Repositorio :: Examinando por Autor "Valbuena, Oscar"

Examinando por Autor "Valbuena, Oscar"

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Assessment of methods for volumetric quantification of intracerebral hematomas in computerized tomography images
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Vera, Miguel; Huérfano, Yoleidy; Barrera, Doris; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Hernández, Carlos; Vivas, Marisela; Borrero, Maryury; Molina, Ángel Valentín; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin; Contreras, Yudith; Sáenz, Frank
This work evaluates the performance of computational methods aimed at volume generation of five intracerebral hematomas (ICH), present in multi-layer computed tomography images, by means of three complementary steps. First. A ground truth volume or reference volume (RV) is considered. This RV is obtained, by a neurosurgeon, using the manual planimetric method (MPM). In a second step, the volumetry of the 5 ICH’s 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 calculating hematoma volume under the geometric assumption that the ICH has an ellipsoidal shape. In a third step, a smart automatic technique (SAT) is implemented to generate the three-dimensional segmentation of each ICH. In the context of the present work, the expression SAT method is used to refer to the new methodology proposed to calculate the volume of the ICH. In order to evaluate the performance of the SAT, the Dice coefficient (Dc) is used to compare the dilated segmentations of the ICH with the ICH segmentations generated, manually, by a neurosurgeon. Finally, the percentage relative error is calculated as a measure to evaluate the methodologies considered. The results show that the SAT method exhibits the best performance, generating an average percentage error of less than 3%.
Automatic segmentation of a cerebral glioblastoma using a smart computational technique
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Vera, Miguel; Huérfano, Yoleidy; Valbuena, Oscar; Hoyos, Diego; Arias, Yeni; Contreras, Yudith; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Vivas, Marisela; Hernández, Carlos; Barrera, Doris; Molina, Ángel Valentín; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin
We propose an intelligent computational technique for the image segmentation of a type IV brain tumor, identified as multiform glioblastoma (MGB), which is present in multi-layer computed tomography images. This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and validation. During the validation stage, the Dice coefficient (Dc) is considered in order to compare the segmentations of the MGB, obtained automatically, with the segmentations of the MGB generated manually, by a neuro-oncologist. The combination of parameters linked to 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 higher than 0.88, validating a good correlation between the manual segmentations and those produced by the computational technique developed.
Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Vera, Miguel; Huérfano, Yoleidy; Molina, Ángel Valentín; Valbuena, Oscar; Vivas, Marisela; Cuberos, María; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Hernández, Carlos; Barrera, Doris; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin; Contreras, Yudith; Sáenz, Frank
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.
Automatic segmentation of epidural hematomas using a computational technique based on intelligent operators: a clinical utility
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Salazar, Juan; Vera, Miguel; Huérfano, Yoleidy; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Gelvez, Elkin; Contreras, Yudith; Borrero, Maryury; Vivas, Marisela; Barrera, Doris; Hernández, Carlos; Molina, Ángel Valentín; Martínez, Luis Javier; Sáenz, Frank
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.
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology
(Sociedad Latinoamericana de Hipertensión, 2018) Contreras, Yudith; Vera, Miguel; Huérfano, Yoleidy; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Barrera, Doris; Hernández, Carlos; Molina, Ángel Valentín; Martínez, Luis Javier; Sáenz, Frank; Vivas, Marisela; Salazar, Juan; Gelvez, Elkin
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.
High grade glioma segmentation in magnetic resonance imaging
(Sociedad Latinoamericana de Hipertensión, 2018) Vera, Miguel; Huérfano, Yoleidy; Martínez, Luis Javier; Contreras, Yudith; Salazar, Williams; Vera, María Isabel; Valbuena, Oscar; Borrero, Maryury; Hernández, Carlos; Barrera, Doris; Molina, Ángel Valentín; Salazar, Juan; Gelvez, Elkin; Sáenz, Frank; Hoyos, Diego; Arias, Yeny
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.
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Vera, Miguel; Huérfano, Yoleidy; Valbuena, Oscar; Contreras, Yudith; Cuberos, María; Vivas, Marisela; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Hernández, Carlos; Barrera, Doris; Molina, Ángel Valentín; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin; Sáenz, Frank
Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain. They are: pre-processing, segmentation and postprocessing. The percent relative error (PrE) is considered to compare the segmentations of the LGG, generated by a neuro- oncologist manually, with the dilated segmentations of the LGG, obtained automatically. The combination of parameters linked to the lowest PrE, allow establishing the optimal parameters of each computational algorithm that makes up the proposed computational technique. The results allow reporting a PrE of 1.43%, which indicates an excellent correlation between the manual segmentations and those produced by the computational technique developed.
Una revisión actual de las técnicas computacionales para la caracterización de enfermedades vinculadas con la válvula aórtica
(Sociedad Venezolana de Hipertensión, 2020) Valbuena, Oscar; Vera, Miguel; Borrero, Maryuri; Huérfano, Yuleidy; Capacho, Yulian
En los últimos años, los avances en imagenología médica estan cambiado la forma de obtener información anatómica y funcional de las estructuras vinculadas con el corazón, particularmente, de las válvulas cardíacas. En este artículo se hace una revisión, que abarca el periodo 2014-2020, sobre las técnicas computacionales usadas en la caracterización, vía segmentación, de las enfermedades que afectan las mencionadas válvulas. La presente revisión proporciona información actualizada acerca de: a) enfermedades que afectan las válvulas, b) principales modalidades de adquisición de imágenes cardíacas, c) últimos avances en prótesis de válvulas aórticas empleadas en el implante valvular aórtico transcatéter (TAVI), d) técnicas usadas para la segmentación y caracterización de las válvulas. Los principales hallazgos indican que se destaca la tomografía computarizada para hacer una caracterización de la geometría y de la capacidad funcional de los principales tejidos de las válvulas; mientras que se ha proliferado el uso de prótesis, de última generación, las cuales tienden a disminuir las complicaciones clínicas posterior al remplazo de válvula y, a su vez, elevan la calidad de vida del paciente, razón por la cual el TAVI es cada vez más frecuente en pacientes de moderado y bajo riesgo quirúrgico.
Semi-automated detection of aortic root in human heart MSCT images using nonlinear filtering and unsupervised clustering
(Inderscience Publishers, 2021) Valbuena, Oscar; Vera, Miguel Ángel; Del Mar, Atilio; Roa, Felida Andreina; Bravo, Antonio José
Abstract: A semiautomatic technique to detect the aortic root in three-dimensional multi-slice computerised tomography images is proposed. Three steps are considered: conditioning, filtering, and detection. The conditioning is based on multi-planar reconstruction and it is required for reformatting the information to orthogonal planes to the aortic root. During the filtering, three nonlinear filters based on similarity enhancement, median and weighted median are considered to reduce noise and enhance the reformatted images. In the detection, the filtered volumes are processed with a clustering technique. Dice score, the point-to-mesh and the Hausdorff distances are used to compare the obtained results with respect to ground truth traced by a cardiologist. A clinical dataset of 90 volumes from 45 patients is used to validate the technique. The maximum Dice score (0.92), the minimum average point-to-mesh distance (0.96 mm) and the minimum average Hausdorff distance (4.80 mm) are obtained during preprocessed volumes segmentation using similarity enhancement.
Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
(Sociedad Latinoamericana de Hipertensión, 2018) Gelvez, Elkin; Vera, Miguel; Huérfano, Yoleidy; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Borrero, Maryury; Barrera, Doris; Hernández, Carlos; Molina, Ángel Valentín; Martínez, Luis Javier; Sáenz, Frank; Vivas, Marisela; Contreras, Julio; Restrepo, Jorge; Vanegas, Juan; Salazar, Juan; Contreras, Yudith
This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during the course of this evaluation are available freely on the Internet and are reported in specialized literature as synthetic images called BrainWeb. The images that belong to this DB were contaminated with Rician noise, this being the most frequent type of noise in real MRI images. Also, the techniques that are usually considered to minimize the impact of Rician noise on the quality of BrainWeb images are matched with the Gaussian filter (GF) and an anisotropic diffusion filter, based on the gradient of the image (GADF). Each of these filters has 2 parameters that control their operation and, therefore, undergo a rigorous tuning process to identify the optimal values that guarantee the best performance of both the GF and the GADF. The peak of the signal-to-noise ratio (PSNR) and the computation time are considered as key elements to analyze the behavior of each of the filtering techniques applied. The results indicate that: a) both filters generate PSNR values comparable to each other. b) The GF requires a significantly shorter computation time to soften the Rician noise present in the considered DB.
Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods
(Sociedad Latinoamericana de Hipertensión, 2018) Vera, Miguel; Huérfano, Yoleidy; Hernández, Carlos; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Barrera, Doris; Borrero, Maryury; Molina, Ángel Valentín; Martínez, Luis Javier; Salazar, Juan; Gelvez, Elkin; Contreras, Yudith; Saenz, Frank
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%.
Volumetry of subdural hematomas in computed tomography images: ABC methods versus an intelligent computational technique
(Sociedad Venezolana de Farmacología Clínica y Terapéutica, 2018) Vera, Miguel; Huérfano, Yoleidy; Borrero, Maryury; Valbuena, Oscar; Salazar, Williams; Vera, María Isabel; Barrera, Doris; Hernández, Carlos; Molina, Ángel Valentín; Martínez, Luis Javier; Salazar MSc, Juan; Gelvez, Elkin; Contreras, Yudith; Sáenz, Frank
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%.

Examinando por Autor "Valbuena, Oscar"

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