Automatic segmentation of a cerebral glioblastoma using a smart computational technique
We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-...
- Autores:
-
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Hoyos, Diego
Arias, Yeni
Contreras, Yudith
Salazar, Williams
Vera, María Isabel
Borrero, Maryury
Vivas, Marisela
Hernández, Carlos
Barrera, Doris
Molina, Ángel Valentín
Martínez, Luis Javier
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:20.500.12442/2524
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2524
- Palabra clave:
- Brain Tomography
Cerebral Tumor
Glioblastoma
Intelligent Computational Technique
Segmentation
Tomografía cerebral
Tumor cerebral
Glioblastoma
Técnica computacional inteligente
Segmentación
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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| dc.title.eng.fl_str_mv |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| dc.title.alternative.spa.fl_str_mv |
Segmentación automática de glioblastoma cerebral usando una técnica computacional inteligente |
| title |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| spellingShingle |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique Brain Tomography Cerebral Tumor Glioblastoma Intelligent Computational Technique Segmentation Tomografía cerebral Tumor cerebral Glioblastoma Técnica computacional inteligente Segmentación |
| title_short |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| title_full |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| title_fullStr |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| title_full_unstemmed |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| title_sort |
Automatic segmentation of a cerebral glioblastoma using a smart computational technique |
| dc.creator.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Hoyos, Diego Arias, Yeni Contreras, Yudith Salazar, Williams Vera, María Isabel Borrero, Maryury Vivas, Marisela Hernández, Carlos Barrera, Doris Molina, Ángel Valentín Martínez, Luis Javier Salazar, Juan Gelvez, Elkin |
| dc.contributor.author.none.fl_str_mv |
Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Hoyos, Diego Arias, Yeni Contreras, Yudith Salazar, Williams Vera, María Isabel Borrero, Maryury Vivas, Marisela Hernández, Carlos Barrera, Doris Molina, Ángel Valentín Martínez, Luis Javier Salazar, Juan Gelvez, Elkin |
| dc.subject.eng.fl_str_mv |
Brain Tomography Cerebral Tumor Glioblastoma Intelligent Computational Technique Segmentation |
| topic |
Brain Tomography Cerebral Tumor Glioblastoma Intelligent Computational Technique Segmentation Tomografía cerebral Tumor cerebral Glioblastoma Técnica computacional inteligente Segmentación |
| dc.subject.spa.fl_str_mv |
Tomografía cerebral Tumor cerebral Glioblastoma Técnica computacional inteligente Segmentación |
| description |
We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to the highest Dc, allows to establish the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow to report a Dc higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-01-25T14:52:01Z |
| dc.date.available.none.fl_str_mv |
2019-01-25T14:52:01Z |
| dc.type.eng.fl_str_mv |
article |
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http://purl.org/coar/resource_type/c_6501 |
| dc.identifier.issn.none.fl_str_mv |
26107988 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/2524 |
| identifier_str_mv |
26107988 |
| url |
http://hdl.handle.net/20.500.12442/2524 |
| 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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http://purl.org/coar/access_right/c_abf2 |
| dc.publisher.spa.fl_str_mv |
Sociedad Venezolana de Farmacología Clínica y Terapéutica |
| dc.source.spa.fl_str_mv |
Revista AVFT-Archivos Venezolanos de Farmacología y Terapéutica Vol. 37, No. 4 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.eng.fl_str_mv |
http://www.revistaavft.com/images/revistas/2018/avft_4_2018/5_automatic_segmentation_of_a_cerebral.pdf |
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Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e3Huérfano, Yoleidy769899ba-e6a1-4144-95c2-ff4614f93578Valbuena, Oscar262b3f8e-b422-4786-b036-2aaa5b963f84Hoyos, Diegoe2ee20bc-d2c2-48f8-9b5c-a7dde7233bcdArias, Yeni0a2c50bf-3166-4cd1-b1c8-a3a7e74434b3Contreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a64Salazar, Williamsfd007214-08c4-4cd6-ae19-7f2ba4f184eaVera, María Isabelc522f56e-ec03-4aa6-9e83-d339a37388acBorrero, Maryuryce8424b3-6f43-4a46-8f73-214fafbb62fdVivas, Mariselafce67a67-3a3b-493c-8fed-422fb00a2e71Hernández, Carlosa82d5fb1-0724-456f-8223-93882ad7278dBarrera, Doris4b365c16-7d6f-4aee-985c-e70d635e8807Molina, Ángel Valentín5fcd607f-8710-40a9-b4dc-b9d1f71d1c1eMartínez, Luis Javierd0fa0a36-7752-496a-979e-48fdb02a5ee9Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Gelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94c2019-01-25T14:52:01Z2019-01-25T14:52:01Z201826107988http://hdl.handle.net/20.500.12442/2524We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to the highest Dc, allows to establish the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow to report a Dc higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed.Proponemos una técnica computacional inteligente para la segmentación de imágenes de un tumor cerebral tipo IV, identificado como glioblastoma multiforme (MGB), que está presente en imágenes de tomografía computarizada de múltiples capas. Esta técnica consiste en 3 etapas desarrolladas en el dominio tridimensional. Ellos son: preprocesamiento, segmentación y validación. Durante la etapa de validación, se considera el coeficiente de dados (Dc) para comparar las segmentaciones del MGB, obtenidas automáticamente, con las segmentaciones del MGB generado manualmente, por un neurooncólogo. La combinación de parámetros vinculados a la mayor Dc permite establecer los parámetros óptimos de cada uno de los algoritmos computacionales que conforman la técnica no lineal propuesta. Los resultados obtenidos permiten informar una Dc superior a 0,88, validando una buena correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada.engSociedad Venezolana de Farmacología Clínica y TerapéuticaRevista AVFT-Archivos Venezolanos de Farmacología y TerapéuticaVol. 37, No. 4 (2018)http://www.revistaavft.com/images/revistas/2018/avft_4_2018/5_automatic_segmentation_of_a_cerebral.pdfBrain TomographyCerebral TumorGlioblastomaIntelligent Computational TechniqueSegmentationTomografía cerebralTumor cerebralGlioblastomaTécnica computacional inteligenteSegmentaciónAutomatic segmentation of a cerebral glioblastoma using a smart computational techniqueSegmentación automática de glioblastoma cerebral usando una técnica computacional inteligentearticlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/access_right/c_abf2Stelzer K. Epidemiology and prognosis of brain metastases. Surg Neurol Int. 2013;4(Suppl 4):S192-202.Mcneill KA. Epidemiology of Brain Tumors. Neurol Clin. 2016;34(4):981-998.American Brain Tumor Association (ABTA). About Brain Tumors: A Primer for Patients and Caregivers. 9ª Edition. 2015 ABTA.WHO (2007). Cavenee W, Louis D, Ohgaki H et al. Eds. WHO Classification of Tumours of the Central Nervous System. WHO Regional Office Europe.Ostrom Q., Gittleman H., Xi J., Kromer C., Wolinsky Y., Krinchko C., Barnholtz J., CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2009- 2013, Neuro Oncol (2016) 18 (suppl 5) v1-v75.Maiera A, Wigstrm L, Hofmann H, Hornegger J, Zhu L, Strobel N, Fahrig R. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT. Medical Physics. 2011;38(11):5896–909.Kroft L, De Roos A, Geleijns J. Artifacts in ECG– synchronized MDCT coronary angiography. American Journal of Roentgenology. 2007;189(3):581–91.Casamitjana A., Puch S., Aduriz A., Vilaplana V. (2017). 3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures. arXiv:1705.08236v1.Zhang, J., Shen, X., Zhuo, T., & Zhou, H. (2017). Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss. arXiv preprint arXiv:1712.09093Kleesiek, J., Biller, A., Urban, G., Kothe, U., Bendszus, M., & Hamprecht, F. (2014). Ilastik for multi-modal brain tumor segmentation. Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge), 12-17.Serra J. Image Analysis Using Mathematical Morphology. London, England: Academic Press, 1982.W. Pratt, Digital Image Processing. USA: John Wiley & Sons Inc, 2007.Mukhopadhyay S., Chanda B. A multiscale morphological approach to local contrast enhancement. Signal Processing, vol. 80, no. 4, pp. 685–696, 2000.Yu Z., Wei G., Zhen C., Jing T., Ling L. Medical images edge detection based on mathematical morphology. En Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai–China, September 2005, pp. 6492–6495.Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, vol. 13, pp. 146–165, 2004.Meijering H. Image en enhancement in digital X ray angiography. Tesis de Doctorado, Utrecht University, Netherlands, 2000.V. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998.E. Osuna, R. Freund, y F. Girosi, “Training support vector machines: an application to face detection.” en Conference on Computer Vision and Pattern Recognition (CVPR ’97), San Juan, Puerto Rico, 1997, pp. 130–136.A. Smola, “Learning with kernels,” Tesis de Doctorado,Technische Universitt Berlin, Germany, 1998.B. Scholkopf y A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA , USA: The MIT Press, 2002.J. Suykens, T. V. Gestel, y J. D. Brabanter, Least Squares Support Vector Machines.UK: World Scientific Publishing Co., 2002.M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, y T. Poggio, “Pedestrian detection using wavelet templates,” en CVPR ’97: Conference on Computer Vision and Pattern Recognition (CVPR ’97). Washington, DC, USA: IEEE Computer Society, 1997, pp. 193–200.Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, Universidad de los Andes, Mérida-Venezuela, 2014.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf950815https://bonga.unisimon.edu.co/bitstreams/d72149ac-6bd7-4069-b7c2-1f2e350ff761/download356b2086485ba2265f81a409d9371ac6MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/2669c0aa-b2e4-4fc3-a102-1752f8915fb8/download3fdc7b41651299350522650338f5754dMD52TEXTAutomatic segmentation of a cerebral.pdf.txtAutomatic segmentation of a cerebral.pdf.txtExtracted texttext/plain31943https://bonga.unisimon.edu.co/bitstreams/898179f6-ac48-49c6-96a6-0c9e994bf1d6/download25b3666f503cc0d5e99a13fb89d033f5MD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain32405https://bonga.unisimon.edu.co/bitstreams/5aa2e1ce-e8f7-4a63-93c0-c339bb8d6799/download7ef14847a5956146adfd3089bff783d4MD55THUMBNAILAutomatic segmentation of a cerebral.pdf.jpgAutomatic segmentation of a cerebral.pdf.jpgGenerated Thumbnailimage/jpeg1957https://bonga.unisimon.edu.co/bitstreams/04753a9c-34be-488e-9dff-0bec3018dc4a/downloadc0ab16e36dde92834d40adfba93a0b40MD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6882https://bonga.unisimon.edu.co/bitstreams/d7ca230c-bef2-4ea6-8ce5-6f0d6a303f85/downloadb0294c4cb3167ee6c103b684c8342468MD5620.500.12442/2524oai:bonga.unisimon.edu.co:20.500.12442/25242024-08-14 21:53:09.21open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |