image analysis algorithms: Issues and a proposal (2013) Investigative Ophthalmology & Visual Science, 54 (5), pp. 3546-3559http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9157/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9157oai:repositorio.utb.edu.co:20.500.12585/91572021-02-02 14:44:28.559Repositorio Institucional UTBrepositorioutb@utb.edu.co |