Examinando por Autor "Molina, V"
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Ítem Brain hematoma computational segmentation(IOP Publishing, 2018) Sáenz, F; Vera, M; Huerfano, Y; Molina, V; Martinez, L; Vera, M I; Salazar, W; Gelvez, E; Salazar, J; Valbuena, O; Robles, H; Bautista, M; Arango, JIn computed tomography imaging, brain hematoma (BH) segmentation is a very challenging process due to a high variability of BH morphology, low contrast and noisy images. Because of this, BH segmentation is an open problem. In order to approach this problem, we propose an automatic technique, named nonlinear technique (NLT), based on a thresholding method, noise suppression filters, intelligent operators, a clustering strategy and a binary morphological operator. NLT performance is assessed by Jaccard's similarity index (JSI) used to compare automatic and manual BH segmentations. This assessment allows developing a tuning process for establishing the optimal parameters of each of the algorithms which constitute the proposed technique. The results indicate a good correlation, based on JSI, between the manual segmentations and the automatic ones. Finally, the BH volume is generated considering the automatic segmentation. This volume indicates whether or not the patient must undergo a surgical intervention for BH treatment.Ítem Left atrial appendage automatic segmentation, in computed tomography images(IOP Publishing, 2019) Huérfano, Y; Vera, M; Vera, M I; Valbuena, O; Gelvez-Almeida, E; Salazar-Torres, J; Molina, VThe left atrial appendage is one of the anatomical places where most frequently blood thrombi occur. When migrating from the appendage, these thrombi, become blood emboli that, potentially, can compromise the physiology and/or anatomy of cardiac or cerebral blood vessels, being able to generate cerebrovascular events. The left atrial appendage segmentation is very difficult due, mainly, to its location and the identical densitometric information presents into of this appendage and around of the left atrium. In this paper, an automatic technique is proposed to segment this appendage with the purpose of generating important information to the procedure called left atrial appendage surgical closure. This information is linked to the volume and the diameters of the left atrial appendage. The technique consists of a digital pre-processing stage, based on filtering processes and definition of a region of interest and, of one segmentation stage that considers a clustering method. The results are promising and they allow us to calculate useful quantitative variables when characterizing the most lethal appendix of the human body represented by the mentioned appendage. These results are very important in clinical processes where both the shape and volume of this appendage are vital for detecting and monitoring some vascular diseases such as cardiac embolism, arterial hypertension and stroke, among others.