Examinando por Autor "Molina, V."
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Ítem A computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography images(IOP Publishing, 2019) Huerfano, Y.; Vera, M.; Gelvez, E.; Salazar, J.; Del Mar, A.; Valbuena, O.; Molina, V.The objective of the work is to propose a computational strategy to identify lung squamous cell carcinoma in three-dimensional databases (3D) of multislice computerized tomography. This strategy consists of the pre-processing, segmentation, and post-processing stages. During pre-processing, an anisotropic, gradient-based diffusion algorithm and a filter bank are used to address artifact and image noise issues. During segmentation, the technique called region growing is applied to pre-processed images. Finally, in the post-processing, a morphological dilation filter is used to process the segmented images. In order to make value judgments about the performance of the proposed strategy, the relative percentage error is used to compare the dilated segmentations of the squamous cell carcinoma with the segmentations of the squamous cell carcinoma generated, manually, by a pulmonologist. The combination of parameters linked to the highest PrE, allows establishing the optimal parameters of each of the algorithms that make up the proposed strategy.Ítem Computational strategy for the segmentation of the aortic annulus in cardiac computed tomography images(IOP Publishing, 2019) Valbuena, O.; Vera, M.; Huérfano, Y.; Gelvez, E.; Salazar, J.; Molina, V.; Sáenz, F.; Vera, M I.; Salazar, W.The purpose of this research is to segment the aortic annulus, present in cardiac computed tomography images, from a computational strategy generated using global similarity enhancement, vector least squares support machines and a segmentation technique named region growing. This enhancement is obtained by applying the following steps: a) Obtain an image of similarity by calculating the absolute value of the arithmetic subtraction that considers the original image and an image of contours. The image of contours is obtained by processing the original image with a filter based on the magnitude of the gradient. b) Is is processed with a Gaussian filter, generating a smoothed similarity image. On the other hand, considering the smoothed similarity image, the least squares support machines are used both to construct two cutting planes (that isolate the aortic artery) and to detect the coordinates of a seed voxel. In order to produce the morphology of the aortic valve annulus, the seed voxel initializes the technique of growth of regions during the segmentation process. From this morphology, some useful quantitative descriptors are calculated for the aortic ring characterization.Ítem Isotropic versus anisotropic techniques in cardiac computed tomography images processing(IOP Publishing, 2019) Barrera, D.; Valbuena, O.; Vera, M.; Huérfano, Y.; Gelvez, E.; Salazar, J.; Molina, V.; Sáenz, F.; Vera, M I.; Salazar, W.The objective of the work is to compare the performance of two filters, one isotropic and another one of anisotropic diffusion based on gradient. To do this, experiments are carried out to establish which of the filters exhibits a better behavior against the imperfections that characterize the computed tomography images. The structure of the experiments is as follows: a) The parameters linked to the aforementioned filters are identified. b) The ranges of valuesof these parameters and the way to use them are established. c) A database of three-dimensional cardiac images is filtered by applying, independently, the aforementioned filters considering a pre-established subset of values associated with the parameters. d) All the filtered images are addressed by a segmentation process, based on the growth of regions, which allows extracting the 3D morphology of the thoracic external aorta. e) As a metric to evaluate the performance of each technique, the Jaccard similarity index (JSI) is used. f) The one that generates the lowest calculated JSI is selected as the best technique when comparing a reference segmentation with all generated segmentations. The results indicate that the anisotropic diffusion filter, based on a gradient, obtained the best performance.Ítem Segmentation of brain tumors using a semi-automatic computational strategy(IOP Publishing, 2019) Vera, M.; Huérfano, Y.; Gelvez, E.; Valbuena, O.; Salazar, J.; Molina, V.; Vera, M I.; Salazar, W.; Sáenz, F.In this work, a semi-automatic computational strategy is proposed for brain tumor segmentation. The filtering (erosion + gaussian filters), segmentation (level set technique) and quantification (BT volume) stages are applied to magnetic resonance imaging in order to generate the three-dimensional morphology of brain tumors. The Jaccard's Similarity Index is considered to contrast manual segmentation with semi-automatic segmentations of brain tumor. In this sense, the highest Jaccard's Similarity Index provides the best parameters of the techniques that constitute the semi-automatic computational strategy. Results are promising, showing an excellent correlation between these segmentations. The volume is used for the brain tumors characterization.