Retinal image analysis: Image processing and feature extraction oriented to the clinical task
Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract quantitative information about many diseases. Modern ophthalmology relies on the advances in digital...
- 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:20.500.12585/8744
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8744
- Palabra clave:
- Computer-aided diagnosis
Medical image
Ophthalmology
Retinal image
Telemedicine
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
| title |
Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
| spellingShingle |
Retinal image analysis: Image processing and feature extraction oriented to the clinical task Computer-aided diagnosis Medical image Ophthalmology Retinal image Telemedicine |
| title_short |
Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
| title_full |
Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
| title_fullStr |
Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
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Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
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Retinal image analysis: Image processing and feature extraction oriented to the clinical task |
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Computer-aided diagnosis Medical image Ophthalmology Retinal image Telemedicine |
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Computer-aided diagnosis Medical image Ophthalmology Retinal image Telemedicine |
| description |
Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract quantitative information about many diseases. Modern ophthalmology relies on the advances in digital imaging and computing power. In this paper we present an overview of the results from the doctoral dissertation by Andrés G. Marrugo. This dissertation contributes to the digital analysis of retinal images and the problems that arise along the imaging pipeline of fundus photography, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination compensation, poor image quality, automated focusing, image segmentation, change detection, space-invariant (SI) and space-variant (SV) blind deconvolution (BD). Digital retinal image analysis can be effective and cost-efficient for disease management, computeraided diagnosis, screening and telemedicine and applicable to a variety of disorders such as glaucoma, macular degeneration, and retinopathy. © 2017. Sociedad Española de Óptica. All right reserved. |
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2017 |
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2019-11-06T19:05:16Z |
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2019-11-06T19:05:16Z |
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Optica Pura y Aplicada; Vol. 50, Núm. 1; pp. 49-62 |
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0030-3917 |
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https://hdl.handle.net/20.500.12585/8744 |
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10.7149/OPA.50.1.49507 |
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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. 1; pp. 49-62 0030-3917 10.7149/OPA.50.1.49507 Universidad Tecnológica de Bolívar Repositorio UTB |
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eng |
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2019-11-06T19:05:16Z2019-11-06T19:05:16Z2017Optica Pura y Aplicada; Vol. 50, Núm. 1; pp. 49-620030-3917https://hdl.handle.net/20.500.12585/874410.7149/OPA.50.1.49507Universidad Tecnológica de BolívarRepositorio UTBMedical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract quantitative information about many diseases. Modern ophthalmology relies on the advances in digital imaging and computing power. In this paper we present an overview of the results from the doctoral dissertation by Andrés G. Marrugo. This dissertation contributes to the digital analysis of retinal images and the problems that arise along the imaging pipeline of fundus photography, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination compensation, poor image quality, automated focusing, image segmentation, change detection, space-invariant (SI) and space-variant (SV) blind deconvolution (BD). Digital retinal image analysis can be effective and cost-efficient for disease management, computeraided diagnosis, screening and telemedicine and applicable to a variety of disorders such as glaucoma, macular degeneration, and retinopathy. © 2017. Sociedad Española de Óptica. All right reserved.Recurso 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-s2.0-85016604839&doi=10.7149%2fOPA.50.1.49507&partnerID=40&md5=82d7d7f5daa0a83d73d473185646a2a0Scopus 24329839300Scopus 7201466399Retinal image analysis: Image processing and feature extraction oriented to the clinical taskinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Computer-aided diagnosisMedical imageOphthalmologyRetinal imageTelemedicineMarrugo Hernández, Andrés GuillermoMillán, M.S.Abramoff, M.D., Garvin, M., Sonka, M., Retinal Imaging and Image Analysis (2010) Biomedical Engineering, IEEE Reviews, 3, pp. 169-208Marrugo, A.G., (2013) Comprehensive Retinal Image Analysis: Image Processing and Feature Extraction Techniques Oriented to the Clinical Task, , http://hdl.handle.net/10803/134698, Universitat Politècnica de Catalunya, BarcelonaMarrugo, A.G., Millan, M.S., Cristóbal, G., Gabarda, S., Abril, H.C., (2011) No-reference