Radon Transformation Applied to the Segmentation of Grayscale Digital Images

In this scientific research article, the community interested in digital image processing is introduced to the new application of Radon’s transformation to segment images in grayscale, which allows the identification and classification of regions or objects, which can be extended to color images. Re...

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Autores:
De Armas Costa, Ricarod Joaquín
Quintero Torres, Shirley Viviana
Acosta Muñoz, Cristina
Rey Torres, Carlos Camilo Guillermo
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/5498
Acceso en línea:
http://hdl.handle.net/11407/5498
https://doi.org/10.22395/rium.v17n32a10
Palabra clave:
Radon transformation
Segmentation
Region of interest
Binarized images
Transformada de Radon
Segmentação
Região de interesse
Imagens binarizadas
Transformada de Radon
Segmentación
Región de interés
Imágenes binarizadas
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
id REPOUDEM2_f806d253ae1a941ee0db03812526fbac
oai_identifier_str oai:repository.udem.edu.co:11407/5498
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.eng.fl_str_mv Radon Transformation Applied to the Segmentation of Grayscale Digital Images
dc.title.por.fl_str_mv A transformada de Radon aplicada à segmentação de imagens digitais em escala de cinzas
dc.title.spa.fl_str_mv La transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grises
title Radon Transformation Applied to the Segmentation of Grayscale Digital Images
spellingShingle Radon Transformation Applied to the Segmentation of Grayscale Digital Images
Radon transformation
Segmentation
Region of interest
Binarized images
Transformada de Radon
Segmentação
Região de interesse
Imagens binarizadas
Transformada de Radon
Segmentación
Región de interés
Imágenes binarizadas
title_short Radon Transformation Applied to the Segmentation of Grayscale Digital Images
title_full Radon Transformation Applied to the Segmentation of Grayscale Digital Images
title_fullStr Radon Transformation Applied to the Segmentation of Grayscale Digital Images
title_full_unstemmed Radon Transformation Applied to the Segmentation of Grayscale Digital Images
title_sort Radon Transformation Applied to the Segmentation of Grayscale Digital Images
dc.creator.fl_str_mv De Armas Costa, Ricarod Joaquín
Quintero Torres, Shirley Viviana
Acosta Muñoz, Cristina
Rey Torres, Carlos Camilo Guillermo
dc.contributor.author.none.fl_str_mv De Armas Costa, Ricarod Joaquín
Quintero Torres, Shirley Viviana
Acosta Muñoz, Cristina
Rey Torres, Carlos Camilo Guillermo
dc.subject.eng.fl_str_mv Radon transformation
Segmentation
Region of interest
Binarized images
topic Radon transformation
Segmentation
Region of interest
Binarized images
Transformada de Radon
Segmentação
Região de interesse
Imagens binarizadas
Transformada de Radon
Segmentación
Región de interés
Imágenes binarizadas
dc.subject.por.fl_str_mv Transformada de Radon
Segmentação
Região de interesse
Imagens binarizadas
dc.subject.spa.fl_str_mv Transformada de Radon
Segmentación
Región de interés
Imágenes binarizadas
description In this scientific research article, the community interested in digital image processing is introduced to the new application of Radon’s transformation to segment images in grayscale, which allows the identification and classification of regions or objects, which can be extended to color images. Results obtained were compared with the results of two classic segmentation algorithms: the optimized Otsu thresholding algorithm, and the Seeded Region Growing growth algorithm.
publishDate 2018
dc.date.created.none.fl_str_mv 2018-07-04
dc.date.accessioned.none.fl_str_mv 2019-11-07T15:00:31Z
dc.date.available.none.fl_str_mv 2019-11-07T15:00:31Z
dc.type.eng.fl_str_mv Article
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dc.type.local.spa.fl_str_mv Artículo científico
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dc.identifier.issn.none.fl_str_mv 1692-3324
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dc.identifier.doi.none.fl_str_mv https://doi.org/10.22395/rium.v17n32a10
dc.identifier.eissn.none.fl_str_mv 2248-4094
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
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reponame:Repositorio Institucional Universidad de Medellín
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url http://hdl.handle.net/11407/5498
https://doi.org/10.22395/rium.v17n32a10
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dc.relation.citationvolume.none.fl_str_mv 17
dc.relation.citationissue.none.fl_str_mv 32
dc.relation.citationstartpage.none.fl_str_mv 213
dc.relation.citationendpage.none.fl_str_mv 227
dc.relation.references.spa.fl_str_mv [1] V. Bogachev y M. N. Lukintsova. “The Radon transform in infinite-dimensional spaces”. Doklady Mathematics. Vol. 85. N.° 2. MAIK Nauka/Interperiodica, 2012.
[2] J. Radon, “On the Determination of Functions from Their Integral Values along Certain Manifolds”, IEEE Transactions on Medical Imaging, 5:170–176, 1986.
[3] J. Radon, “Über die Bestimmung von Funktionen durch ihre Ihre Integralwerte längs gewisser Mannigfaltigkeiten”, Berichte Sächsische Akademie der Wissen-schaften, Leipzig, Math-Phys., 69:262-277, 1917.
[4] T. Buzug, “Computed Tomography.From Photon Statistics to Modern Cone Be- am CT”. Leipzig, Germany: Springer, 2008.
[5] A. Kak y M. Sallaney, “Principles of Computarized Tomography”, IEEE Press, New York, 1988.
[6] E. Grinberg, “On images of Radon transforms”, Duke Mathematical Journal, 52:939-972, 1985.
[7] S. Deans, “The Radon Transform and some of its applications”, New York: John Wiley and Sons Inc, 1983.
[8] P. Tyagi, y U. Bhosle, “Radiometric correction of Multispectral Images using Radon transform”. Journal of the Indian Society of Remote Sensing 42.1, 2014.
[9] M. Miguel, et al., “Radon transform algorithm for fingerprint core point detection”. Mexican Conference on Pattern Recognition. Springer Berlin Heidelberg, 2010.
[10] P. Sharma et al., “An Innovative ANN Based Assamese Character Recognition System Configured with Radon Transform.” Wireless Networks and Computational Intelligence. Springer Berlin Heidelberg, 287-292, 2012.
[11] G. Pavlidis, “Mixed Raster Content. Segmentation, Compression, Transmission”, Singapore: Springer, 2017.
[12] R. González y R. Woods, “Digital Image Processing”. New Jersey: Prentice-Hall, 2002.
[13] R. Bracewell, “Two-Dimensional Imaging”, Englewood Cliffs, NJ, Prentice-Hall, 1995.
[14] J. Lim, “Two-Dimensional Signal and Image Processing”, Englewood Cliffs, NJ, Prentice Hall, 1990.
[15] M. Ekstrom, “Digital image processing techniques”, Vol. 2, Academic Press, 2012.
[16] R. Yogamangalam and B. Karthikeyan. “Segmentation techniques comparison in image processing.” International Journal of Engineering and Technology (IJET) 5.1, 307-313, 2013.
[17] Oak, Rajvardhan. “A study of digital image segmentation techniques.” Int. J. Eng. Comput. Sci 5.12, 19779-19783, 2016.
[18] Kaganami, Hassana Grema, and Zou Beiji. “Region-based segmentation versus edge detection.” Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP’09. Fifth International Conference on. IEEE, 2009.
[19] F. Natterer, “The Mathematics of Computarized Tomography”, Siam, Society for Industrial and Applied Mathematics, Philadelphia, EUA, 2001.
[20] S. Helgason, “The Radon Transform”, Birkhäuser. Second Edition. Boston, Mass. EUA, p. 2, 1999.
[21] N. Otsu, “A threshold method from gray-level histogram”, IEEE Transactions on System Man Cybernetics, Vol. SMC-9. No.1, 1979, pp.62-66. Optimizado en la Universidad Nacional de Quilmes. Ingeniería en Automatización y Control Industrial. Cátedra Visión Artificial, 2005.
[22] R. Adams y L. Bischof, “Seeded Region Growing”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, 1994.
[23] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, n.° 7, pp. 629–639, 1990
dc.relation.ispartofjournal.spa.fl_str_mv Revista Ingenierías Universidad de Medellín
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dc.rights.creativecommons.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Attribution-NonCommercial-ShareAlike 4.0 International
http://purl.org/coar/access_right/c_abf2
dc.format.extent.spa.fl_str_mv p. 213-227
dc.format.medium.spa.fl_str_mv Electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Lat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.publisher.spa.fl_str_mv Universidad de Medellín
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
dc.publisher.place.spa.fl_str_mv Medellín
dc.source.spa.fl_str_mv Revista Ingenierías Universidad de Medellín; Vol. 17 Núm. 32 (2018): Enero-Junio; 213-227
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling De Armas Costa, Ricarod JoaquínQuintero Torres, Shirley VivianaAcosta Muñoz, CristinaRey Torres, Carlos Camilo GuillermoDe Armas Costa, Ricarod Joaquín; Universidad CentralQuintero Torres, Shirley Viviana; Universidad CentralAcosta Muñoz, Cristina; Universidad CentralRey Torres, Carlos Camilo Guillermo; Universidad Central2019-11-07T15:00:31Z2019-11-07T15:00:31Z2018-07-041692-3324http://hdl.handle.net/11407/5498https://doi.org/10.22395/rium.v17n32a102248-4094reponame:Repositorio Institucional Universidad de Medellínrepourl:https://repository.udem.edu.co/instname:Universidad de MedellínIn this scientific research article, the community interested in digital image processing is introduced to the new application of Radon’s transformation to segment images in grayscale, which allows the identification and classification of regions or objects, which can be extended to color images. Results obtained were compared with the results of two classic segmentation algorithms: the optimized Otsu thresholding algorithm, and the Seeded Region Growing growth algorithm.Este artigo de pesquisa científica está dirigido à comunidade interessa no processamento digital de imagens, uma aplicação inédita da transformada de Radon para segmentar imagens em escala de cinzas, o que permite a identificação e classificação de regiões ou objetos, a qual se pode estender a imagens em cor. Os resultados obtidos foram comparados com os resultados de dois algoritmos clássicos de segmentação: o algoritmo de umbralização Otsu otimizado e o algoritmo de crescimento de regiões Seeded Region Growing.En este artículo de investigación científica se da a conocer a la comunidad interesada en el procesamiento digital de imágenes, una aplicación inédita de la transformada de Radon para segmentar imágenes en escala de grises, lo que permite la identificación y clasificación de regiones u objetos, misma que puede extenderse a imágenes en color. Los resultados obtenidos se compararon con los resultados de dos algoritmos clásicos de segmentación: el algoritmo de umbralización Otsu optimizado, y el algoritmo de crecimiento de regiones Seeded Region Growing.p. 213-227Electrónicoapplication/pdfspaUniversidad de MedellínFacultad de IngenieríasMedellínhttps://revistas.udem.edu.co/index.php/ingenierias/article/view/17941732213227[1] V. Bogachev y M. N. Lukintsova. “The Radon transform in infinite-dimensional spaces”. Doklady Mathematics. Vol. 85. N.° 2. MAIK Nauka/Interperiodica, 2012.[2] J. Radon, “On the Determination of Functions from Their Integral Values along Certain Manifolds”, IEEE Transactions on Medical Imaging, 5:170–176, 1986.[3] J. Radon, “Über die Bestimmung von Funktionen durch ihre Ihre Integralwerte längs gewisser Mannigfaltigkeiten”, Berichte Sächsische Akademie der Wissen-schaften, Leipzig, Math-Phys., 69:262-277, 1917.[4] T. Buzug, “Computed Tomography.From Photon Statistics to Modern Cone Be- am CT”. Leipzig, Germany: Springer, 2008.[5] A. Kak y M. Sallaney, “Principles of Computarized Tomography”, IEEE Press, New York, 1988.[6] E. Grinberg, “On images of Radon transforms”, Duke Mathematical Journal, 52:939-972, 1985.[7] S. Deans, “The Radon Transform and some of its applications”, New York: John Wiley and Sons Inc, 1983.[8] P. Tyagi, y U. Bhosle, “Radiometric correction of Multispectral Images using Radon transform”. Journal of the Indian Society of Remote Sensing 42.1, 2014.[9] M. Miguel, et al., “Radon transform algorithm for fingerprint core point detection”. Mexican Conference on Pattern Recognition. Springer Berlin Heidelberg, 2010.[10] P. Sharma et al., “An Innovative ANN Based Assamese Character Recognition System Configured with Radon Transform.” Wireless Networks and Computational Intelligence. Springer Berlin Heidelberg, 287-292, 2012.[11] G. Pavlidis, “Mixed Raster Content. Segmentation, Compression, Transmission”, Singapore: Springer, 2017.[12] R. González y R. Woods, “Digital Image Processing”. New Jersey: Prentice-Hall, 2002.[13] R. Bracewell, “Two-Dimensional Imaging”, Englewood Cliffs, NJ, Prentice-Hall, 1995.[14] J. Lim, “Two-Dimensional Signal and Image Processing”, Englewood Cliffs, NJ, Prentice Hall, 1990.[15] M. Ekstrom, “Digital image processing techniques”, Vol. 2, Academic Press, 2012.[16] R. Yogamangalam and B. Karthikeyan. “Segmentation techniques comparison in image processing.” International Journal of Engineering and Technology (IJET) 5.1, 307-313, 2013.[17] Oak, Rajvardhan. “A study of digital image segmentation techniques.” Int. J. Eng. Comput. Sci 5.12, 19779-19783, 2016.[18] Kaganami, Hassana Grema, and Zou Beiji. “Region-based segmentation versus edge detection.” Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP’09. Fifth International Conference on. IEEE, 2009.[19] F. Natterer, “The Mathematics of Computarized Tomography”, Siam, Society for Industrial and Applied Mathematics, Philadelphia, EUA, 2001.[20] S. Helgason, “The Radon Transform”, Birkhäuser. Second Edition. Boston, Mass. EUA, p. 2, 1999.[21] N. Otsu, “A threshold method from gray-level histogram”, IEEE Transactions on System Man Cybernetics, Vol. SMC-9. No.1, 1979, pp.62-66. Optimizado en la Universidad Nacional de Quilmes. Ingeniería en Automatización y Control Industrial. Cátedra Visión Artificial, 2005.[22] R. Adams y L. Bischof, “Seeded Region Growing”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, 1994.[23] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, n.° 7, pp. 629–639, 1990Revista Ingenierías Universidad de Medellínhttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Revista Ingenierías Universidad de Medellín; Vol. 17 Núm. 32 (2018): Enero-Junio; 213-227Radon transformationSegmentationRegion of interestBinarized imagesTransformada de RadonSegmentaçãoRegião de interesseImagens binarizadasTransformada de RadonSegmentaciónRegión de interésImágenes binarizadasRadon Transformation Applied to the Segmentation of Grayscale Digital ImagesA transformada de Radon aplicada à segmentação de imagens digitais em escala de cinzasLa transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grisesArticlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artículo científicoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comunidad Universidad de MedellínLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees11407/5498oai:repository.udem.edu.co:11407/54982021-05-14 14:29:35.648Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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