Discernment of bee pollen loads using computer vision and one-class classification techniques

In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types. Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

Implementation techniques for evolvable HW systems: virtual vs. dynamic reconfiguration

Adaptive hardware requires some reconfiguration capabilities. FPGAs with native dynamic partial reconfiguration (DPR) support pose a dilemma for system designers: whether to use native DPR or to build a virtual reconfigurable circuit (VRC) on top of the FPGA which allows selecting alternative functions by a multiplexing scheme. This solution allows much faster reconfiguration, but with higher resource overhead. This paper discusses the advantages of both implementations for a 2D image processing matrix. Results show how higher operating frequency is obtained for the matrix using DPR. However, this is compensated in the VRC during evolution due to the comparatively negligible reconfiguration time. Regarding area, the DPR implementation consumes slightly more resources due to the reconfiguration engine, but adds further more capabilities to the system.

Image characterisation based on the mammalian visual cortex

A proposal for a model of the primary visual cortex is reported. It is structured with the basis of a simple unit cell able to perform fourteen pairs of different boolean functions corresponding to the two possible inputs. As a first step, a model of the retina is presented. Different types of responses, according to the different possibilities of interconnecting the building blocks, have been obtained. These responses constitute the basis for an initial configuration of the mammalian primary visual cortex. Some qualitative functions, as symmetry or size of an optical input, have been obtained. A proposal to extend this model to some higher functions, concludes the paper.

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

A novel FPGA-based evolvable hardware system based on multiple processing arrays

In this paper, an architecture based on a scalable and flexible set of Evolvable Processing arrays is presented. FPGA-native Dynamic Partial Reconfiguration (DPR) is used for evolution, which is done intrinsically, letting the system to adapt autonomously to variable run-time conditions, including the presence of transient and permanent faults. The architecture supports different modes of operation, namely: independent, parallel, cascaded or bypass mode. These modes of operation can be used during evolution time or during normal operation. The evolvability of the architecture is combined with fault-tolerance techniques, to enhance the platform with self-healing features, making it suitable for applications which require both high adaptability and reliability. Experimental results show that such a system may benefit from accelerated evolution times, increased performance and improved dependability, mainly by increasing fault tolerance for transient and permanent faults, as well as providing some fault identification possibilities. The evolvable HW array shown is tailored for window-based image processing applications.

Procesado de imágenes médicas en MATLAB

La relación entre la ingeniería y la medicina cada vez se está haciendo más estrecha, y debido a esto se ha creado una nueva disciplina, la bioingeniería, ámbito en el que se centra el proyecto. Este ámbito cobra gran interés debido al rápido desarrollo de nuevas tecnologías que en particular permiten, facilitan y mejoran la obtención de diagnósticos médicos respecto de los métodos tradicionales. Dentro de la bioingeniería, el campo que está teniendo mayor desarrollo es el de la imagen médica, gracias al cual se pueden obtener imágenes del interior del cuerpo humano con métodos no invasivos y sin necesidad de recurrir a la cirugía. Mediante métodos como la resonancia magnética, rayos X, medicina nuclear o ultrasonidos, se pueden obtener imágenes del cuerpo humano para realizar diagnósticos. Para que esas imágenes puedan ser utilizadas con ese fin hay que realizar un correcto tratamiento de éstas mediante técnicas de procesado digital. En ése ámbito del procesado digital de las imágenes médicas es en el que se ha realizado este proyecto. Gracias al desarrollo del tratamiento digital de imágenes con métodos de extracción de información, mejora de la visualización o resaltado de rasgos de interés de las imágenes, se puede facilitar y mejorar el diagnóstico de los especialistas. Por todo esto en una época en la que se quieren automatizar todos los procesos para mejorar la eficacia del trabajo realizado, el automatizar el procesado de las imágenes para extraer información con mayor facilidad, es muy útil. Actualmente una de las herramientas más potentes en el tratamiento de imágenes médicas es Matlab, gracias a su toolbox de procesado de imágenes. Por ello se eligió este software para el desarrollo de la parte práctica de este proyecto, su potencia y versatilidad simplifican la implementación de algoritmos. Este proyecto se estructura en dos partes. En la primera se realiza una descripción general de las diferentes modalidades de obtención de imágenes médicas y se explican los diferentes usos de cada método, dependiendo del campo de aplicación. Posteriormente se hace una descripción de las técnicas más importantes de procesado de imagen digital que han sido utilizadas en el proyecto. En la segunda parte se desarrollan cuatro aplicaciones en Matlab para ejemplificar el desarrollo de algoritmos de procesado de imágenes médicas. Dichas implementaciones demuestran la aplicación y utilidad de los conceptos explicados anteriormente en la parte teórica, como la segmentación y operaciones de filtrado espacial de la imagen, así como otros conceptos específicos. Las aplicaciones ejemplo desarrolladas han sido: obtención del porcentaje de metástasis de un tejido, diagnóstico de las deformidades de la columna vertebral, obtención de la MTF de una cámara de rayos gamma y medida del área de un fibroadenoma de una ecografía de mama. Por último, para cada una de las aplicaciones se detallará su utilidad en el campo de la imagen médica, los resultados obtenidos y su implementación en una interfaz gráfica para facilitar su uso. ABSTRACT. The relationship between medicine and engineering is becoming closer than ever giving birth to a recently appeared science field: bioengineering. This project is focused on this subject. This recent field is becoming more and more important due to the fast development of new technologies that provide tools to improve disease diagnosis, with regard to traditional procedures. In bioengineering the fastest growing field is medical imaging, in which we can obtain images of the inside of the human body without need of surgery. Nowadays by means of the medical modalities of magnetic resonance, X ray, nuclear medicine or ultrasound, we can obtain images to make a more accurate diagnosis. For those images to be useful within the medical field, they should be processed properly with some digital image processing techniques. It is in this field of digital medical image processing where this project is developed. Thanks to the development of digital image processing providing methods for data collection, improved visualization or data highlighting, diagnosis can be eased and facilitated. In an age where automation of processes is much sought, automated digital image processing to ease data collection is extremely useful. One of the most powerful image processing tools is Matlab, together with its image processing toolbox. That is the reason why that software was chosen to develop the practical algorithms in this project. This final project is divided into two main parts. Firstly, the different modalities for obtaining medical images will be described. The different usages of each method according to the application will also be specified. Afterwards we will give a brief description of the most important image processing tools that have been used in the project. Secondly, four algorithms in Matlab are implemented, to provide practical examples of medical image processing algorithms. This implementation shows the usefulness of the concepts previously explained in the first part, such as: segmentation or spatial filtering. The particular applications examples that have been developed are: calculation of the metastasis percentage of a tissue, diagnosis of spinal deformity, approximation to the MTF of a gamma camera, and measurement of the area of a fibroadenoma in an ultrasound image. Finally, for each of the applications developed, we will detail its usefulness within the medical field, the results obtained, and its implementation in a graphical user interface to ensure ease of use.

Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

A nivel mundial, el cáncer de mama es el tipo de cáncer más frecuente además de una de las principales causas de muerte entre la población femenina. Actualmente, el método más eficaz para detectar lesiones mamarias en una etapa temprana es la mamografía. Ésta contribuye decisivamente al diagnóstico precoz de esta enfermedad que, si se detecta a tiempo, tiene una probabilidad de curación muy alta. Uno de los principales y más frecuentes hallazgos en una mamografía, son las microcalcificaciones, las cuales son consideradas como un indicador importante de cáncer de mama. En el momento de analizar las mamografías, factores como la capacidad de visualización, la fatiga o la experiencia profesional del especialista radiólogo hacen que el riesgo de omitir ciertas lesiones presentes se vea incrementado. Para disminuir dicho riesgo es importante contar con diferentes alternativas como por ejemplo, una segunda opinión por otro especialista o un doble análisis por el mismo. En la primera opción se eleva el coste y en ambas se prolonga el tiempo del diagnóstico. Esto supone una gran motivación para el desarrollo de sistemas de apoyo o asistencia en la toma de decisiones. En este trabajo de tesis se propone, se desarrolla y se justifica un sistema capaz de detectar microcalcificaciones en regiones de interés extraídas de mamografías digitalizadas, para contribuir a la detección temprana del cáncer demama. Dicho sistema estará basado en técnicas de procesamiento de imagen digital, de reconocimiento de patrones y de inteligencia artificial. Para su desarrollo, se tienen en cuenta las siguientes consideraciones: 1. Con el objetivo de entrenar y probar el sistema propuesto, se creará una base de datos de imágenes, las cuales pertenecen a regiones de interés extraídas de mamografías digitalizadas. 2. Se propone la aplicación de la transformada Top-Hat, una técnica de procesamiento digital de imagen basada en operaciones de morfología matemática. La finalidad de aplicar esta técnica es la de mejorar el contraste entre las microcalcificaciones y el tejido presente en la imagen. 3. Se propone un algoritmo novel llamado sub-segmentación, el cual está basado en técnicas de reconocimiento de patrones aplicando un algoritmo de agrupamiento no supervisado, el PFCM (Possibilistic Fuzzy c-Means). El objetivo es encontrar las regiones correspondientes a las microcalcificaciones y diferenciarlas del tejido sano. Además, con la finalidad de mostrar las ventajas y desventajas del algoritmo propuesto, éste es comparado con dos algoritmos del mismo tipo: el k-means y el FCM (Fuzzy c-Means). Por otro lado, es importante destacar que en este trabajo por primera vez la sub-segmentación es utilizada para detectar regiones pertenecientes a microcalcificaciones en imágenes de mamografía. 4. Finalmente, se propone el uso de un clasificador basado en una red neuronal artificial, específicamente un MLP (Multi-layer Perceptron). El propósito del clasificador es discriminar de manera binaria los patrones creados a partir de la intensidad de niveles de gris de la imagen original. Dicha clasificación distingue entre microcalcificación y tejido sano. ABSTRACT Breast cancer is one of the leading causes of women mortality in the world and its early detection continues being a key piece to improve the prognosis and survival. Currently, the most reliable and practical method for early detection of breast cancer is mammography.The presence of microcalcifications has been considered as a very important indicator ofmalignant types of breast cancer and its detection and classification are important to prevent and treat the disease. However, the detection and classification of microcalcifications continue being a hard work due to that, in mammograms there is a poor contrast between microcalcifications and the tissue around them. Factors such as visualization, tiredness or insufficient experience of the specialist increase the risk of omit some present lesions. To reduce this risk, is important to have alternatives such as a second opinion or a double analysis for the same specialist. In the first option, the cost increases and diagnosis time also increases for both of them. This is the reason why there is a great motivation for development of help systems or assistance in the decision making process. This work presents, develops and justifies a system for the detection of microcalcifications in regions of interest extracted fromdigitizedmammographies to contribute to the early detection of breast cancer. This systemis based on image processing techniques, pattern recognition and artificial intelligence. For system development the following features are considered: With the aim of training and testing the system, an images database is created, belonging to a region of interest extracted from digitized mammograms. The application of the top-hat transformis proposed. This image processing technique is based on mathematical morphology operations. The aim of this technique is to improve the contrast betweenmicrocalcifications and tissue present in the image. A novel algorithm called sub-segmentation is proposed. The sub-segmentation is based on pattern recognition techniques applying a non-supervised clustering algorithm known as Possibilistic Fuzzy c-Means (PFCM). The aim is to find regions corresponding to the microcalcifications and distinguish them from the healthy tissue. Furthermore,with the aim of showing themain advantages and disadvantages this is compared with two algorithms of same type: the k-means and the fuzzy c-means (FCM). On the other hand, it is important to highlight in this work for the first time the sub-segmentation is used for microcalcifications detection. Finally, a classifier based on an artificial neural network such as Multi-layer Perceptron is used. The purpose of this classifier is to discriminate froma binary perspective the patterns built from gray level intensity of the original image. This classification distinguishes between microcalcifications and healthy tissue.

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms.

La ingeniería al servicio de la historia: imágenes invisibles del paleolítico.

El presente artículo pretende describir el desarrollo de una nueva metodología no invasiva de documentación digital de petroglifos y pinturas rupestres pertenecientes al paleolítico, a través de técnicas y herramientas del tratamiento digital de imágenes para optimizar materiales y tiempos en la obtención de información gráfica, representativa y de precisión. Abstract: This article aims to describe the development of a new non-invasive methodology, through techniques and tools of digital image processing to optimize materials and time in obtaining graphical representative and accurate information from petroglyphs and rock paintings belonging to Paleolithic.

Dust tracking techniques applied to the STARDUST facility: First results

An important issue related to future nuclear fusion reactors fueled with deuterium and tritium is the creation of large amounts of dust due to several mechanisms (disruptions, ELMs and VDEs). The dust size expected in nuclear fusion experiments (such as ITER) is in the order of microns (between 0.1 and 1000 μm). Almost the total amount of this dust remains in the vacuum vessel (VV). This radiological dust can re-suspend in case of LOVA (loss of vacuum accident) and these phenomena can cause explosions and serious damages to the health of the operators and to the integrity of the device. The authors have developed a facility, STARDUST, in order to reproduce the thermo fluid-dynamic conditions comparable to those expected inside the VV of the next generation of experiments such as ITER in case of LOVA. The dust used inside the STARDUST facility presents particle sizes and physical characteristics comparable with those that created inside the VV of nuclear fusion experiments. In this facility an experimental campaign has been conducted with the purpose of tracking the dust re-suspended at low pressurization rates (comparable to those expected in case of LOVA in ITER and suggested by the General Safety and Security Report ITER-GSSR) using a fast camera with a frame rate from 1000 to 10,000 images per second. The velocity fields of the mobilized dust are derived from the imaging of a two-dimensional slice of the flow illuminated by optically adapted laser beam. The aim of this work is to demonstrate the possibility of dust tracking by means of image processing with the objective of determining the velocity field values of dust re-suspended during a LOVA.

Methodology of image analysis for study of the vertisols moisture content

The main problem to study vertical drainage from the moisture distribution, on a vertisol profile, is searching for suitable methods using these procedures. Our aim was to design a digital image processing methodology and its analysis to characterize the moisture content distribution of a vertisol profile. In this research, twelve soil pits were excavated on a ba re Mazic Pellic Vertisols ix of them in May 13/2011 and the rest in May 19 /2011 after a moderate rainfall event. Digital RGB images were taken from each vertisol pit using a Kodak? camera selecting a size of 1600x945 pixels. Each soil image was processed to homogenized brightness and then a spatial filter with several window sizes was applied to select the optimum one. The RGB image obtained were divided in each matrix color selecting the best thresholds for each one, maximum and minimum, to be applied and get a digital binary pattern. This one was analyzed by estimating two fractal scaling exponents box counting dimension D BC) and interface fractal dimension (D) In addition, three pre-fractal scaling coefficients were determinate at maximum resolution: total number of boxes intercepting the foreground pattern (A), fractal lacunarity (?1) and Shannon entropy S1). For all the images processed the spatial filter 9x9 was the optimum based on entropy, cluster and histogram criteria. Thresholds for each color were selected based on bimodal histograms.

Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

Advanced morphometric techniques applied to the study of human brain anatomy

El desarrollo de las técnicas de imágenes por resonancia magnética han permitido el estudio y cuantificación, in vivo, de los cambios que ocurren en la morfología cerebral ligados a procesos tales como el neurodesarrollo, el envejecimiento, el aprendizaje o la enfermedad. Un gran número de métodos de morfometría han sido desarrollados con el fin de extraer la información contenida en estas imágenes y traducirla en indicadores de forma o tamaño, tales como el volumen o el grosor cortical; marcadores que son posteriormente empleados para encontrar diferencias estadísticas entre poblaciones de sujetos o realizar correlaciones entre la morfología cerebral y, por ejemplo, la edad o la severidad de determinada enfermedad. A pesar de la amplia variedad de biomarcadores y metodologías de morfometría, muchos estudios sesgan sus hipótesis, y con ello los resultados experimentales, al empleo de un número reducido de biomarcadores o a al uso de una única metodología de procesamiento. Con el presente trabajo se pretende demostrar la importancia del empleo de diversos métodos de morfometría para lograr una mejor caracterización del proceso que se desea estudiar. En el mismo se emplea el análisis de forma para detectar diferencias, tanto globales como locales, en la morfología del tálamo entre pacientes adolescentes con episodios tempranos de psicosis y adolescentes sanos. Los resultados obtenidos demuestran que la diferencia de volumen talámico entre ambas poblaciones de sujetos, previamente descrita en la literatura, se debe a una reducción del volumen de la región anterior-mediodorsal y del núcleo pulvinar del tálamo de los pacientes respecto a los sujetos sanos. Además, se describe el desarrollo de un estudio longitudinal, en sujetos sanos, que emplea simultáneamente distintos biomarcadores para la caracterización y cuantificación de los cambios que ocurren en la morfología de la corteza cerebral durante la adolescencia. A través de este estudio se revela que el proceso de “alisado” que experimenta la corteza cerebral durante la adolescencia es consecuencia de una disminución de la profundidad, ligada a un incremento en el ancho, de los surcos corticales. Finalmente, esta metodología es aplicada, en un diseño transversal, para el estudio de las causas que provocan el decrecimiento tanto del grosor cortical como del índice de girificación en adolescentes con episodios tempranos de psicosis. ABSTRACT The ever evolving sophistication of magnetic resonance image techniques continue to provide new tools to characterize and quantify, in vivo, brain morphologic changes related to neurodevelopment, senescence, learning or disease. The majority of morphometric methods extract shape or size descriptors such as volume, surface area, and cortical thickness from the MRI image. These morphological measurements are commonly entered in statistical analytic approaches for testing between-group differences or for correlations between the morphological measurement and other variables such as age, sex, or disease severity. A wide variety of morphological biomarkers are reported in the literature. Despite this wide range of potentially useful biomarkers and available morphometric methods, the hypotheses and findings of the grand majority of morphological studies are biased because reports assess only one morphometric feature and usually use only one image processing method. Throughout this dissertation biomarkers and image processing strategies are combined to provide innovative and useful morphometric tools for examining brain changes during neurodevelopment. Specifically, a shape analysis technique allowing for a fine-grained assessment of regional thalamic volume in early-onset psychosis patients and healthy comparison subjects is implemented. Results show that disease-related reductions in global thalamic volume, as previously described by other authors, could be particularly driven by a deficit in the anterior-mediodorsal and pulvinar thalamic regions in patients relative to healthy subjects. Furthermore, in healthy adolescents different cortical features are extracted and combined and their interdependency is assessed over time. This study attempts to extend current knowledge of normal brain development, specifically the largely unexplored relationship between changes of distinct cortical morphological measurements during adolescence. This study demonstrates that cortical flattening, present during adolescence, is produced by a combination of age-related increase in sulcal width and decrease in sulcal depth. Finally, this methodology is applied to a cross-sectional study, investigating the mechanisms underlying the decrease in cortical thickness and gyrification observed in psychotic patients with a disease onset during adolescence.

Methods for the analysis of multi-scale cell dynamics from fluorescence microscopy images

La embriogénesis es el proceso mediante el cual una célula se convierte en un ser un vivo. A lo largo de diferentes etapas de desarrollo, la población de células va proliferando a la vez que el embrión va tomando forma y se configura. Esto es posible gracias a la acción de varios procesos genéticos, bioquímicos y mecánicos que interaccionan y se regulan entre ellos formando un sistema complejo que se organiza a diferentes escalas espaciales y temporales. Este proceso ocurre de manera robusta y reproducible, pero también con cierta variabilidad que permite la diversidad de individuos de una misma especie. La aparición de la microscopía de fluorescencia, posible gracias a proteínas fluorescentes que pueden ser adheridas a las cadenas de expresión de las células, y los avances en la física óptica de los microscopios han permitido observar este proceso de embriogénesis in-vivo y generar secuencias de imágenes tridimensionales de alta resolución espacio-temporal. Estas imágenes permiten el estudio de los procesos de desarrollo embrionario con técnicas de análisis de imagen y de datos, reconstruyendo dichos procesos para crear la representación de un embrión digital. Una de las más actuales problemáticas en este campo es entender los procesos mecánicos, de manera aislada y en interacción con otros factores como la expresión genética, para que el embrión se desarrolle. Debido a la complejidad de estos procesos, estos problemas se afrontan mediante diferentes técnicas y escalas específicas donde, a través de experimentos, pueden hacerse y confrontarse hipótesis, obteniendo conclusiones sobre el funcionamiento de los mecanismos estudiados. Esta tesis doctoral se ha enfocado sobre esta problemática intentando mejorar las metodologías del estado del arte y con un objetivo específico: estudiar patrones de deformación que emergen del movimiento organizado de las células durante diferentes estados del desarrollo del embrión, de manera global o en tejidos concretos. Estudios se han centrado en la mecánica en relación con procesos de señalización o interacciones a nivel celular o de tejido. En este trabajo, se propone un esquema para generalizar el estudio del movimiento y las interacciones mecánicas que se desprenden del mismo a diferentes escalas espaciales y temporales. Esto permitiría no sólo estudios locales, si no estudios sistemáticos de las escalas de interacción mecánica dentro de un embrión. Por tanto, el esquema propuesto obvia las causas de generación de movimiento (fuerzas) y se centra en la cuantificación de la cinemática (deformación y esfuerzos) a partir de imágenes de forma no invasiva. Hoy en día las dificultades experimentales y metodológicas y la complejidad de los sistemas biológicos impiden una descripción mecánica completa de manera sistemática. Sin embargo, patrones de deformación muestran el resultado de diferentes factores mecánicos en interacción con otros elementos dando lugar a una organización mecánica, necesaria para el desarrollo, que puede ser cuantificado a partir de la metodología propuesta en esta tesis. La metodología asume un medio continuo descrito de forma Lagrangiana (en función de las trayectorias de puntos materiales que se mueven en el sistema en lugar de puntos espaciales) de la dinámica del movimiento, estimado a partir de las imágenes mediante métodos de seguimiento de células o de técnicas de registro de imagen. Gracias a este esquema es posible describir la deformación instantánea y acumulada respecto a un estado inicial para cualquier dominio del embrión. La aplicación de esta metodología a imágenes 3D + t del pez zebra sirvió para desvelar estructuras mecánicas que tienden a estabilizarse a lo largo del tiempo en dicho embrión, y que se organizan a una escala semejante al del mapa de diferenciación celular y con indicios de correlación con patrones de expresión genética. También se aplicó la metodología al estudio del tejido amnioserosa de la Drosophila (mosca de la fruta) durante el cierre dorsal, obteniendo indicios de un acoplamiento entre escalas subcelulares, celulares y supracelulares, que genera patrones complejos en respuesta a la fuerza generada por los esqueletos de acto-myosina. En definitiva, esta tesis doctoral propone una estrategia novedosa de análisis de la dinámica celular multi-escala que permite cuantificar patrones de manera inmediata y que además ofrece una representación que reconstruye la evolución de los procesos como los ven las células, en lugar de como son observados desde el microscopio. Esta metodología por tanto permite nuevas formas de análisis y comparación de embriones y tejidos durante la embriogénesis a partir de imágenes in-vivo. ABSTRACT The embryogenesis is the process from which a single cell turns into a living organism. Through several stages of development, the cell population proliferates at the same time the embryo shapes and the organs develop gaining their functionality. This is possible through genetic, biochemical and mechanical factors that are involved in a complex interaction of processes organized in different levels and in different spatio-temporal scales. The embryogenesis, through this complexity, develops in a robust and reproducible way, but allowing variability that makes possible the diversity of living specimens. The advances in physics of microscopes and the appearance of fluorescent proteins that can be attached to expression chains, reporting about structural and functional elements of the cell, have enabled for the in-vivo observation of embryogenesis. The imaging process results in sequences of high spatio-temporal resolution 3D+time data of the embryogenesis as a digital representation of the embryos that can be further analyzed, provided new image processing and data analysis techniques are developed. One of the most relevant and challenging lines of research in the field is the quantification of the mechanical factors and processes involved in the shaping process of the embryo and their interactions with other embryogenesis factors such as genetics. Due to the complexity of the processes, studies have focused on specific problems and scales controlled in the experiments, posing and testing hypothesis to gain new biological insight. However, methodologies are often difficult to be exported to study other biological phenomena or specimens. This PhD Thesis is framed within this paradigm of research and tries to propose a systematic methodology to quantify the emergent deformation patterns from the motion estimated in in-vivo images of embryogenesis. Thanks to this strategy it would be possible to quantify not only local mechanisms, but to discover and characterize the scales of mechanical organization within the embryo. The framework focuses on the quantification of the motion kinematics (deformation and strains), neglecting the causes of the motion (forces), from images in a non-invasive way. Experimental and methodological challenges hamper the quantification of exerted forces and the mechanical properties of tissues. However, a descriptive framework of deformation patterns provides valuable insight about the organization and scales of the mechanical interactions, along the embryo development. Such a characterization would help to improve mechanical models and progressively understand the complexity of embryogenesis. This framework relies on a Lagrangian representation of the cell dynamics system based on the trajectories of points moving along the deformation. This approach of analysis enables the reconstruction of the mechanical patterning as experienced by the cells and tissues. Thus, we can build temporal profiles of deformation along stages of development, comprising both the instantaneous events and the cumulative deformation history. The application of this framework to 3D + time data of zebrafish embryogenesis allowed us to discover mechanical profiles that stabilized through time forming structures that organize in a scale comparable to the map of cell differentiation (fate map), and also suggesting correlation with genetic patterns. The framework was also applied to the analysis of the amnioserosa tissue in the drosophila’s dorsal closure, revealing that the oscillatory contraction triggered by the acto-myosin network organized complexly coupling different scales: local force generation foci, cellular morphology control mechanisms and tissue geometrical constraints. In summary, this PhD Thesis proposes a theoretical framework for the analysis of multi-scale cell dynamics that enables to quantify automatically mechanical patterns and also offers a new representation of the embryo dynamics as experienced by cells instead of how the microscope captures instantaneously the processes. Therefore, this framework enables for new strategies of quantitative analysis and comparison between embryos and tissues during embryogenesis from in-vivo images.

