Desarrollo de un algoritmo de visión artificial: un enfoque a la identificación y evaluación temprana de heridas de pie diabético

Diabetic foot is one of the most devastating consequences related to diabetes. It is relevant because of its incidence and the elevated percentage of amputations and deaths that the disease implies. Given the fact that the existing tests and laboratories designed to diagnose it are limited and expensive, the most common evaluation is still based on signs and symptoms. This means that the specialist completes a questionnaire based solely on observation and an invasive wound measurement. Using the questionnaire, the physician issues a diagnose. In this sense, the diagnose relies only on the criteria and the specialists’ experience. For some variables such as the lesions area or their location, this dependency is not acceptable. Currently bioengineering has played a key role on the diagnose of different chronic degenerative diseases. A timely diagnose has proven to be the best tool against diabetic foot. The diabetic’s foot clinical evaluation increases the possibility to identify risks and further complications. The main goal of this paper is to present the development of an algorithm based on digital image processing techniques, which enables to optimize the results on the diabetic’s foot lesion evaluation. Using advanced techniques for object segmentation and adjusting the sensibility parameter, allows the correlation between the algorithm’s identified wounds and those observed by the physician. Using the developed algorithm it is possible to identify and asses the wounds, their size, location, in a noninvasive way.El pie diabético es una de las complicaciones más devastadoras de la diabetes. Su trascendencia es significativa por su alta incidencia y por el elevado porcentaje de amputaciones y decesos que implica. Dado que las pruebas de laboratorio para su diagnóstico son limitadas y costosas, la evaluación típica sigue basándose en signos y síntomas. Esto es, el médico llena un cuestionario basado en la medición de sus instrumentos de apoyo y su propia observación. Con base en tal cuestionario emite un diagnóstico. En el sentido de lo anterior, se tiene un diagnóstico dependiente del criterio y experiencia del evaluador. Para algunas variables, como el área de las lesiones o la ubicación de éstas, esta dependencia no es aceptable. El presente trabajo pretende, mediante la introducción de técnicas de procesamiento de imágenes digitales, convertirse en un primer eslabón para la optimización de los resultados en la evaluación del pie diabético. La aplicación del algoritmo sobre un grupo de imágenes de prueba dio resultados aceptables en la detección de las heridas, así como su tamaño y ubicación, gracias al empleo de técnicas avanzadas de segmentación de objetos y al parámetro que permite ajustar la sensibilidad del sistema hasta obtener los resultados deseados. La aportación de esta tesis es un sistema de evaluación de lesiones del pie diabético que, sin duda, puede ser una herramienta muy útil para el especialista que permite la detección automática de las lesiones y la extracción de sus características de forma no invasiva, además de facilitar el manejo de los datos de forma digital

Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Edge detection in Musculoskeletal Ultrasound Imaging readily allows an ultrasound image to be rendered as a binary image. This facilitates automated measurement of geometric parameters, such as muscle thickness, circumference and cross-sectional area of the tendon. In this work, we introduced a new method of edge detection based on a fuzzy inference system and apply it to the ultrasound image. An anisotropic diffusion filter was used to reduce speckle noise before implementation of the edge detection method, which consists of three characteristic steps. The first step entailed fuzzification, for which three fuzzy membership functions were applied to the image. The parameters of these functions were selected based on an analysis of the standard deviation of grey level intensities in the image. Secondly, 12 fuzzy rules for identifying edges were constructed. Thirdly, defuzzification was carried out using the Takagi-Sugeno method. Furthermore, a reference-based edge measurement was quantitatively determined by comparing edge characteristics with a standard reference. We made two inferences from our observations. Firstly, the ability to automatically identify the important details of a musculoskeletal ultrasound image in a very short time is possible. Secondly, this method is effective compared with other methods

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging