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

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Extraction of Pest Insect Characteristics Present in a Mirasol Pepper (Capsicum annuum L.) Crop by Digital Image Processing

One of the main problems in crops is the presence of pests. Traditionally, sticky yellow traps are used to detect pest insects, and they are then analyzed by a specialist to identify the pest insects present in the crop. To facilitate the identification, classification, and counting of these insects, it is possible to use digital image processing (DIP). This study aims to demonstrate that DIP is useful for extracting invariant characteristics of psyllids (Bactericera cockerelli), thrips (Thrips tabaci), whiteflies (Bemisia tabaci), potato flea beetles (Epitrix cucumeris), pepper weevils (Anthonomus eugenii), and aphids (Myzus persicae). The characteristics (e.g., area, eccentricity, and solidity) help classify insects. DIP includes a first stage that consists of improving the image by changing the levels of color intensity, applying morphological filters, and detecting objects of interest, and a second stage that consists of applying a transformation of invariant scales to extract characteristics of insects, independently of size or orientation. The results were compared with the data obtained from an entomologist, reaching up to 90% precision for the classification of these insects