Radon Transformation Applied to the Segmentation of Grayscale Digital Images
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Submitted:
Jul 3, 2016
Published:
Jul 4, 2018
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Abstract
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.
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De Armas Costa, R. J., Quintero Torres, S. V., Acosta Muñoz, C., & Rey Torres, C. C. G. (2018). Radon Transformation Applied to the Segmentation of Grayscale Digital Images. Revista Ingenierías Universidad De Medellín, 17(32), 213–227. https://doi.org/10.22395/rium.v17n32a10
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References
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[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.