Segmentation of brain tumors using a semi-automatic computational strategy
| dc.contributor.author | Vera, M. | |
| dc.contributor.author | Huérfano, Y. | |
| dc.contributor.author | Gelvez, E. | |
| dc.contributor.author | Valbuena, O. | |
| dc.contributor.author | Salazar, J. | |
| dc.contributor.author | Molina, V. | |
| dc.contributor.author | Vera, M I. | |
| dc.contributor.author | Salazar, W. | |
| dc.contributor.author | Sáenz, F. | |
| dc.date.accessioned | 2019-03-06T21:32:50Z | |
| dc.date.available | 2019-03-06T21:32:50Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this work, a semi-automatic computational strategy is proposed for brain tumor segmentation. The filtering (erosion + gaussian filters), segmentation (level set technique) and quantification (BT volume) stages are applied to magnetic resonance imaging in order to generate the three-dimensional morphology of brain tumors. The Jaccard's Similarity Index is considered to contrast manual segmentation with semi-automatic segmentations of brain tumor. In this sense, the highest Jaccard's Similarity Index provides the best parameters of the techniques that constitute the semi-automatic computational strategy. Results are promising, showing an excellent correlation between these segmentations. The volume is used for the brain tumors characterization. | eng |
| dc.identifier.issn | 09767673 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12442/2740 | |
| dc.language.iso | eng | eng |
| dc.publisher | IOP Publishing | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
| dc.source | Journal of Physics | eng |
| dc.source | IOP Conf. Series: Journal of Physics: Conf. Series 1160 (2019) 012002 | eng |
| dc.source.uri | doi:10.1088/1742-6596/1160/1/012002 | eng |
| dc.subject | Head - Tumors | eng |
| dc.subject | Magnetic resonance | eng |
| dc.subject | Jaccard | eng |
| dc.title | Segmentation of brain tumors using a semi-automatic computational strategy | eng |
| dc.type | Conference | eng |
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