Segmentación de imágenes cerebrales de Resonancia Magnética basada en Redes Neuronales de Regresión Generalizada / Segmentation of Magnetic Resonance images of the brain based on Generalized Regression Neural Networks

El análisis de los cambios estructurales del cerebro a través de imágenes de Resonancia Magnética puede proveer información útil para el diagnóstico y el manejo clínico de los pacientes con demencia. Si bien el grado de sofisticación alcanzado por el equipamiento de Resonancia Magnética es alto, la cuantificación de estructuras y tejidos aún no ha sido completamente solucionada. Las segmentaciones que estos equipos permiten en la actualidad fracasan en aquellas estructuras donde los bordes no están claramente definidos. En este trabajo se presenta un método de segmentación automática de imágenes de Resonancia Magnética cerebrales basada en la utilización de Redes Neuronales de Regresión Generalizada utilizando algoritmos genéticos para el ajuste de los parámetros. La red se entrena a partir de una sola imagen y clasifica al resto de ellas siempre que las imágenes de Resonancia Magnética hayan sido adquiridas con el mismo protocolo. Un método de medición de la atrofia progresiva y sus posibles cambios frente a un efecto terapéutico debe ser fundamentalmente automático y por lo tanto independiente del radiólogo(AU)

The analysis of structural changes in the brain through Magnetic Resonance Images may provide useful information for the diagnosis and clinical management of patients with dementia. While the degree of sophistication achieved by the MRI equipment is high, the quantification of structures and tissues has not been completely solved. The segmentations that these equipment provide nowadays, fail on those structures where the edges are not clearly defined. This paper presents a method for automatic segmentation of magnetic resonance images of the brain, based on the use of generalized regression neural networks using genetic algorithms for adjusting parameters. The network is trained from a single image and classifies rest of them whenever magnetic resonance images were acquired with the same protocol. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist(AU)

METODOLOGÍA PARA DIMENSIONAMIENTO DE MECANISMO POLICÉNTRICO DE RODILLA UTILIZANDO ANÁLISIS DE MARCHA Y ALGORITMOS GENÉTICOS / METHODOLOGY TO GAUGE A FOUR-BAR LINKAGE PROSTHETIC KNEE MECHANISM BASED ON GAIT ANALYSIS AND GENETIC ALGORITHMS

El objetivo de esta investigación es desarrollar una metodología para dimensionar un mecanismo policéntrico de rodilla de 4 barras para máxima estabilidad. Basado en el hecho de que la estabilidad del mecanismo durante la respuesta a la carga depende de la posición del centro instantáneo de rotación (CIR) respecto la fuerza de reacción del piso (FRP) durante la fase de apoyo, se desarrolló una plataforma de cómputo que representa el movimiento real de la pierna, el vector FRP y el mecanismo con su CIR. Para obtener los datos de entrada a la plataforma, se realizó un análisis de marcha a una paciente con amputación transfemoral unilateral, obteniendo la FRP, el ángulo de flexo-extensión de rodilla y la cinemática de los miembros inferiores. Por otra parte, a través de los algoritmos genéticos (AGs), se obtienen las dimensiones y configuración de los eslabones del mecanismo requeridas para iterar con la plataforma en la cual, comparando la ubicación de la FRP respecto al CIR en el plano sagital, se determinan las dimensiones funcionales adecuadas. El mecanismo se dimensionó exitosamente utilizando la metodología desarrollada, garantizando estabilidad de la rodilla después del contacto inicial y flexión voluntaria antes del despegue de punta.

This research was aimed to develop a methodology for establishing the proper dimensions of a four-bar linkage prosthetic knee mechanism for maximum stability. Based on the fact that the stability of a four-bar knee during load-bearing is determined by the location of the instantaneous center of rotation (ICR) with respect to the ground reaction force (GRF) vector, a computational platform was developed to simulate the movement of the leg, the GRF vector and the position of the ICR of the mechanism. On one hand, a gait analysis was carried out on a subject with unilateral transfemoral amputation, from which the GRF, the knee flexion-extension angle and the kinematics of the lower limbs were determined. On the other hand, genetic algorithms (GAs) technique provided the dimensions and mechanism links configuration required to iterate with the platform on which, comparing the location of the GRF and the ICR in the sagittal plane, the functional dimensions of the mechanism were obtained. The polycentric knee mechanism was gauged successfully by ensuring knee stability during the initial contact and load response as well as the ability to initiate voluntary flexion toward late stance before the toe-off.

Algoritmos Genéticos aplicados a la Ingeniería biomédica / Genetic algorithms applied to Biomedical Engineering

En este trabajo se describe las aplicaciones y alcances del método de los algoritmos genéticos (AG) en la investigación en bioingeniería, mecanobiología y medicina. Para este fin, se ha desarrollado el trabajo sobre tres artículos recientes que describen las aplicaciones de los AG en problemas de ingeniería biomédica. Este trabajo pone de manifiesto la importancia del uso de nuevas metodologías de optimización en las investigaciones biomédicas.

In present paper are described the applications and scope of the genetic algorithms method (GA) in the case of the research in the bioengineering, mechanobiology and medicine. For this aim, the paper on three recent articles was developed describing the applications of the GA in problems related to biomedical engineering. Present paper emphasizes the significance of the use of new methodologies of optimization in the biomedical researches.

Linear versus non-linear noninvasive temperature prediction in a homogeneous medium subjected to physiotherapeutic ultrasound / Comparação entre predição linear e não linear não-invasiva de temperatura num meio homogêneo sujeito a ultra-som de terapia

The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information(inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.

A falta de estimadores de temperatura espaço-temporais que sejam precisos impede a aplicação segura das terapias térmicas, como por exemplo a hipertermia. Neste artigo é apresentada uma comparação entre uma classe de modelos lineares e uma classe de modelos não lineares, na predição não invasiva de temperatura num meio homogêneo, quando o mesmo é aquecido por ultra-som em níveis usados em fisioterapia. Os modelos lineares considerados foram do tipo auto-regressivo com entradas exógenas (ARX); a nível não-linear foram considerados redes neuronais RBF (RBFNN). Para treinar e validar os modelos foram recolhidas os ecos provenientes do meio, bem como os correspondentes valores de temperatura. Após a colheita de informação, foram extraídas características dos ecos medidos e posteriormente os modelos foram treinados e validados. Para ambas as classes de modelos, as melhores estruturas foram seleccionadas usando um algoritmo genético multi-objectivo (MOGA), devido ao número elevado de estruturas possíveis. O melhor modelo RBFNN apresentou um erro máximo absoluto cinco vezes inferior ao erro máximo absoluto apresentado pelo melhor modelo ARX. Neste trabalho, o melhor modelo RBFNN apresentou um erro máximo absolutode 0,42 ºC, valor este que é inferior ao limite (0,5 ºC) apresentado como sendo a fronteira entre um estimador desejado e um estimador indesejado. O erro médio cometido pelo melhor modelo neuronal é uma ordem de grandeza inferior ao erro médio apresentado pelo melhor modelo linear, obtendo-se deste modo uma estimação menos enviesada no caso das redes neuronais, com menos informação do ambiente (menos entradas) devido ao processamento não-linear dos dados de entrada. Os resultados obtidos são encorajadores, apontando no sentido de se obter bons resultados numa estimação espaço-temporal.

Ultrassom de terapia / Ultrasound therapy

Temperature modeling of human tissue subjected to ultrasound for therapeutic use is essential for an accurate instrumental assessment and calibration. Prior studies with a homogeneous medium are hereby reported. Nonlinear punctual temperature modeling is proposed by means of Radial Basis Functions Neural Network (RBFNN) structures...