Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology
In this work an adaptation of the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, in the context of digital medical image processing is proposed. Specifically, synthetic images reported in the literature are used as numerical phantoms. Construction of the synthetic images was...
- Autores:
-
Contreras, Yudith
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Barrera, Doris
Hernández, Carlos
Molina, Ángel Valentín
Martínez, Luis Javier
Sáenz, Frank
Vivas, Marisela
Salazar, Juan
Gelvez, Elkin
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2527
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2527
- Palabra clave:
- CRISP-DM Methodology
Synthetic cardiac images
Computerized tomography
Noise
Artifacts
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| dc.title.alternative.spa.fl_str_mv |
Procesamiento digital de imágenes médicas: aplicación a bases de datos sintéticas cardiacas usando la metodología CRISP-DM |
| title |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| spellingShingle |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology CRISP-DM Methodology Synthetic cardiac images Computerized tomography Noise Artifacts |
| title_short |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| title_full |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| title_fullStr |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| title_full_unstemmed |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| title_sort |
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology |
| dc.creator.fl_str_mv |
Contreras, Yudith Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Salazar, Williams Vera, María Isabel Borrero, Maryury Barrera, Doris Hernández, Carlos Molina, Ángel Valentín Martínez, Luis Javier Sáenz, Frank Vivas, Marisela Salazar, Juan Gelvez, Elkin |
| dc.contributor.author.none.fl_str_mv |
Contreras, Yudith Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Salazar, Williams Vera, María Isabel Borrero, Maryury Barrera, Doris Hernández, Carlos Molina, Ángel Valentín Martínez, Luis Javier Sáenz, Frank Vivas, Marisela Salazar, Juan Gelvez, Elkin |
| dc.subject.eng.fl_str_mv |
CRISP-DM Methodology Synthetic cardiac images Computerized tomography Noise Artifacts |
| topic |
CRISP-DM Methodology Synthetic cardiac images Computerized tomography Noise Artifacts |
| description |
In this work an adaptation of the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, in the context of digital medical image processing is proposed. Specifically, synthetic images reported in the literature are used as numerical phantoms. Construction of the synthetic images was inspired by a detailed analysis of some of the imperfections found in the real multilayer cardiac computed tomography images. Of all the imperfections considered, only Poisson noise was selected and incorporated into a synthetic database. An example is presented in which images contaminated with Poisson noise are processed and then subject to two classical digital smoothing techniques, identified as Gaussian filter and anisotropic diffusion filter. Additionally, the peak of the signal-to-noise ratio (PSNR) is considered as a metric to analyze the performance of these filters. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-01-25T16:23:23Z |
| dc.date.available.none.fl_str_mv |
2019-01-25T16:23:23Z |
| dc.type.eng.fl_str_mv |
article |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.identifier.issn.none.fl_str_mv |
18564550 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/2527 |
| identifier_str_mv |
18564550 |
| url |
http://hdl.handle.net/20.500.12442/2527 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
| rights_invalid_str_mv |
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| dc.publisher.spa.fl_str_mv |
Sociedad Latinoamericana de Hipertensión |
| dc.source.spa.fl_str_mv |
Revista Latinoamericana de Hipertensión Vol. 13, No. 4 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.eng.fl_str_mv |
http://www.revhipertension.com/rlh_4_2018/1_digital_processing_of_medical.pdf |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Contreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a64Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Huérfano, Yoleidy769899ba-e6a1-4144-95c2-ff4614f93578Valbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Salazar, Williamsfd007214-08c4-4cd6-ae19-7f2ba4f184eaVera, María Isabelc522f56e-ec03-4aa6-9e83-d339a37388acBorrero, Maryuryce8424b3-6f43-4a46-8f73-214fafbb62fdBarrera, Doris4b365c16-7d6f-4aee-985c-e70d635e8807Hernández, Carlosa82d5fb1-0724-456f-8223-93882ad7278dMolina, Ángel Valentín5fcd607f-8710-40a9-b4dc-b9d1f71d1c1eMartínez, Luis Javierd0fa0a36-7752-496a-979e-48fdb02a5ee9Sáenz, Frank5a93b50c-3ebe-476e-8aa6-4286185e2b1dVivas, Marisela8a48d7f3-9b15-4821-892c-91f9749f1286Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Gelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94c2019-01-25T16:23:23Z2019-01-25T16:23:23Z201818564550http://hdl.handle.net/20.500.12442/2527In this work an adaptation of the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, in the context of digital medical image processing is proposed. Specifically, synthetic images reported in the literature are used as numerical phantoms. Construction of the synthetic images was inspired by a detailed analysis of some of the imperfections found in the real multilayer cardiac computed tomography images. Of all the imperfections considered, only Poisson noise was selected and incorporated into a synthetic database. An example is presented in which images contaminated with Poisson noise are processed and then subject to two classical digital smoothing techniques, identified as Gaussian filter and anisotropic diffusion filter. Additionally, the peak of the signal-to-noise ratio (PSNR) is considered as a metric to analyze the performance of these filters.engSociedad Latinoamericana de HipertensiónRevista Latinoamericana de HipertensiónVol. 13, No. 4 (2018)http://www.revhipertension.com/rlh_4_2018/1_digital_processing_of_medical.pdfCRISP-DM MethodologySynthetic cardiac imagesComputerized tomographyNoiseArtifactsDigital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodologyProcesamiento digital de imágenes médicas: aplicación a bases de datos sintéticas cardiacas usando la metodología CRISP-DMarticlehttp://purl.org/coar/resource_type/c_6501Moine J. Methodologies for the discovery of knowledge in databases: a comparative study. [Master´s thesis]. Mar de Plata-Argentina: University of la Plata, 2013.Dnuggets K (2007). Poll: ¿What main methodology are you using for data mining? Recovered in 7 de noviembre de 2010, de http://www. kdnuggets.com/polls/2007/data_mining_methodology.htm.Vera M. Segmentation of cardiac structures in multi-slice computed tomography images. [Doctoral thesis]. Merida-Venezuela: Los Andes University, 2014.Shapiro L, Stockman G. Computer Vision. 1 edition. Upper Saddle River, NJ: Pearson; 2001.Devroye L. Non–Uniform Random Variate Generation. New York, USA: Springer–Verlag, 1986.Pratt W. Digital Image Processing. USA: John Wiley & Sons Inc, 2007.González R., Woods R. Digital Image Processing. USA: Prentice Hall, 2001.Perona P., Malik J. Scalespace and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990:12 (7), pp. 629–639.Coupé P., Yger P., Prima S., Hellier P., Kervrann C., Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transactions on Medical Imaging, 2008: 27 (4), pp. 425–441.Meijering H. Image enhancement in digital X–ray angiography. [Tesis Doctoral], Utrecht University, Netherlands, 2000.Lei T., Sewchand W. Statistical approach to x–ray CT imaging and its applications in image analysis. statistical analysis of x–ray CT imaging. IEEE Transactions on Medical Imaging, 1992: 11 (1), pp. 53–61.Lu H., Li X., Hsiao I., Liang Z. Analytical noise treatment for low-dose ct projection data by penalized weighted least-square smoothing in the kl domain. Proceedings of SPIE Medical Imaging, 2002: 4682, pp. 146–152.Schroeder W., Martin K., Lorensen B. The Visualization Toolkit, An Object-Oriented Approach to 3D Graphics. New York: Prentice Hall, 2001.Fast Light Toolkit (FLTK). Web page available on line: http://fltk.org/ last access: Oct, 2017.B. Stroustrup, The C++ Programming Language. 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