MÉTODO ADAPTIVO DE DESCRIPCIÓN DE TEXTURA UTILIZANDO EL PATRÓN ESPECTRUM Y LA MORFOLOGÍA MATEMÁTICA

La morfología matemática ha sido utilizada en diferentes tareas de procesamiento de imágenes, como el filtrado y en la descripción de la textura, usando el método denominado patrón espectrum. En este artículo se propone un descriptor adaptivo de textura basado en el patrón espectrum. El elemento estructurante usado permite realizar operaciones para procesar el patrón espectrum en varias formas y tamaños, por medio de un criterio de distancia, el cual se adapta a la superficie de la textura alrededor de cada pixel. Los resultados de la clasificación de textura dependen del tamaño deldescriptor y su elemento estructurante, logrando que el método adaptivo de patrón espectrum mejore en un 10% la tasa de acierto al compararlo con el método tradicional

Quantitative assessment of damage during MCET: a parametric study in a rodent model

Abstract Background Myocardial cavitation-enabled therapy (MCET) has been proposed as a means to achieve minimally invasive myocardial reduction using ultrasound to produce scattered microlesions by cavitating contrast agent microbubbles. Methods Rats were treated using burst mode focused ultrasound at 1.5 MHz center frequency and varying envelope and pressure amplitudes. Evans blue staining indicated lethal cardiomyocytic injury. A previously developed quantitative scheme, evaluating the histologic treatment results, provides an insightful analysis for MCET treatment parameters. Such include ultrasound exposure amplitude and pulse modulation, contrast agent dose, and infusion rate. Results The quantitative method overcomes the limitation of visual scoring and works for a large dynamic range of treatment impact. Macrolesions are generated as an accumulation of probability driven microlesion formations. Macrolesions grow radially with radii from 0.1 to 1.6 mm as the ultrasound exposure amplitude (peak negative) increases from 2 to 4 MPa. To shorten treatment time, a swept beam was investigated and found to generate an acceptable macrolesion volume of about 40 μL for a single beam position. Conclusions Ultrasound parameters and administration of microbubbles directly influence lesion characteristics such as microlesion density and macrolesion dimension. For lesion generation planning, control of MCET is crucial, especially when targeting larger pre-clinical models.http://deepblue.lib.umich.edu/bitstream/2027.42/115462/1/40349_2015_Article_39.pd

Segmentación mediante modelos deformables aplicada a imágenes 3D de ultrasonido intravascular

En este trabajo se presenta una estrategia que integra la segmentación de imágenes tridimensionales basada en modelos deformables y la posterior reconstrucción de mallas de superficie, la cual se aplica a la reconstrucción de las paredes arteriales a partir de series de imágenes de ultrasonido intravascular. El algoritmo contempla el tratamiento del ruido inherente a este tipo de imágenes y, dada la similitud entre los cortes del estudio, la deformación de cada contorno utiliza información sobre la segmentación en la imagen previa de la secuencia. En base a los contornos individuales detectados se reconstruyen modelos de superficie asociados a la estructura arterial y se proveen facilidades de visualización 3D. Las pruebas realizadas muestran que el método permite obtener reconstrucciones de la sección arterial de alta calidad y con bajo costo computacionalSociedad Argentina de Informática e Investigación Operativ

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

Vessel tractography using an intensity based tensor model

In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores