24 research outputs found
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging
Through this work we propose a computational techniquefor the segmentation of a brain tumor, identified as meningioma(MGT), which is present in magnetic resonance images(MRI). This technique consists of 3 stages developed inthe three-dimensional domain: pre-processing, segmentationand post-processing. The percent relative error (PrE) is consideredto compare the segmentations of the MGT, generatedby a neuro-oncologist manually, with the dilated segmentationsof the MGT, obtained automatically. The combination ofparameters linked to the lowest PrE, provides the optimal parametersof each computational algorithm that makes up theproposed computational technique. Results allow reporting aPrE of 1.44%, showing an excellent correlation between themanual segmentations and those produced by the computationaltechnique developed
Segmentación automática de un meningioma usando una técnica computacional en imágenes de resonancia magnética
Through this work we propose a computational technique
for the segmentation of a brain tumor, identified as meningioma
(MGT), which is present in magnetic resonance images
(MRI). This technique consists of 3 stages developed in
the three-dimensional domain: pre-processing, segmentation
and post-processing. The percent relative error (PrE) is considered
to compare the segmentations of the MGT, generated
by a neuro-oncologist manually, with the dilated segmentations
of the MGT, obtained automatically. The combination of
parameters linked to the lowest PrE, provides the optimal parameters
of each computational algorithm that makes up the
proposed computational technique. Results allow reporting a
PrE of 1.44%, showing an excellent correlation between the
manual segmentations and those produced by the computational
technique developed.Este trabajo propone una técnica computacional para la segmentación
de un tumor cerebral, identificado como meningioma
(MGT), que está presente en imágenes de resonancia
magnética (MRI). Esta técnica consta de 3 etapas desarrolladas
en el dominio tridimensional: preprocesamiento,
segmentación y postprocesamiento. El porcentaje de error
relativo (PrE) se considera para comparar las segmentaciones
de la MGT, generadas por un neurooncólogo de forma
manual, con las segmentaciones dilatadas de la MGT, obtenidas
automáticamente. La combinación de parámetros vinculados
al PrE más bajo proporciona los parámetros óptimos
de cada algoritmo computacional que conforma la técnica
de cálculo propuesta. Los resultados permiten informar un
PrE de 1.44%, mostrando una excelente correlación entre
las segmentaciones manuales y las producidas por la técnica
computacional desarrollada
Motivación para formarse en trabajo social en la Universidad Francisco de Paula Santander de los estudiantes del undécimo (11) semestre
En la presente investigación se tiene como propósito el reconocer las motivaciones de los estudiantes del undécimo (11) semestre para formarse en Trabajo Social. Para llevar a cabo este estudio, se aplicaron dos instrumentos de recolección de información, bajo el método cualitativo. Como resultado, se logró determinar las fortalezas y debilidades en los procesos motivacionales de los estudiantes, para ello, se propusieron estrategias con el fin de fortalecer procesos académicos del programa.Archivo Medios ElectrónicosPregradoTrabajador(a) Socia
Volumetry of subdural hematomas in computed tomography images: ABC methods versus an intelligent computational technique
This work evaluates the performance of some methods orientedtowards the generation of the volume of four subduralhematomas (SDH), present in multi-layer computed tomographyimages. To do this, firstly, a reference volume is specified:the volume obtained by a neurosurgeon using the manualplanimetric method (MPM); which allows the generation ofmanual segmentations of space-occupying lesions. In thiscase, these volumes are matched with the SDH. In parallel,the volumetry of the 4 SDHs is obtained, considering both theoriginal version of the ABC/2 method and two of its variants,identified in this paper as ABC/3 method and 2ABC/3 method.The ABC methods allow the calculation of the volume ofthe hematoma under the assumption that the SDH has anellipsoidal shape. In third place, SDH’s are studied throughan intelligent automatic technique (SAT) that generates thethree-dimensional segmentation of each SDH. Finally, thepercentage relative error is calculated as a metric to evaluatethe methodologies considered. The results show that the SATmethod exhibits the best performance generating an averagepercentage error of less than 5%
Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods
This work evaluates the performance of somemethods employed for assessing the volume ofseven subdural hematomas (EDH), present inmulti-layer computed tomography images. Firstly, a referencevolume is considered to be that obtained by a neurosurgeonusing the manual planimetric method (MPM).Secondly, the volume of the 7 EDHs is obtained consideringboth the original version of the ABC/2 method and two ofits variants, identified in this paper as ABC/3 method and2ABC/3 method. The ABC methods allow for calculationof the volume of the hematoma under the assumptionthat the EDH has an ellipsoidal shape. In third place, anintelligent automatic technique (SAT) is implemented thatgenerates the three-dimensional segmentation of eachEDH and from it the volume of the hematoma is calculated.The SAT consists of the pre-processing, segmentationand post-processing stages. In order to make judgmentsabout the performance of the SAT, the Dice coefficient(Dc) is used to compare the dilated segmentations of theEDH with the EDH segmentations generated manually. Finally,the percentage relative error is calculated as a metricto evaluate the methodologies considered. The resultsshow that the SAT method exhibits the best performancegenerating an average percentage error of less than 2%
Estimación del tamaño de hematomas epidurales en imágenes de tomografía computarizada: estudio comparativo entre métodos lineales y volumétricos
This work evaluates the performance of some
methods employed for assessing the volume of
seven subdural hematomas (EDH), present in
multi-layer computed tomography images. Firstly, a reference
volume is considered to be that obtained by a neurosurgeon
using the manual planimetric method (MPM).
Secondly, the volume of the 7 EDHs is obtained considering
both the original version of the ABC/2 method and two of
its variants, identified in this paper as ABC/3 method and
2ABC/3 method. The ABC methods allow for calculation
of the volume of the hematoma under the assumption
that the EDH has an ellipsoidal shape. In third place, an
intelligent automatic technique (SAT) is implemented that
generates the three-dimensional segmentation of each
EDH and from it the volume of the hematoma is calculated.
The SAT consists of the pre-processing, segmentation
and post-processing stages. In order to make judgments
about the performance of the SAT, the Dice coefficient
(Dc) is used to compare the dilated segmentations of the
EDH with the EDH segmentations generated manually. Finally,
the percentage relative error is calculated as a metric
to evaluate the methodologies considered. The results
show that the SAT method exhibits the best performance
generating an average percentage error of less than 2%
Segmentación automática de hematomas epidurales usando una técnica computacional, basada en operadores inteligentes: utilidad clínica
This paper proposes a non-linear computational technique
for the segmentation of epidural hematomas (EDH), present
in 7 multilayer computed tomography brain imaging databases.
This technique consists of 3 stages developed in the
three-dimensional domain, namely: pre-processing, segmentation
and quantification of the volume occupied by each of
the segmented EDHs. To make value judgments about the
performance of the proposed technique, the EDH dilated segmentations,
obtained automatically, and the EDH segmentations,
generated manually by a neurosurgeon, are compared
using the Dice coefficient (Dc). The combination of parameters
linked to the highest Dc value, defines the optimal parameters
of each of the computational algorithms that make
up the proposed nonlinear technique. The obtained results
allow the reporting of a Dc superior to 0.90 which indicates
a good correlation between the manual segmentations and
those produced by the computational technique developed.
Finally, as an immediate clinical application, considering the automatic
segmentations, the volume of each hematoma is calculated
considering both the voxel size of each database and the
number of voxels that make up the segmented hematomas.Este artículo propone una técnica computacional no lineal
para la segmentación de los hematomas epidurales (EDH),
presente en 7 bases de datos de imágenes cerebrales de
tomografía multicapa. Esta técnica consta de 3 etapas desarrolladas
en el dominio tridimensional, a saber: preprocesamiento,
segmentación y cuantificación del volumen ocupado
por cada uno de los EDH segmentados. Para hacer juicios de
valor sobre el rendimiento de la técnica propuesta, las segmentaciones
dilatadas de EDH, obtenidas automáticamente,
y las segmentaciones de EDH, generadas manualmente por
un neurocirujano, se comparan utilizando el coeficiente de
Dice (Dc). La combinación de parámetros vinculados al valor
más alto de Dc define los parámetros óptimos de cada uno
de los algoritmos computacionales que conforman la técnica
no lineal propuesta. Los resultados obtenidos permiten el reporte
de un Dc superior a 0.90 que indica una buena correlación
entre las segmentaciones manuales y las producidas
por la técnica computacional desarrollada. Finalmente, como
aplicación clínica inmediata, considerando las segmentaciones
automáticas, el volumen de cada hematoma se calcula
considerando tanto el tamaño del vóxel de cada base de datos
como el número de vóxeles que conforman los hematomas
segmentados
Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods
This work evaluates the performance of somemethods employed for assessing the volume ofseven subdural hematomas (EDH), present inmulti-layer computed tomography images. Firstly, a referencevolume is considered to be that obtained by a neurosurgeonusing the manual planimetric method (MPM).Secondly, the volume of the 7 EDHs is obtained consideringboth the original version of the ABC/2 method and two ofits variants, identified in this paper as ABC/3 method and2ABC/3 method. The ABC methods allow for calculationof the volume of the hematoma under the assumptionthat the EDH has an ellipsoidal shape. In third place, anintelligent automatic technique (SAT) is implemented thatgenerates the three-dimensional segmentation of eachEDH and from it the volume of the hematoma is calculated.The SAT consists of the pre-processing, segmentationand post-processing stages. In order to make judgmentsabout the performance of the SAT, the Dice coefficient(Dc) is used to compare the dilated segmentations of theEDH with the EDH segmentations generated manually. Finally,the percentage relative error is calculated as a metricto evaluate the methodologies considered. The resultsshow that the SAT method exhibits the best performancegenerating an average percentage error of less than 2%
Volumetría de hematomas subdurales en imágenes de tomografía computarizada: métodos abc versus una técnica computacional inteligente
This work evaluates the performance of some methods oriented
towards the generation of the volume of four subdural
hematomas (SDH), present in multi-layer computed tomography
images. To do this, firstly, a reference volume is specified:
the volume obtained by a neurosurgeon using the manual
planimetric method (MPM); which allows the generation of
manual segmentations of space-occupying lesions. In this
case, these volumes are matched with the SDH. In parallel,
the volumetry of the 4 SDHs is obtained, considering both the
original version of the ABC/2 method and two of its variants,
identified in this paper as ABC/3 method and 2ABC/3 method.
The ABC methods allow the calculation of the volume of
the hematoma under the assumption that the SDH has an
ellipsoidal shape. In third place, SDH’s are studied through
an intelligent automatic technique (SAT) that generates the
three-dimensional segmentation of each SDH. Finally, the
percentage relative error is calculated as a metric to evaluate
the methodologies considered. The results show that the SAT
method exhibits the best performance generating an average
percentage error of less than 5%.Este trabajo evalúa el rendimiento de algunos métodos orientados
a la generación del volumen de cuatro hematomas
subdurales (SDH), presentes en imágenes de tomografía
computarizada multicapa. Para hacer esto, en primer lugar,
se especifica un volumen de referencia: el volumen obtenido
por un neurocirujano utilizando el método planimétrico manual
(MPM); que permite la generación de segmentaciones
manuales de lesiones ocupantes de espacio. En este caso,
estos volúmenes se comparan con el SDH. Paralelamente,
se obtiene la volumetría de los 4 SDH, considerando tanto
la versión original del método ABC / 2 como dos de sus
variantes, identificadas en este documento como el método
ABC / 3 y el método 2ABC / 3. Los métodos ABC permiten el
cálculo del volumen del hematoma bajo el supuesto de que
el SDH tiene una forma elipsoidal. En tercer lugar, los SDH
se estudian a través de una técnica automática inteligente
(SAT) que genera la segmentación tridimensional de cada
SDH. Finalmente, el error relativo porcentual se calcula como
una métrica para evaluar las metodologías consideradas.
Los resultados muestran que el método SAT exhibe el mejor
rendimiento generando un porcentaje de error promedio de
menos del 5%
Procesamiento digital de imágenes médicas: aplicación a bases de datos sintéticas cardiacas usando la metodología CRISP-DM
In this work an adaptation of the Cross Industry
Standard Process for Data Mining (CRISP-DM) methodology,
in the context of digital medical image
processing is proposed. Specifically, synthetic images reported
in the literature are used as numerical phantoms.
Construction of the synthetic images was inspired by a detailed
analysis of some of the imperfections found in the
real multilayer cardiac computed tomography images. Of
all the imperfections considered, only Poisson noise was
selected and incorporated into a synthetic database. An
example is presented in which images contaminated with
Poisson noise are processed and then subject to two classical
digital smoothing techniques, identified as Gaussian
filter and anisotropic diffusion filter. Additionally, the peak
of the signal-to-noise ratio (PSNR) is considered as a metric
to analyze the performance of these filters