Semi-automatic detection of hepatic tumor in computed tomography images
In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of th...
- Autores:
-
Sáenz, F
Vera, M
López, J
Huérfano, Y
Valbuena, O
Vera, M I
Gelvez-Almeida, E
Salazar-Torres, J
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/5098
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/5098
- Palabra clave:
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Semi-automatic detection of hepatic tumor in computed tomography images |
| title |
Semi-automatic detection of hepatic tumor in computed tomography images |
| spellingShingle |
Semi-automatic detection of hepatic tumor in computed tomography images |
| title_short |
Semi-automatic detection of hepatic tumor in computed tomography images |
| title_full |
Semi-automatic detection of hepatic tumor in computed tomography images |
| title_fullStr |
Semi-automatic detection of hepatic tumor in computed tomography images |
| title_full_unstemmed |
Semi-automatic detection of hepatic tumor in computed tomography images |
| title_sort |
Semi-automatic detection of hepatic tumor in computed tomography images |
| dc.creator.fl_str_mv |
Sáenz, F Vera, M López, J Huérfano, Y Valbuena, O Vera, M I Gelvez-Almeida, E Salazar-Torres, J |
| dc.contributor.author.none.fl_str_mv |
Sáenz, F Vera, M López, J Huérfano, Y Valbuena, O Vera, M I Gelvez-Almeida, E Salazar-Torres, J |
| description |
In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of the objects present in those images and, on the other hand, generate the liver tumor morphology. For this, the sequence of techniques composed of gaussian smoothing, gradient magnitude, median filter, region growing and binary morphological dilation are used. The value obtained, for the metric called Dice score, show a good correlation between manual segmentation, performed by a hepatologist, and the tumor segmentation obtained using the proposed technique. This type of segmentation is the extreme utility for the characterization of hepatic tumors and the planning of the clinical behavior to be followed in the treatment of this human liver disease. |
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2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
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2020-04-14T03:19:32Z |
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2020-04-14T03:19:32Z |
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article |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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article |
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17426596 |
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https://hdl.handle.net/20.500.12442/5098 |
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17426596 |
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https://hdl.handle.net/20.500.12442/5098 |
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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://purl.org/coar/access_right/c_abf2 |
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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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pdf |
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IOP Publishing |
| dc.source.eng.fl_str_mv |
Journal of Physics: Conference Series 1408 (2019) |
| institution |
Universidad Simón Bolívar |
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https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012001 |
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Sáenz, Fe7336b90-cde6-4d03-880d-55f6a198725dVera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eLópez, J981f81ee-06f3-4ed3-bcf7-41a2d4f97e46Huérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fValbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9Vera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eGelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deSalazar-Torres, J40a2a6c9-3e39-4994-9b5a-1c6112bd80002020-04-14T03:19:32Z2020-04-14T03:19:32Z201917426596https://hdl.handle.net/20.500.12442/5098In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of the objects present in those images and, on the other hand, generate the liver tumor morphology. For this, the sequence of techniques composed of gaussian smoothing, gradient magnitude, median filter, region growing and binary morphological dilation are used. The value obtained, for the metric called Dice score, show a good correlation between manual segmentation, performed by a hepatologist, and the tumor segmentation obtained using the proposed technique. This type of segmentation is the extreme utility for the characterization of hepatic tumors and the planning of the clinical behavior to be followed in the treatment of this human liver disease.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series1408 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012001Semi-automatic detection of hepatic tumor in computed tomography imagesarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Latarjet M and Ruiz A 2004 Anatomía humana (Barcelona: Médica Panamericana)Vera M 2014 Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multicorte (Venezuela: Universidad de Los Andes)Tarasik A, Jaroszewicz J, Januszkiewicz M 2017 Surgical treatment of liver tumors – own experience and literature review Clin Exp Hepatol 3(1)Wu W, Wu S, Zhou Z, Zhang R, Zhang Y 2017 3D Liver tumor segmentation in ct images using improved fuzzy c-means and graph cuts BioMed Research International 2017 5207685Chlebus G, Schenk A, Moltz J, Van Ginneken B, Hahn H, Meine H 2018 Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based postprocessing Scientific Reports 8(1) 15497Meijering H 2000 Image enhancement in digital X ray angiography (Netherlands: Utrecht University)Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)Huérfano Y, Vera M, Mar A, Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation. J. Phys. Conf. Ser. 1160 012003González R, Woods R 2001 Digital image processing (New Jersey: Prentice Hall)Petrou M, Bosdogianni P 2003 Image processing the fundamentals (UK: Wiley)Saénz F, Vera M, Huérfano Y, Molina V, Martinez L, Vera MI, Salazar W, Gelvez E, Salazar J, Valbuena O, Robles H, Bautista M, Arango J 2018 Brain hematoma computational segmentation. J. Phys. Conf. 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