Quality Metrics for Eye Fundus Imaging, pp. 486-493. , CAIP, LNCS, 68542011Gabarda, S., Cristóbal, G., Blind image quality assessment through anisotropy (2007) J Opt Soc Am A Opt Image Sci Vis, 24, pp. B42-B51Zhu, X., Milanfar, P., Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content (2010) IEEE Trans Image Process, 19, pp. 3116-3132Ferzli, R., Karam, L.J., A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB) (2009) IEEE Trans Image Process, 18, pp. 717-728Qu, Y., Pu, Z., Zhao, H., Zhao, Y., Comparison of different quality assessment functions in autoregulative illumination intensity algorithms (2006) Opt. Eng, 45, p. 117201Marrugo, A.G., Millan, M.S., Cristóbal, G., Gabarda, S., Abril, H.C., Anisotropy-based robust focus measure for non-mydriatic retinal imaging (2012) J. Biomed. Opt, 17, p. 076021Moscaritolo, M., Jampel, H., Knezevich, F., Zeimer, R., An Image Based Auto-Focusing Algorithm for Digital Fundus Photography (2009) IEEE Trans. Med. Imaging, 28, pp. 1703-1707Nayar, S., Nakagawa, Y., Shape from focus (1994) IEEE Trans Pattern Anal Mach Intell, 16, pp. 824-831Choi, K., Lee, J., Ko, S., New autofocusing technique using the frequency selective weighted median filter for video cameras (1999) IEEE T. Consum. Electr, 45, pp. 820-827Kristan, M., Pers, J., Perse, M., Kovacic, S., A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform (2006) Pattern Recognit. Lett, 27, pp. 1431-1439Marrugo, A.G., Millan, M.S., Abril, H.C., Implementation of an image based focusing algorithm for non-mydriatic retinal imaging (2014) Engineering Mechatronics and Automation (CIIMA), 2014 III International Congress of, pp. 1-3Marrugo, A.G., (2016) Anisotropy focus measure, , https://github.com/agmarrugo/anisotropy-focus/, [Online Accessed: 17-Apr]Narasimha-Iyer, H., Can, A., Roysam, B., Stewart, C., Tanenbaum, H., Majerovics, A., Singh, H., Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy (2005) IEEE Trans. Biomed. Eng, 53, pp. 1084-1098Marrugo, A.G., Sroubek, F., Sorel, M., Millan, M.S., (2011) Multichannel blind deconvolution in eye fundus imaging, presented at the ISABEL '11-Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, 7, pp. 1-5Everdell, N., Styles, I., Calcagni, A., Gibson, J., Hebden, J., Claridge, E., Multispectral imaging of the ocular fundus using light emitting diode illumination (2010) Rev. Sci. Instrum, 81, pp. 093706-093709Marrugo, A.G., Sorel, M., Sroubek, F., Millan, M.S., Retinal image restoration by means of blind deconvolution (2011) J. Biomed. Opt, 16 (11), p. 116016Stewart, C., Tsai, C.-L., Roysam, B., The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003) IEEE Trans. Med. Imaging, 22, pp. 1379-1394Aach, T., Kaup, A., Bayesian Algorithms for Change Detection in Image Sequences Using Markov Random Fields (1995) Signal Process. Image, 7, pp. 147-160Levin, A., Weiss, Y., Durand, F., Freeman, W., Understanding Blind Deconvolution Algorithms (2011) IEEE Trans Pattern Anal. Mach Intell, 12, pp. 2354-2367Marrugo, A.G., Millan, M.S., Cristóbal, G., Gabarda, S., Sorel, M., Sroubek, F., (2012) Image analysis in modern ophthalmology: From acquisition to computer assisted diagnosis and telemedicine, presented at the Proceedings SPIE, 8436, pp. 84360C-84360CMuramatsu, C., Hayashi, Y., Sawada, A., Hatanaka, Y., Hara, T., Yamamoto, T., Fujita, H., Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma (2010) J. Biomed. Opt, 15, p. 016021Xu, L., Luo, S., Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve (2010) J. Biomed. Opt, 15, p. 065004Bedggood, P., Daaboul, M., Ashman, R., Smith, G., Metha, A., Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging (2008) J. Biomed. Opt, 13, p. 24008Marrugo, A.G., Millan, M.S., Sorel, M., Sroubek, F., Restoration of retinal images with space-variant blur (2014) J. Biomed. Opt, 19 (1), p. 16023. , JanMarrugo, A.G., Millan, M.S., Sorel, M., Kotera, J., Sroubek, F., Improving the blind restoration of retinal images by means of point-spread-function estimation assessment (2015) presented at the Tenth International Symposium on Medical Information Processing and Analysis, 9287, p. 92871DTallón, M., Mateos, J., Babacan, S.D., Molina, R., Katsaggelos, A.K., Space-variant blur deconvolution and denoising in the dual exposure problem (2012) INFORMATION FUSIONMarrugo, A.G., Millan, M.S., Sorel, M., Sroubek, F., Blind restoration of retinal images degraded by space-variant blur with adaptive blur estimation (2013) presented at the 8th Ibero American Optics Meeting/11th Latin American Meeting on Optics, p. 8785D1. , Lasers, and Applications 8785http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_7149OPA_50_1_49507.pdfapplication/pdf23780713https://repositorio.utb.edu.co/bitstream/20.500.12585/8744/1/DOI10_7149OPA_50_1_49507.pdf6defd4c9d5385b24670ba2842d1144deMD51TEXTDOI10_7149OPA_50_1_49507.pdf.txtDOI10_7149OPA_50_1_49507.pdf.txtExtracted texttext/plain57979https://repositorio.utb.edu.co/bitstream/20.500.12585/8744/4/DOI10_7149OPA_50_1_49507.pdf.txtc631025ea2566d39c2352dbae3085978MD54THUMBNAILDOI10_7149OPA_50_1_49507.pdf.jpgDOI10_7149OPA_50_1_49507.pdf.jpgGenerated Thumbnailimage/jpeg92652https://repositorio.utb.edu.co/bitstream/20.500.12585/8744/5/DOI10_7149OPA_50_1_49507.pdf.jpg6f48d37b1cbe4271bdb6ee2efeb073beMD5520.500.12585/8744oai:repositorio.utb.edu.co:20.500.12585/87442023-05-26 16:25:14.499Repositorio Institucional UTBrepositorioutb@utb.edu.co |