Brain hematoma computational segmentation
In computed tomography imaging, brain hematoma (BH) segmentation is a very challenging process due to a high variability of BH morphology, low contrast and noisy images. Because of this, BH segmentation is an open problem. In order to approach this problem, we propose an automatic technique, named n...
- Autores:
-
Sáenz, F
Vera, M
Huerfano, Y
Molina, V
Martinez, L
Vera, M I
Salazar, W
Gelvez, E
Salazar, J
Valbuena, O
Robles, H
Bautista, M
Arango, J
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2531
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2531
- Palabra clave:
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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Brain hematoma computational segmentation |
| title |
Brain hematoma computational segmentation |
| spellingShingle |
Brain hematoma computational segmentation |
| title_short |
Brain hematoma computational segmentation |
| title_full |
Brain hematoma computational segmentation |
| title_fullStr |
Brain hematoma computational segmentation |
| title_full_unstemmed |
Brain hematoma computational segmentation |
| title_sort |
Brain hematoma computational segmentation |
| dc.creator.fl_str_mv |
Sáenz, F Vera, M Huerfano, Y Molina, V Martinez, L Vera, M I Salazar, W Gelvez, E Salazar, J Valbuena, O Robles, H Bautista, M Arango, J |
| dc.contributor.author.none.fl_str_mv |
Sáenz, F Vera, M Huerfano, Y Molina, V Martinez, L Vera, M I Salazar, W Gelvez, E Salazar, J Valbuena, O Robles, H Bautista, M Arango, J |
| description |
In computed tomography imaging, brain hematoma (BH) segmentation is a very challenging process due to a high variability of BH morphology, low contrast and noisy images. Because of this, BH segmentation is an open problem. In order to approach this problem, we propose an automatic technique, named nonlinear technique (NLT), based on a thresholding method, noise suppression filters, intelligent operators, a clustering strategy and a binary morphological operator. NLT performance is assessed by Jaccard's similarity index (JSI) used to compare automatic and manual BH segmentations. This assessment allows developing a tuning process for establishing the optimal parameters of each of the algorithms which constitute the proposed technique. The results indicate a good correlation, based on JSI, between the manual segmentations and the automatic ones. Finally, the BH volume is generated considering the automatic segmentation. This volume indicates whether or not the patient must undergo a surgical intervention for BH treatment. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-01-25T20:04:46Z |
| dc.date.available.none.fl_str_mv |
2019-01-25T20:04:46Z |
| 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 |
17426588 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/2531 |
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17426588 |
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http://hdl.handle.net/20.500.12442/2531 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| dc.publisher.eng.fl_str_mv |
IOP Publishing |
| dc.source.eng.fl_str_mv |
Journal of Physics: Conference Series |
| dc.source.spa.fl_str_mv |
Vol. 1126, No. 012071 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.eng.fl_str_mv |
doi :10.1088/1742-6596/1126/1/012071 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Sáenz, Fe7336b90-cde6-4d03-880d-55f6a198725d-1Vera, M847eada8-99d3-4ff1-a613-ae3f62c30f9e-1Huerfano, Y8a6a81bd-56d9-4903-9c91-b4203d27ef83-1Molina, V032250ff-d108-4692-b72d-63a013ff98b4-1Martinez, L1355c5ff-8425-4dff-bdf2-9868f1a11037-1Vera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363e-1Salazar, Wf373f4f6-6308-4037-aa3f-bbcbde9cbe1b-1Gelvez, Ed34b29f4-5323-4e58-83ca-7ae2e85e1ce0-1Salazar, J6f1d932b-654d-42d9-bc5b-30b467b897d2-1Valbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9-1Robles, Ha24aace4-ae71-4c29-86a0-43aa3648e5bf-1Bautista, M2fdc3acb-b41e-45fd-8bf5-739dab74ea9d-1Arango, J2990edc6-3822-4731-aa68-7ddbbf479124-12019-01-25T20:04:46Z2019-01-25T20:04:46Z201817426588http://hdl.handle.net/20.500.12442/2531In computed tomography imaging, brain hematoma (BH) segmentation is a very challenging process due to a high variability of BH morphology, low contrast and noisy images. Because of this, BH segmentation is an open problem. In order to approach this problem, we propose an automatic technique, named nonlinear technique (NLT), based on a thresholding method, noise suppression filters, intelligent operators, a clustering strategy and a binary morphological operator. NLT performance is assessed by Jaccard's similarity index (JSI) used to compare automatic and manual BH segmentations. This assessment allows developing a tuning process for establishing the optimal parameters of each of the algorithms which constitute the proposed technique. The results indicate a good correlation, based on JSI, between the manual segmentations and the automatic ones. Finally, the BH volume is generated considering the automatic segmentation. This volume indicates whether or not the patient must undergo a surgical intervention for BH treatment.engIOP PublishingJournal of Physics: Conference SeriesVol. 1126, No. 012071 (2018)doi :10.1088/1742-6596/1126/1/012071Brain hematoma computational segmentationarticlehttp://purl.org/coar/resource_type/c_6501Stippler M 2016 Craniocerebral trauma Bradley's Neurology in Clinical Practice vol 2 ed Robert B. Daroff, Joseph Jankovic, John C Mazziotta, Scott L Pomeroy (Philadelphia: Elsevier) chapter 62 pp 867– 880Maier A, Wigstrom L, Hofmann H G, Hornegger J, Zhu L, Strobel N and Fahrig R 2011 Threedimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT Medical Physics 38 5896Li-Hong J and Wu M 2010 MRI brain lesion image detection based on colour converted k-means clustering segmentation Measurement 43 941Roy S, Nag S, Bandyopadhyay S K, Bhattacharyya D and Kim T H 2015 Automated brain haemorrhage lesion segmentation and classification from MR image using an innovative composite method Journal of Theoretical and Applied Information Technology 78 34Kamnitsas K, Lediga C, Newcombeb V, Simpsonb J, Kaneb A, Menonb D, Rueckerta D, Glockera B 2017 Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal 23 1603Liao C, Xiao F, Wong J, Chiang I 2010 Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography. Comput Med Imaging Graph 34 563Vera M, Martinez L J, Huerfano Y, Molina V, Vargas S, Vera M, Salazar W, Rodriguez J, Rodriguez R, Chacon G, Isaza A, Saenz F, Glevez E and Salazar J 2018 Automatic segmentation of subdural hematomas using a computational technique based on smart operators Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (Porto) 1 1Sharma B and Venugopalan K 2012 Automatic segmentation of brain ct scan image to identify hemorrhages. International Journal of Computer Applications 40 1Al-Ayyoub M, Alawad D, Al-Darabsah K and Aljarrah I 2013. Automatic detection and classification of brain hemorrhages. WSEAS Transactions on Computers 10 395Vera M, Bravo A and Medina R 2011 Improving ventricle detection in 3D cardiac multislice computerized tomography images Communications in Computer and Information Science 229 170Real R and Vargas J 1996. The probabilistic basis of Jaccard's index of similarity Syst. Biol 45 380Hu T, Yan L, Yan P, Wang X and Yue G 2016 Assessment of the ABC/2 method of epidural hematoma volume measurement as compared to computer-assisted planimetric analysis Biological Research for Nursing 18 5ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf705519https://bonga.unisimon.edu.co/bitstreams/2e4d7340-1ce0-4b13-ae6c-f7f76ee6fefd/downloadff151921e182650ac3ba59fdab190c5dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/37edeb09-f8b7-4967-885e-3465905f1e13/download3fdc7b41651299350522650338f5754dMD52TEXTBrain hematoma computational segmentation.pdf.txtBrain hematoma computational segmentation.pdf.txtExtracted texttext/plain16423https://bonga.unisimon.edu.co/bitstreams/b09fb770-b25f-4ca0-9021-63b3b378e666/downloadd5dce208918c0459c27f161439916e7dMD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain16917https://bonga.unisimon.edu.co/bitstreams/09ae362c-a9b9-4e0d-8ab7-7fe246d4fad4/downloadaf23d05c2f096629656fb47a2bba359dMD55THUMBNAILBrain hematoma computational segmentation.pdf.jpgBrain hematoma computational segmentation.pdf.jpgGenerated Thumbnailimage/jpeg1270https://bonga.unisimon.edu.co/bitstreams/8bc2fb4e-a824-48a4-9b93-46e59ef65637/downloada8bbbdb18cfea85e056f9af36495c66cMD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3179https://bonga.unisimon.edu.co/bitstreams/b273e2ca-3b20-4a88-b936-fead1254b35f/download0855009b3fcd86fdeffbf0f30257ed6fMD5620.500.12442/2531oai:bonga.unisimon.edu.co:20.500.12442/25312024-07-25 03:03:52.522open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |