Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility
This paper proposes a non-linear computational technique for the segmentation of epidural hematomas (EDH), present in 7 multilayer computed tomography brain imaging databases. This technique consists of 3 stages developed in the three-dimensional domain, namely: pre-processing, segmentation and quan...
- Autores:
-
Salazar, Juan
Vera, Miguel
Huérfano, Yoleidy
Valbuena, Oscar
Salazar, Williams
Vera, María Isabel
Gelvez, Elkin
Contreras, Yudith
Borrero, Maryury
Vivas, Marisela
Barrera, Doris
Hernández, Carlos
Molina, Ángel Valentín
Martínez, Luis Javier
Sáenz, Frank
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2522
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2522
- Palabra clave:
- Brain Tomography
Epidural Hematomas
Nonlinear Computational Technique
Smart Operators
Segmentation
Tomografía cerebral
Hematomas epidurales
Técnica computacional no lineal
Operadores inteligentes
Segmentación
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| dc.title.alternative.spa.fl_str_mv |
Segmentación automática de hematomas epidurales usando una técnica computacional, basada en operadores inteligentes: utilidad clínica |
| title |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| spellingShingle |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility Brain Tomography Epidural Hematomas Nonlinear Computational Technique Smart Operators Segmentation Tomografía cerebral Hematomas epidurales Técnica computacional no lineal Operadores inteligentes Segmentación |
| title_short |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| title_full |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| title_fullStr |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| title_full_unstemmed |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| title_sort |
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility |
| dc.creator.fl_str_mv |
Salazar, Juan Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Salazar, Williams Vera, María Isabel Gelvez, Elkin Contreras, Yudith Borrero, Maryury Vivas, Marisela Barrera, Doris Hernández, Carlos Molina, Ángel Valentín Martínez, Luis Javier Sáenz, Frank |
| dc.contributor.author.none.fl_str_mv |
Salazar, Juan Vera, Miguel Huérfano, Yoleidy Valbuena, Oscar Salazar, Williams Vera, María Isabel Gelvez, Elkin Contreras, Yudith Borrero, Maryury Vivas, Marisela Barrera, Doris Hernández, Carlos Molina, Ángel Valentín Martínez, Luis Javier Sáenz, Frank |
| dc.subject.eng.fl_str_mv |
Brain Tomography Epidural Hematomas Nonlinear Computational Technique Smart Operators Segmentation |
| topic |
Brain Tomography Epidural Hematomas Nonlinear Computational Technique Smart Operators Segmentation Tomografía cerebral Hematomas epidurales Técnica computacional no lineal Operadores inteligentes Segmentación |
| dc.subject.spa.fl_str_mv |
Tomografía cerebral Hematomas epidurales Técnica computacional no lineal Operadores inteligentes Segmentación |
| description |
This paper proposes a non-linear computational technique for the segmentation of epidural hematomas (EDH), present in 7 multilayer computed tomography brain imaging databases. This technique consists of 3 stages developed in the three-dimensional domain, namely: pre-processing, segmentation and quantification of the volume occupied by each of the segmented EDHs. To make value judgments about the performance of the proposed technique, the EDH dilated segmentations, obtained automatically, and the EDH segmentations, generated manually by a neurosurgeon, are compared using the Dice coefficient (Dc). The combination of parameters linked to the highest Dc value, defines the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow the reporting of a Dc superior to 0.90 which indicates a good correlation between the manual segmentations and those produced by the computational technique developed. Finally, as an immediate clinical application, considering the automatic segmentations, the volume of each hematoma is calculated considering both the voxel size of each database and the number of voxels that make up the segmented hematomas. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-01-25T14:15:54Z |
| dc.date.available.none.fl_str_mv |
2019-01-25T14:15:54Z |
| 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 |
26107988 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/2522 |
| identifier_str_mv |
26107988 |
| url |
http://hdl.handle.net/20.500.12442/2522 |
| 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 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_2018/2_automatic_segmentation_of_epidural.pdf |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181Vera, 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-d339a37388acGelvez, Elkin90dd023c-1cb7-48ef-bff5-4071ee82a94cContreras, Yudith5ec79ce9-bc7e-44bb-95cb-bf1dab3e3a64Borrero, Maryuryce8424b3-6f43-4a46-8f73-214fafbb62fdVivas, Mariselafce67a67-3a3b-493c-8fed-422fb00a2e71Barrera, 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-8aa6-4286185e2b1d2019-01-25T14:15:54Z2019-01-25T14:15:54Z201826107988http://hdl.handle.net/20.500.12442/2522This paper proposes a non-linear computational technique for the segmentation of epidural hematomas (EDH), present in 7 multilayer computed tomography brain imaging databases. This technique consists of 3 stages developed in the three-dimensional domain, namely: pre-processing, segmentation and quantification of the volume occupied by each of the segmented EDHs. To make value judgments about the performance of the proposed technique, the EDH dilated segmentations, obtained automatically, and the EDH segmentations, generated manually by a neurosurgeon, are compared using the Dice coefficient (Dc). The combination of parameters linked to the highest Dc value, defines the optimal parameters of each of the computational algorithms that make up the proposed nonlinear technique. The obtained results allow the reporting of a Dc superior to 0.90 which indicates a good correlation between the manual segmentations and those produced by the computational technique developed. Finally, as an immediate clinical application, considering the automatic segmentations, the volume of each hematoma is calculated considering both the voxel size of each database and the number of voxels that make up the segmented hematomas.Este artículo propone una técnica computacional no lineal para la segmentación de los hematomas epidurales (EDH), presente en 7 bases de datos de imágenes cerebrales de tomografía multicapa. Esta técnica consta de 3 etapas desarrolladas en el dominio tridimensional, a saber: preprocesamiento, segmentación y cuantificación del volumen ocupado por cada uno de los EDH segmentados. Para hacer juicios de valor sobre el rendimiento de la técnica propuesta, las segmentaciones dilatadas de EDH, obtenidas automáticamente, y las segmentaciones de EDH, generadas manualmente por un neurocirujano, se comparan utilizando el coeficiente de Dice (Dc). La combinación de parámetros vinculados al valor más alto de Dc define los parámetros óptimos de cada uno de los algoritmos computacionales que conforman la técnica no lineal propuesta. Los resultados obtenidos permiten el reporte de un Dc superior a 0.90 que indica una buena correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada. Finalmente, como aplicación clínica inmediata, considerando las segmentaciones automáticas, el volumen de cada hematoma se calcula considerando tanto el tamaño del vóxel de cada base de datos como el número de vóxeles que conforman los hematomas segmentados.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_2018/2_automatic_segmentation_of_epidural.pdfBrain TomographyEpidural HematomasNonlinear Computational TechniqueSmart OperatorsSegmentationTomografía cerebralHematomas epiduralesTécnica computacional no linealOperadores inteligentesSegmentaciónAutomatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utilitySegmentación automática de hematomas epidurales usando una técnica computacional, basada en operadores inteligentes: utilidad clínicaarticlehttp://purl.org/coar/resource_type/c_6501Stippler M. Craniocerebral trauma. In: Daroff RB, Jankovic J, Mazziotta JC, Pomeroy SL, eds. Bradley’s Neurology in Clinical Practice. 7th ed. Philadelphia, PA: Elsevier; 2016: chap 62.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.Liao C., Xiao F., Wong J., Chiang I. Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography. Computerized Medical Imaging and Graphics 34 (2010) 563–571.Dice, L. Measures of the amount of ecologic association between species. Ecology, vol. 26, n. 3, pp. 297-302. 1945.Kamnitsas K., Lediga C. , Newcombeb V., Simpsonb J. , Kaneb A. , Menonb D., Rueckerta D., Glockera B. Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation. Medical Image Analysis, Vol 23, pp.1603-1659, 2017.Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, vol. 13, pp. 146–165, 2004.Pham D., Xu C., Prince J.Current methods in medical image segmentation, Annual Review of Biomedical Engineering. vol. 2, pp. 315–337, 2000.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.Chen T., Wu Q., Rahmani R., Hughes J. A pseudo top-hat mathematical morphological approach to edge detection in dark regions. Pattern Recognition. 2002; 35(1):199–210.Fischer M., Paredes J., Arce G. Weighted median image sharpeners for the world wide web. IEEE Transactions on Image Processing. 2002;11(7):717-27.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.Hu T., Yan L., Yan Peng., Wang X., Yue G. Assessment of the ABC/2 Method of Epidural Hematoma Volume Measurement as Compared to Computer-Assisted Planimetric Analysis. Biological Research for Nursing. 2016, 18(1) 5-11.Freeman, W., Barrett, K., Bestic, J.,Meschia, J., Broderick, D., Brott, T. Computer-assisted volumetric analysis compared with ABC/2 method for assessing warfarinrelated intracranial hemorrhage volumes. 2008, Neurocritical Care, 9, 307–312.Mezzadri J., Goland J., y Sokolvsky M. Introducción a la Neurocirugía. Capítulo: Patología vascular II. Ediciones Journal. Segunda edición. 2011.Vera M. Segmentación de estructuras cardiacas en imágenes de tomografía computarizada multi-corte. Ph.D. dissertation, Universidad de los Andes, Mérida-Venezuela, 2014.Passarielo G., Mora F. Imágenes Médicas, Adquisición, Análisis, Procesamiento e Interpretación. Venezuela: Equinoccio Universidad Simón Bolívar. 1995.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf730512https://bonga.unisimon.edu.co/bitstreams/7f8059c5-8a5b-4f9f-9225-f034bbb56122/download1a6f9a340960da4ebd14817d79468a4aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/d0659b72-7544-4f5e-8aa4-a81518fe4ec5/download3fdc7b41651299350522650338f5754dMD52TEXTAutomatic segmentation of epidural.pdf.txtAutomatic segmentation of epidural.pdf.txtExtracted texttext/plain27711https://bonga.unisimon.edu.co/bitstreams/4b94ab8d-2c07-478c-bce9-d9e090ec194e/downloadf702d224caa8e481035b81c10799c530MD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain28136https://bonga.unisimon.edu.co/bitstreams/7de10925-a5b1-4043-ba3a-f650ee07e962/downloadf1387e37971ece912d4abfbb76e672d4MD55THUMBNAILAutomatic segmentation of epidural.pdf.jpgAutomatic segmentation of epidural.pdf.jpgGenerated Thumbnailimage/jpeg1893https://bonga.unisimon.edu.co/bitstreams/a086b1bf-6419-4e13-a2df-a6017cb3be72/download928d50f28fc5654efbc985b1f1111ecfMD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6389https://bonga.unisimon.edu.co/bitstreams/995008ef-2bf8-415a-b93d-53f4bc1c4619/download609b29890eb209b9d2eb24db4cfe70a2MD5620.500.12442/2522oai:bonga.unisimon.edu.co:20.500.12442/25222024-08-14 21:52:46.881open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |