High grade glioma segmentation in magnetic resonance imaging
| dc.contributor.author | Vera, Miguel | |
| dc.contributor.author | Huérfano, Yoleidy | |
| dc.contributor.author | Martínez, Luis Javier | |
| dc.contributor.author | Contreras, Yudith | |
| dc.contributor.author | Salazar, Williams | |
| dc.contributor.author | Vera, María Isabel | |
| dc.contributor.author | Valbuena, Oscar | |
| dc.contributor.author | Borrero, Maryury | |
| dc.contributor.author | Hernández, Carlos | |
| dc.contributor.author | Barrera, Doris | |
| dc.contributor.author | Molina, Ángel Valentín | |
| dc.contributor.author | Salazar, Juan | |
| dc.contributor.author | Gelvez, Elkin | |
| dc.contributor.author | Sáenz, Frank | |
| dc.contributor.author | Hoyos, Diego | |
| dc.contributor.author | Arias, Yeny | |
| dc.date.accessioned | 2019-01-25T16:42:23Z | |
| dc.date.available | 2019-01-25T16:42:23Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Through this work we propose a computational technique for the segmentation of magnetic resonance images (MRI) of a brain tumor, identified as high grade glioma (HGG), specifically grade III anaplastic astrocytoma. This technique consists of 3 stages developed in the threedimensional domain. They are: pre-processing, segmentation and post-processing. The pre-processing stage uses a thresholding technique, morphological erosion filter (MEF), in gray scale, followed by a median filter and a gradient magnitude algorithm. On the other hand, in order to obtain a HGG preliminary segmentation, during the segmentation stage a clustering algorithm called region growing (RG) is implemented and it is applied to the preprocessed images. The RG requires, for its initialization, a seed voxel whose coordinates are obtained, automatically, through the training and validation of an intelligent operator based on support vector machines (SVM). Due to the high sensitivity of the RG to the location of the seed, the SVM is implemented as a highly selective binary classifier. During the post-processing stage, a morphological dilation filter is applied to preliminary segmentation generated by RG. The percent relative error (PrE) is considered by comparing the segmentations of the HGG, generated manually by a neuro-oncologist, with the dilated segmentations of the HGG, obtained automatically. The combination of parameters linked to the lowest PrE, allows establishing the optimal parameters of each computational algorithms that make up the proposed computational technique. The obtained results allow reporting a PrE of 11.10%, which indicates a good correlation between the manual segmentations and those produced by the computational technique developed. | eng |
| dc.description.abstract | A través de este trabajo se propone una técnica computacional para la segmentación de un tumor cerebral, identificado como un glioma de alto grado (HGG) de tipo astrocitoma anaplásico de grado III, que está presente en las imágenes de resonancia magnética (MRI). Esta técnica consta de 3 etapas desarrolladas en el dominio tridimensional. Ellas son: preprocesamiento, segmentación y postprocesamiento. La etapa de preprocesamiento utiliza una técnica de umbralización, un filtro de erosión morfológica (MEF), en escala de grises, seguido de un filtro de mediana y de un algoritmo de magnitud de gradiente. Por otro lado, con el propósito de generar una segmentación preliminar del HGG, durante la etapa de segmentación se implementa un algoritmo de agrupamiento, llamado crecimiento de regiones (RG), que se aplica a las imágenes preprocesadas. El RG requiere para su inicialización la ubicación de un vóxel semilla cuyas coordenadas se obtienen, automáticamente, a través del entrenamiento y la validación de un operador inteligente basado en máquinas de vectores de soporte (SVM). Debido a la alta sensibilidad del RG a la ubicación de la semilla, la SVM se implementa como un clasificador binario altamente selectivo. Durante la etapa de post-procesamiento, se aplica un filtro de dilatación morfológica a la segmentación preliminar, generada por RG. El error relativo porcentual (PrE) se considera para comparar las segmentaciones de la HGG generadas de forma manual por un neurooncólogo, con las segmentaciones dilatadas de la HGG, obtenidas automáticamente. La combinación de parámetros vinculados al PrE más bajo permite establecer los parámetros óptimos de cada uno de los algoritmos computacionales que componen la técnica computacional propuesta. Los resultados obtenidos permiten reportar un PrE de 11.10%, lo cual indica una buena correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada. | spa |
| dc.identifier.issn | 18564550 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12442/2528 | |
| dc.language.iso | eng | eng |
| dc.publisher | Sociedad Latinoamericana de Hipertensión | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
| dc.source | Revista Latinoamericana de Hipertensión | spa |
| dc.source | Vol. 13, No. 4 (2018) | spa |
| dc.source.uri | http://www.revhipertension.com/rlh_4_2018/3_high_grade_glioma_segmentation.pdf | eng |
| dc.subject | Magnetic resonance brain imaging | eng |
| dc.subject | Cerebral tumor | eng |
| dc.subject | High grade glioma | eng |
| dc.subject | Grade III anaplastic astrocytoma | eng |
| dc.subject | Computational technique | eng |
| dc.subject | Segmentation | eng |
| dc.subject | Imágenes cerebrales por resonancia magnética | spa |
| dc.subject | Tumor cerebral | spa |
| dc.subject | Gliomas de alto grado | spa |
| dc.subject | Astrocitoma anaplásico de grado III | spa |
| dc.subject | Técnica computacional | spa |
| dc.subject | Segmentación | spa |
| dc.title | High grade glioma segmentation in magnetic resonance imaging | eng |
| dc.title.alternative | Segmentación de glioma de alto grado en imágenes de resonancia magnética | spa |
| dc.type | article | eng |
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