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dc.contributor.authorBravo Valero, Antonio José
dc.contributor.authorVera, Miguel Ángel
dc.contributor.authorHuérfano Maldonado, Yoleidy Katherine
dc.contributor.authorManrique Hidalgo, Yeison Fabián
dc.description.abstractThis research focuses on the study of a particular filter based on a similarity criterion that has been applied to improve the information contained in images acquired using different cardiac imaging modalities. The primary attention of this study is to examine which component of the similarity criterion generates more relevant information useful to increase the medical image quality. In this sense, four case studies are established, first a complete formulation of the similarity criterion is considered, and then three additional cases, representing each component of the criterion; such cases are referred to as full, main, residual1, and residual2, respectively. A score function is used for quantifying and then assessing the impact of each component of the similarity criterion. Such measure is a relation between some full–reference and blind–reference image enhancement measures. A computer generated phantom and a representative clinical dataset (1270 three–dimensional images from 126 patients) are used in a thorough evaluation of the similarity criterion. In general terms of performance of the image enhancement technique, the results of the study reveal that the component residual1 outperforms than the other two components of similarity criterion or its complete formulation.eng
dc.publisherUniversidad de Antioquia, Facultad de Ingeneríaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.sourceRevista Facultad de Ingenieríaspa
dc.subjectMedical technologyeng
dc.subjectData processingeng
dc.subjectData analysiseng
dc.titleA comprehensive study of a similarity criterion in cardiac computerized tomography images enhancementeng
dcterms.referencesA. Gómez, G. Díez, and A. E. Salazar, “A markov random field image segmentation model for lizard spots,” Revista Facultad de Ingeniería, Universidad de Antioquia, no. 79, June 16 2016. [Online]. Available:
dcterms.referencesO. Hurtado, H. Rueda, and H. Arguello, “An algorithm for learning sparsifying transforms of multidimensionaleng
dcterms.referencessignals,” Revista Facultad de Ingeniería Universidad de Antioquia, no. 83, June 26 2017. [Online]. Available:
dcterms.referencesN. Terashima, “Computer vision,” in Intelligent Communication Systems, N. Terashima, Ed. San Diego: Academic Press, 2002, pp. 149–179.eng
dcterms.referencesJ. M. Vianney, A. J. Rosales, F. J. Gallegos, and A. Arellano, “Computer-aided diagnosis of brain tumors using image enhancement and fuzzy logic,” Dyna, vol. 81, no. 183, pp. 148–157, mar 2014.eng
dcterms.referencesI. Bankman, Handbook of Medical Imaging: Processing and Analisys, 2nd ed. USA: Academic Press, 2008.eng
dcterms.referencesG. D. Rubin, “Computed tomography: Revolutionizing the practice of medicine for 40 years,” Radiology, vol. 273, no. 2 Suppl, November 2014. [Online]. Available:
dcterms.referencesT. G. Flohr and et al, “Multi–detector row CT systems and image–reconstruction techniques,” Radiology, vol. 235, no. 3, June 1 2005. [Online]. Available:
dcterms.referencesD. T. Ginat and R. Gupta, “Advances in computed tomography imaging technology,” Annual Review of Biomedical Engineering, vol. 16, July 11 2014. [Online]. Available:
dcterms.referencesF. F. Faletra, N. G. Pandian, and S. Y. Ho, Anatomy of the Heart by Multislice Computed Tomography. UK: Wiley-Blackwel, 2008.eng
dcterms.referencesG. Deng, “A generalized unsharp masking algorithm,” IEEE Transaction on Image Processing, vol. 20, no. 5, May 2011. [Online]. Available:
dcterms.referencesT. Chaira, “An improved medical image enhancement scheme using Type II fuzzy set,” Applied Soft Computing, vol. 25, December 2014. [Online]. Available:
dcterms.referencesZ. Al-Ameen and G. Sulong, “A new algorithm for improving the low contrast of computed tomography images using tuned brightness controlled single-scale Retinex,” Scanning, vol. 37, no. 2, March 2015. [Online]. Available:
dcterms.referencesE. Daniel and J. Anitha, “Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm,” Computers in Biology and Medicine, vol. 71, April 1 2016. [Online]. Available:
dcterms.referencesP. Zhuang, X. Fu, Y. Huang, and X. Ding, “Image enhancement using divide-and-conquer strategy,” Journal of Visual Communication and Image Representation, vol. 45, May 2017. [Online]. Available:
dcterms.referencesL. Rundo and et al, “MedGA: A novel evolutionary method for image enhancement in medical imaging systems,” Expert Systems with Applications, vol. 119, April 1 2019. [Online]. Available:
dcterms.referencesR. M. Haralick and L. G. Shapiro, Computer and Robot Vision. Boston, USA: Addison-Wesley, 1992.eng
dcterms.referencesA. Bravo and R. Medina, “An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms,” Computerized Medical Imaging and Graphics, vol. 32, no. 5, July 2008. [Online]. Available:
dcterms.referencesJ. Clemente, A. Bravo, and R. Medina, “Using morphological and clustering analysis for left ventricle detection in MSCT cardiac images,” in Proceedings of IEEE International Symposium on Signal Processing and Information Technology, Sarajevo, 2008, pp. 264–269.eng
dcterms.referencesA. Bravo, J. Clemente, M. Vera, J. Avila, and R. Medina, “A hybrid boundary–region left ventricle segmentation in computed tomography,” in Proceedings of International Conference on Computer Vision Theory and Applications, Angers, France, 2010, pp. 107–114.eng
dcterms.referencesA. Bravo, M. Vera, M. Garreau, and R. Medina, “Three–dimensional segmentation of ventricular heart chambers from multi–slice computerized tomography: An hybrid approach,” in Proceedings of Digital Information and Communication Technology and Its Applications-DICTAP 2011, France, 2011, pp. 287–301.eng
dcterms.referencesM. Vera, A. Bravo, M. Garreau, and R. Medina, “Similarity enhancement for automatic segmentation of cardiac structures in computed tomography volumes,” in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 2011, pp. 8094–8097.eng
dcterms.referencesM. Vera, A. Bravo, and R. Medina, “Improving ventricle detection in 3–D cardiac multislice computerized tomography images,” in International Conference on Computer Vision, Imaging and Computer Graphics-VISIGRAPP 2010, France, 2011, pp. 170–183.eng
dcterms.referencesG. C. and et al, “A score function as quality measure for cardiac image enhancement techniques assessment,” Revista Latinoamericana de Hipertensión, vol. 14, no. 2, pp. 180–186, 2019.eng
dcterms.referencesM. Vera, “Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte,” Ph. D. dissertation, Universidad de Los Andes, Mérida, Venezuela,
dcterms.referencesL. Devroye, Non-Uniform Random Variate Generation. USA: Springer Verlag, 1986.eng
dcterms.referencesA. Primak, C. McCollough, M. Bruesewitz, J. Zhang, and J. Fletcher, “Relationship between noise, dose, and pitch in cardiac multi–detector row CT,” Radiographics, vol. 26, no. 6, November 2006. [Online]. Available:
dcterms.referencesL. J. Kroft, A. de Roos, and J. Geleijns, “Artifacts in ECG–synchronized MDCT coronary angiography,” American Journal of Roentgenology, vol. 189, no. 3, September 2007. [Online]. Available:
dcterms.referencesR. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. New Jersey, USA: Prentice Hall, 2006.eng
dcterms.referencesW. Schroeder, K. M. Martin, and W. E. Lorensen, The Visualization Toolkit: An Object-oriented Approach to 3D Graphics, 2nd ed. USA: Kitware, 2006.eng
dcterms.referencesWorld Health Organization. (2011) Global status report on noncommunicable diseases 2010. [World Health Organization]. [Online]. Available:
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