Xray medical image characterization with sparse radiation based on Wavelets
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Abstract
In medical radiology there are large amounts of digital images in hospitals and health centers. Equipment that enables the acquisition of medical radiographs uses X-radiation sensor plates for image acquisition in medical diagnosis. Medical radiology equipment uses anti-scatter grids, which are physical devices, to avoid unwanted effects on imaging. In the present work, we analyse from a qualitative point of view the radiation scattering effect that is caused in images without the presence of the anti-scattering grid. In this research, the acquisition of radiological images was made by means of X-ray equipment with an anti-scattering grid, capturing images without scattering and others that only present radiation scattering as a point of comparison. The methodology uses the Wavelet transformation to image characterization in segment process that define the regions that affect the different types of dispersion presented in X radiation. The tool used for the analysis of the images is the multi-resolution Wavelet transform, specifically the Discrete Wavelet Transform (DWT). The methodology was applied to different 2D radiological images in shades of gray. In the images used, it showed a robustness in the differentiation of X radiation incidence zones. This work is the beginning of a distortion analysis for the reconstruction of this type of images.
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References
C. Gonzalez, R. Woods, Digital image processing. Pearson Prentice Hall, 2014.
C. Chui, Wavelet analysis and its applications, USA. Academic Press, 1992.
S. Anwar, F. Arshad, M. Majid, “Fast wavelet based image characterization for content based medical image retrieval,†International Conference on Communication, Computing and Digital Systems (C-CODE), 2017.
S. Mallat, “A wavelet tour of signal processing,†USA. Academic Press is an imprint of Elsevier, 2009.
J. Acuna-Garcia, S Canchola-Magdaleno, F Jacques, “Comparative study: 2D image processing in gray scale using wavelets,†International Journal of Computer Science and Sof tware Engineering (IJCSSE), Volume 8, Issue 7, July 2019, pp: 151-160.
F. Manriquez and I. Terol, “Caracterización de HIPS mediante técnicas de análisis de imágenes,†Revista mexicana de fÃsica 50 suplemento 1, 2003.
G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, “Fast wavelet-based image characterization for highly adaptive image retrieval,†IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1613–1623, 2012.
C. Carlsson and G. Carlsson, “Basic physics of X-ray imaging,†ser. Report / Linkopings hogskola, Institutionen for radiologi. Department of Medicine and Care Radio Physics, 2014.
G. Dougherty, “Medical image processing,†ser. Report / Linkopings hogskola, Institutionen for radiologi. Springer New York Dordrecht Heidelberg London, 2011.
G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, “Wavelet optimization for content-based image retrieval in medical databases,†Medical image analysis, vol. 14, no. 2, pp. 227–241, 2010.
G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, “Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval,†IEEE transactions on image processing, vol. 19, no. 1, pp. 25–35, 2010.
G. Quellec, M. Lamard, G. Cazuguel, M. Cozic, G. Coatrieux, “Multipleinstance learning for anomaly detection in digital mammography,†IEEE Transactions on Image Processing, vol. 35, no. 7, pp. 1604–1614, 2016.