Realce por similaridad local para la segmentación computacional de la aurícula derecha en imágenes de tomografía computarizada cardiaca.

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Bibliographic Details
Title:	Realce por similaridad local para la segmentación computacional de la aurícula derecha en imágenes de tomografía computarizada cardiaca.
Alternate Title:	Local similarity enhancement for the computational segmentation of the right atrium in cardiac computed tomography.
Authors:	Huérfano, Yoleidy¹ yoleidyfismat@gmail.com, Vera, Miguel^1,2, Contreras-Velásquez, Julio², Del Mar, Atilio³, Chacón, José², Wilches-Durán, Sandra², Graterol-Rivas, Modesto², Riaño-Wilches, Daniela⁴, Rojas, Joselyn⁵, Bermúdez, Valmore^2,6
Source:	Revista Latinoamericana de Hipertensión. 2017, Vol. 12 Issue 2, p62-69. 8p.
Abstract (English):	This work proposes a strategy to segment the right atrium (RA) into three-dimensional (3-D) multi-layer Computed Tomography (CT) images. This strategy consists of the stages of pre-processing, segmentation and intonation of parameters. The pre-processing stage is divided into two phases. In the first one, called the filtering phase, a technique called Local Similarity Enhancement (LSE) is used in order to reduce the impact of artifacts and attenuate noise in the quality of the images. This technique combines an averaging filter, an edge detector filter (called black top hat) and a Gaussian Filter (GF). In the second, identified as the phase of definition of a Region Of Interest (ROI), we consider filtered images, least squares vector support machines and a priori information to isolate the anatomical structures that surround the RA. On the other hand, a clustering algorithm, called Region Growth (RG), is implemented during the 3-D segmentation stage, which is applied to the preprocessed images. During the intonation of parameters of the proposed strategy, the Dice coefficient (Dc) is used to compare the segmentations, obtained automatically, with the segmentation of the right atrium generated manually by a cardiologist. The combination of parameters that generated the highest Dc considering the instant of diastole is then applied to the remaining 19 three-dimensional images, obtaining an average Dc higher than 0.85 which indicates a good correlation between the segmentations generated by the expert cardiologist and those produced by the strategy developed. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	Mediante este trabajo se propone una estrategia para segmentar la aurícula derecha (RA) en imágenes tridimensionales (3-D) de tomografía computarizada multicapa. Esta estrategia consta de las etapas de pre-procesamiento, segmentación y entonación de parámetros. La etapa de pre-procesamiento se divide en dos fases. En la primera, denominada fase de filtrado, se emplea una técnica denominada realce por similaridad local (LSE) con el propósito de disminuir el impacto de los artefactos y atenuar el ruido en la calidad de las imágenes. Esta técnica, combina un filtro promediador, un filtro detector de bordes (denominado black top hat) y un filtro gaussiano (GF). En la segunda, identificada como fase de definición de una región de interés (ROI), se consideran las imágenes filtradas, máquinas de soporte vectorial de mínimos cuadrados e información a priori para aislar las estructuras anatómicas que circundan la RA. Por otra parte, durante la etapa de segmentación 3-D se implementa un algoritmo de agrupamiento, denominado crecimiento de regiones (RG), el cual es aplicado a las imágenes pre-procesadas. Durante la entonación de parámetros de la estrategia propuesta, el coeficiente de Dice (Dc) es utilizado para comparar las segmentaciones, de la RA, obtenidas automáticamente, con la segmentación de la aurícula derecha generada, manualmente, por un cardiólogo. La combinación de parámetros que generó el Dc más elevado considerando el instante de diástole se aplica luego a las 19 imágenes tridimensionales restantes, obteniéndose un Dc promedio superior a 0.85 lo cual indica una buena correlación entre las segmentaciones generadas por el experto cardiólogo y las producidas por la estrategia desarrollada. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	This work proposes a strategy to segment the right atrium (RA) into three-dimensional (3-D) multi-layer Computed Tomography (CT) images. This strategy consists of the stages of pre-processing, segmentation and intonation of parameters. The pre-processing stage is divided into two phases. In the first one, called the filtering phase, a technique called Local Similarity Enhancement (LSE) is used in order to reduce the impact of artifacts and attenuate noise in the quality of the images. This technique combines an averaging filter, an edge detector filter (called black top hat) and a Gaussian Filter (GF). In the second, identified as the phase of definition of a Region Of Interest (ROI), we consider filtered images, least squares vector support machines and a priori information to isolate the anatomical structures that surround the RA. On the other hand, a clustering algorithm, called Region Growth (RG), is implemented during the 3-D segmentation stage, which is applied to the preprocessed images. During the intonation of parameters of the proposed strategy, the Dice coefficient (Dc) is used to compare the segmentations, obtained automatically, with the segmentation of the right atrium generated manually by a cardiologist. The combination of parameters that generated the highest Dc considering the instant of diastole is then applied to the remaining 19 three-dimensional images, obtaining an average Dc higher than 0.85 which indicates a good correlation between the segmentations generated by the expert cardiologist and those produced by the strategy developed. [ABSTRACT FROM AUTHOR]
ISSN:	18564550