Desarrollo de una herramienta para analizar métodos de reconocimiento de emociones

Desde hace más de 20 años, muchos grupos de investigación trabajan en el estudio de técnicas de reconocimiento automático de expresiones faciales. En los últimos años, gracias al avance de las metodologías, ha habido numerosos avances que hacen posible una rápida detección de las caras presentes en una imagen y proporcionan algoritmos de clasificación de expresiones. En este proyecto se realiza un estudio sobre el estado del arte en reconocimiento automático de emociones, para conocer los diversos métodos que existen en el análisis facial y en el reconocimiento de la emoción. Con el fin de poder comparar estos métodos y otros futuros, se implementa una herramienta modular y ampliable y que además integra un método de extracción de características que consiste en la obtención de puntos de interés en la cara y dos métodos para clasificar la expresión, uno mediante comparación de desplazamientos de los puntos faciales, y otro mediante detección de movimientos específicos llamados unidades de acción. Para el entrenamiento del sistema y la posterior evaluación del mismo, se emplean las bases de datos Cohn-Kanade+ y JAFFE, de libre acceso a la comunidad científica. Después, una evaluación de estos métodos es llevada a cabo usando diferentes parámetros, bases de datos y variando el número de emociones. Finalmente, se extraen conclusiones del trabajo y su evaluación, proponiendo las mejoras necesarias e investigación futura. ABSTRACT. Currently, many research teams focus on the study of techniques for automatic facial expression recognition. Due to the appearance of digital image processing, in recent years there have been many advances in the field of face detection, feature extraction and expression classification. In this project, a study of the state of the art on automatic emotion recognition is performed to know the different methods existing in facial feature extraction and emotion recognition. To compare these methods, a user friendly tool is implemented. Besides, a feature extraction method is developed which consists in obtaining 19 facial feature points. Those are passed to two expression classifier methods, one based on point displacements, and one based on the recognition of facial Action Units. Cohn-Kanade+ and JAFFE databases, both freely available to the scientific community, are used for system training and evaluation. Then, an evaluation of the methods is performed with different parameters, databases and varying the number of emotions. Finally, conclusions of the work and its evaluation are extracted, proposing some necessary improvements and future research.