La transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grises
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Derechos de autor 2023 Revista Ingenierías Universidad de Medellín
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
- Articles
- Enviado: julio 3, 2016
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Publicado: julio 4, 2018
Resumen
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.
Referencias
[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.
[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.
Cómo citar
De Armas Costa, R. J., Quintero Torres, S. V., Acosta Muñoz, C., & Rey Torres, C. C. G. (2018). La transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grises. Revista Ingenierías Universidad De Medellín, 17(32), 213-227. https://doi.org/10.22395/rium.v17n32a10
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