Desarrollo de una técnica computacional no lineal para la segmentación de hematomas subdurales, presentes en imágenes de tomografía computarizada cerebral.

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Title:	Desarrollo de una técnica computacional no lineal para la segmentación de hematomas subdurales, presentes en imágenes de tomografía computarizada cerebral.
Alternate Title:	Development of a non-linear computational technique for the segmentation of subdural hematomas, present in computerized brain tomography images.
Authors:	Vera, Miguel^1,2 m.avera@unisimonbolivar.edu.co, Huérfano, Yoleidy¹, Contreras, Julio², Vera, Maria³, Salazar, Williams³, Vargas, Sandra², Chacón, Gerardo², Rodriguez, Jhoel²
Source:	Archivos Venezolanos de Farmacología y Terapéutica. 2017, Vol. 36 Issue 6, p168-173. 6p.
Abstract (English):	The main in this paper is to propose non-linear computational technique to segment a subdural hematoma (SDH), present in multilayered computed tomography images. This technique consists of 4 stages developed in the three-dimensional domain: pre-processing, segmentation, post-processing and intonation of parameters. The pre-processing stage is divided into two phases. In the first one, called the definition of a volume of interest (VOI), a band thresholding algorithm is used which allows, fundamentally, to delimit the SDH considered. In the second phase, identified as filtering, a bank of computational algorithms is applied to reduce the impact of the artifacts and attenuate the noise present in the images. The algorithms that make up this phase are: the morphological erosion filter (MEF) and the median filter (MF). On the other hand, during the segmentation stage a grouping algorithm is implemented, called growth of regions (RG), which is applied to the pre-processed images. In order to compensate the effect of the MEF, the SDH, preliminarily segmented, is subjected to the post-processing stage, which is based on the application of a morphological dilation filter of binary type (MDF). During the intonation of parameters, the coefficient of Dice (Dc) is used to compare the dilated segmentations of the SDH, obtained automatically, with the SDH segmentation generated by a neurosurgeon manually. The combination of parameters that generate 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 superior to 0.88 which indicates a good correlation between the segmentations generated by the expert neurosurgeon and those produced by the developed computational technique. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	permite, fundamentalmente, acotar el SDH considerado. En la segunda fase, identificada como filtrado, se aplica un banco de algoritmos computacionales para disminuir el impacto de los artefactos y atenuar el ruido presente en las imágenes. Los algoritmos que conforman esta fase son: el filtro de erosión morfológica (MEF) y el filtro de mediana (MF). Por otra parte, durante la etapa de segmentación se implementa un algoritmo de agrupamiento, denominado crecimiento de regiones (RG), el cual es aplicado a las imágenes pre-procesadas. A fin de compensar el efecto del MEF el SDH, segmentado preliminarmente, es sometido a la etapa de pos-procesamiento la cual se basa en la aplicación de un filtro de dilatación morfológica de tipo binaria (MDF). Durante la entonación de parámetros, el coeficiente de Dice (Dc) es utilizado para comparar las segmentaciones dilatadas del SDH, obtenidas automáticamente, con la segmentación del SDH generada por un neurocirujano de manera manual. La combinación de parámetros que generan el Dc más elevado, permite establecer los parámetros óptimos de cada una de los algoritmos computacionales que conforman la técnica no lineal propuesta. Los resultados obtenidos permiten reportar un Dc superior a 0.88 lo cual indica una buena correlación entre las segmentaciones generadas por el experto neurocirujano y las producidas por la técnica computacional desarrollada. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	The main in this paper is to propose non-linear computational technique to segment a subdural hematoma (SDH), present in multilayered computed tomography images. This technique consists of 4 stages developed in the three-dimensional domain: pre-processing, segmentation, post-processing and intonation of parameters. The pre-processing stage is divided into two phases. In the first one, called the definition of a volume of interest (VOI), a band thresholding algorithm is used which allows, fundamentally, to delimit the SDH considered. In the second phase, identified as filtering, a bank of computational algorithms is applied to reduce the impact of the artifacts and attenuate the noise present in the images. The algorithms that make up this phase are: the morphological erosion filter (MEF) and the median filter (MF). On the other hand, during the segmentation stage a grouping algorithm is implemented, called growth of regions (RG), which is applied to the pre-processed images. In order to compensate the effect of the MEF, the SDH, preliminarily segmented, is subjected to the post-processing stage, which is based on the application of a morphological dilation filter of binary type (MDF). During the intonation of parameters, the coefficient of Dice (Dc) is used to compare the dilated segmentations of the SDH, obtained automatically, with the SDH segmentation generated by a neurosurgeon manually. The combination of parameters that generate 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 superior to 0.88 which indicates a good correlation between the segmentations generated by the expert neurosurgeon and those produced by the developed computational technique. [ABSTRACT FROM AUTHOR]
ISSN:	07980264