Study of Medical Image Processing techniques applied to Lung Cancer
Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and...
- Autores:
-
Moreno, Silvia
Bonfante, Mario
Zurek, Eduardo
San Juan, Homero
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/3702
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/3702
- Palabra clave:
- Lung Cancer
Medical Image Processing
Radiomics
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Study of Medical Image Processing techniques applied to Lung Cancer |
| title |
Study of Medical Image Processing techniques applied to Lung Cancer |
| spellingShingle |
Study of Medical Image Processing techniques applied to Lung Cancer Lung Cancer Medical Image Processing Radiomics |
| title_short |
Study of Medical Image Processing techniques applied to Lung Cancer |
| title_full |
Study of Medical Image Processing techniques applied to Lung Cancer |
| title_fullStr |
Study of Medical Image Processing techniques applied to Lung Cancer |
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Study of Medical Image Processing techniques applied to Lung Cancer |
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Study of Medical Image Processing techniques applied to Lung Cancer |
| dc.creator.fl_str_mv |
Moreno, Silvia Bonfante, Mario Zurek, Eduardo San Juan, Homero |
| dc.contributor.author.none.fl_str_mv |
Moreno, Silvia Bonfante, Mario Zurek, Eduardo San Juan, Homero |
| dc.subject.eng.fl_str_mv |
Lung Cancer Medical Image Processing Radiomics |
| topic |
Lung Cancer Medical Image Processing Radiomics |
| description |
Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and may be useful in the detection and treatment of cancer. This article presents the state of the art of image processing techniques applied in the study of lung cancer, emphasizing in two main tasks: segmentation of nodules or tumors, and extraction of useful features for classification and prognosis of tumor evolution using Radiomics. |
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2019 |
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2019-08-13T13:59:14Z |
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2019-08-13T13:59:14Z |
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2019-07 |
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article |
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http://purl.org/coar/resource_type/c_6501 |
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9789899843493 |
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21660727 |
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https://hdl.handle.net/20.500.12442/3702 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
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IEEE |
| dc.source.eng.fl_str_mv |
IEEE Xplore Digital Library 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) |
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Universidad Simón Bolívar |
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https://ieeexplore.ieee.org/document/8760888 |
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Moreno, Silviafdbaaeb0-3b4f-4fb1-aa77-ebcfe2658250Bonfante, Mario5b91ad34-74dc-42ed-be44-06beeaf401dcZurek, Eduardof76fa751-7944-4c11-819b-3dae164a6215San Juan, Homero24d53aba-87c5-47ac-9a37-6a67c6f3177b2019-08-13T13:59:14Z2019-08-13T13:59:14Z2019-07978989984349321660727https://hdl.handle.net/20.500.12442/3702Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and may be useful in the detection and treatment of cancer. This article presents the state of the art of image processing techniques applied in the study of lung cancer, emphasizing in two main tasks: segmentation of nodules or tumors, and extraction of useful features for classification and prognosis of tumor evolution using Radiomics.engIEEEAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2IEEE Xplore Digital Library2019 14th Iberian Conference on Information Systems and Technologies (CISTI)https://ieeexplore.ieee.org/document/8760888Lung CancerMedical Image ProcessingRadiomicsStudy of Medical Image Processing techniques applied to Lung Cancerarticlehttp://purl.org/coar/resource_type/c_6501Cancer Research UK, “Worldwide cancer statistics.” [Online]. Available: http://www.cancerresearchuk.org/health-professional/cancerstatistics/ worldwide-cancer. [Accessed: 17-Nov-2017].P. Lambin, E. Rios-Velazquez, R. Leijenaar, S. Carvalho, R. G. P. M. Van Stiphout, P. Granton, C. M. L. Zegers, R. Gillies, R. Boellard, A. Dekker, and H. J. W. L. Aerts, “Radiomics: Extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer, vol. 48, no. 4, pp. 441–446, 2012.G. Niranjana, “A Review on Image Processing Methods in Detecting Lung Cancer Using CT Images - IEEE Conference Publication,” 2017.G. Lee, H. Y. Lee, H. Park, M. L. Schiebler, E. J. R. van Beek, Y. Ohno, J. B. Seo, and A. Leung, “Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art,” Eur. J. Radiol., vol. 86, pp. 297–307, 2017.A. Kulkarni and A. Panditrao, “Classification of lung cancer stages on CT scan images using image processing,” 2014 IEEE Int. Conf. Adv. Commun. Control Comput. Technol., no. 978, pp. 1384–1388, 2014.Y. Gu, V. Kumar, L. O. Hall, D. B. Goldgof, C. Y. Li, R. Korn, C. Bendtsen, E. R. Velazquez, A. Dekker, H. Aerts, P. Lambin, X. Li, J. Tian, R. A. 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