Estimación de la orientación múltiple mediante un banco de filtros y su uso en el desarrollo de aplicaciones de procesado de imagen

Mención Europeo / Mención Internacional: Concedido.[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. En las últimas décadas, la estimación de orientación se ha convertido en una tarea clave del procesado de imagen, dada su capacidad para extraer características de bajo nivel y su aplicación en el análisis de datos. Existen un gran número de aplicaciones donde la estimación de orientación juega un papel fundamental como son: el análisis de huellas dactilares, extracción de puntos característicos, bifurcaciones, esquinas o intersecciones, filtrado adaptativo o seguimiento de objetos, entre otras. Sin embargo, con el paso del tiempo han aparecido diferentes problemas asociados a la estimación de orientación que pueden complicar este proceso. Los más importantes a destacar son los siguientes: las limitaciones que presentan muchos de los métodos de estimación en estructuras complejas, por ejemplo, estructuras con varias orientaciones asociadas, el incremento de la complejidad computacional de los métodos más modernos o la dependencia de éstos a solo unas determinadas aplicaciones. Resulta en estos momentos, por tanto, una tarea clave conseguir métodos de estimación que sean lo más globales y genéricos posibles, en otras palabras, lo más independientes del tipo de imagen con la que se trabaje y del campo de aplicación. En esta Tesis doctoral, en primer lugar, se aborda una revisión de los conceptos más importantes de la estimación de orientación, como es el concepto de estructura, orientación y sus propiedades principales. También se describen los métodos de estimación de orientaciones más importantes: tensor estructural, bancos de filtros, gradiente al cuadrado promediado, etc. Y las aplicaciones más importantes como la detección de texturas, extracción de características, análisis de huellas dactilares, filtrado variante o seguimiento de objetos, entre otras. Las contribuciones principales a esta Tesis son dos. En primer lugar, la propuesta de un marco de trabajo (de estimación de orientaciones) capaz de sistematizar el proceso de estimación de orientaciones, independientemente del tipo de estructuras o el tipo de aplicación. El marco propuesto está basado en una de las técnicas de estimación de orientación más usadas, los bancos de filtros. Durante este trabajo, éstos han sido probados en multitud de escenarios mientras se consideraban diferentes familias de filtros para su aplicación. En segundo lugar, se abordan casos prácticos de aplicación del marco de trabajo propuesto con el objetivo de mostrar sus excelentes capacidades en aplicaciones muy dispares, mostrando su potencial en multitud de posibilidades. Dado que el método de presentación de la presente Tesis doctoral es mediante un compendio de artículos, la organización de esta memoria constará de un primer capítulo de introducción y estado del arte. Seguidamente se mostrarán, de forma coherente y organizada, los artículos con los resultados obtenidos durante el periodo de investigación de la Tesis, con una introducción para cada uno de los artículos incluidos en este compendio. Finalmente, el capítulo de conclusiones y trabajo futuro cierra la Tesis.[ENG] This doctoral dissertation has been presented in the form of thesis by publication. In the last decades, image orientation estimation has become in a fundamental task of image processing, due to its ability to extract low level features and its application to data analysis. There are a wide number of applications where the image orientation estimation plays and important role, some of these are: fingerprint analysis, feature extractions such as bifurcation, junction and corner, adaptive filtering or tracking applications. However, with the pass of time, different problems related to orientation estimation have appeared and they can complicate this process. The most important problems to highlight are: difficult of a wide number of methods to estimate the orientation of complex object structures, for example, structures with multiple orientations associated, high computational cost of modern methods or dependence on the application framework. Therefore, nowadays, the obtention of global and generics methods, in other words, methods as independent as possible from the image and the application, has become in a important task. In this Thesis, firstly, a review of main concepts of image orientation have been carried out, such as the concept of structure, orientation and their main properties. The most important methods have been described, as e.g., structural tensor, bank of filters, average square gradient, etc. And the most important applications based on image orientation estimation as texture analysis, feature extraction, fingerprint analysis, object tracking and space variant filtering, among others. The main contributions to this Thesis are two. First one is the proposal of a new framework for image orientation estimation, which can systematize this process, making it independent of image type and application. The proposed framework is based on one of the most used estimation orientation techniques, the bank of filters. Throughout this work, it have been tested in a wide variety of scenarios, considering different families of filters for their application. Secondly, the proposed framework has been evaluated in practical applications to show its ability and potential. This Thesis has been carried out by the method of compendium of publications, it has been organized as follows. Chapter one shows an introduction and a review of the state of art. Chapter two shows the journal papers and other contributions done during the research period of this Thesis. Finally, Chapter three shows the conclusion and future work.El trabajo de esta Tesis ha estado financiado parcialmente por el Ministerio de Economía, Industria y Competitividad (Proyecto PI17/00771) y la Universidad Politécnica de Valencia - Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano. Labhuman- conjuntamente con la Universidad Politécnica de Cartagena (Proyectos 4106/15TIC y 3626/13TIC).Los artículos y capítulos de libros que forman la tesis son los siguientes: Artículo 1: A.G. Legaz-Aparicio, R. Verdú-Monedero, J. Angulo, “Multiscale Estimation of Multiple Orientations based on Morphological Directional Openings”, Signal, Image and Video Processing, 2018, Accepted, (doi:10.1007/s11760-018-1276-y). ISI-JCR(2017): 1.643, Posición 163 de 260 (T2, Q3), cat ENGINEERING, ELECTRICAL & ELECTRONIC. Artículo 2: Álvar-Ginés Legaz-Aparicio, Rafael Verdú-Monedero, Juan Morales-Sanchez, Jorge Larrey- Ruiz, Jesús Angulo, “Detection of Retinal Vessel Bifurcation by Means of Multiple Orientation Estimation Based on Regularized Morphological Openings”. XIII Medierranean Confe-rence on Medical and Biological Engineering and Computing, Sevilla, 2013. Artículo 3: S. Morales, Á. Legaz-Aparicio, V. Naranjo, R. Verdú-Monedero, “Determination of Bifurcation Angles of the Retinal Vascular Tree through Multiple Orientation Estimation ba-sed on Regularized Morphological Openings”, International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2015), Lisbon (Portugal), January 2015. Artículo 4: S. Morales, V. Naranjo, J. Angulo, A.G. Legaz-Aparicio, R. Verdú-Monedero, “Retinal network characterization through fundus image processing: signicant point identication on vessel centerline”, Signal Processing: Image Communication, Vol. 59, pp. 50-64, November 2017. ISI-JCR(2017): 2.073, Posición 118 de 260 (T2, Q2), cat ENGINEERING, ELECTRICAL & ELEC-TRONIC. Artículo 5: A.G. Legaz-Aparicio, R. Verdú-Monedero, K. Engan, “Noise Robust and Ro-tation Invariant Framework for Texture Analysis and Classification”, Applied Mathematics and Computation, Volume 335, pp. 124 a 132, October 2018. ISI-JCR(2017): 2.300, Posición 21 de 252 (T1, Q1), cat MATHEMATICS, APPLIED. Artículo 6: Álvar-Ginés Legaz-Aparicio, Rafael Verdú-Monedero, Jesús Angulo, “Adaptive spatially variant morphological filters based on a multiple orientation vector field”, Mathematical modelling in Engineering & Human Behaviour 2016. Artículo 7: A.G. Legaz-Aparicio, R. Verdú-Monedero, J. Angulo, “Adaptive morphological filters based on a multiple orientation vector field dependent on image local features”, Journal of Computational and Applied Mathematics, Vol. 330, pp. 965-981, March 2018. ISI-JCR(2017): 1.632, Posición 49 de 252 (T1, Q1), cat MATHEMATICS, APPLIED.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Politécnica de Cartagen

Aprendizaje máquina aplicado a la segmentación de imágenes ecográficas de la arteria carótida para la medida del grosor íntima-media

[SPA] Las enfermedades cardiovasculares son la principal causa de mortalidad, morbilidad y discapacidad a nivel mundial. Gran parte de estas patologías derivan de la aterosclerosis, una enfermedad que afecta a las arterias de mediano y gran calibre provocando su endurecimiento y pérdida de elasticidad. La aterosclerosis se caracteriza por el engrosamiento de la capa más interna de las paredes arteriales debido al depósito de materia grasa, colesterol y otras sustancias. Por tanto, produce un estrechamiento del lumen arterial dificultando el flujo sanguíneo normal. A largo plazo, puede llevar a una oclusión total del vaso afectado, impidiendo la llegada de oxígeno a la zona irrigada y provocando accidentes cardiovasculares severos. Así, es crucial el diagnóstico precoz de la aterosclerosis con fines preventivos. En este sentido, el grosor íntima-media o IMT (Intima-Media Thickness) de la arteria carótida común se considera un marcador precoz y fiable de la aterosclerosis y, por tanto, del riesgo cardiovascular. Las paredes de los vasos sanguíneos están formadas por tres capas, de la más interna a la más externa: íntima, media y adventicia. El IMT se define como la distancia entre las interfaces lumen-íntima y media-adventicia y es evaluado mediante imágenes ecográficas que muestran un corte longitudinal de la arteria carótida común. Esta modalidad de imagen es no-invasiva para el paciente y relativamente económica, aunque suele ser bastante ruidosa y muy dependiente del operador. Además, el IMT se suele evaluar de forma manual, marcando pares de puntos sobre la imagen. Estos aspectos dan un carácter subjetivo a la medida del IMT y afectan a su reproducibilidad. La motivación de esta Tesis Doctoral es la mejora del proceso de evaluación del IMT sobre ecografías de la arteria carótida común. El objetivo fundamental consiste en explorar y proponer diferentes soluciones basadas en técnicas de Aprendizaje Máquina adecuadas para la segmentación de estas imágenes. De esta forma, se pretende detectar las interfaces lumen-íntima y media-adventicia a nivel de la pared posterior del vaso para medir el IMT sin necesidad de la interacción con el usuario. Este hecho implica que las estrategias propuestas resulten adecuadas tanto para el diagnóstico en la práctica clínica diaria como para facilitar el desarrollo de estudios sobre un gran número de imágenes. En particular, el proceso de evaluación del IMT se lleva a cabo en tres etapas completamente automáticas. En la primera etapa se realiza un pre-procesado de las ecografías para detectar la región de interés, es decir, la pared posterior de la arteria carótida común. Seguidamente, se procede a la identificación de las interfaces que definen el IMT. Por último, una etapa de post-procesado depura los resultados y define los contornos finales sobre los que realizar la medida del IMT. Para la detección automática de la región de interés (ROI) se han estudiado dos propuestas diferentes: una basada en Morfología Matemática y otra basada en Aprendizaje Máquina. Sobre la ROI detectada, la segmentación de las interfaces lumen-íntima y media-adventicia se plantea como un problema de Reconocimiento de Patrones, a resolver mediante técnicas de Aprendizaje Máquina. Así, se han estudiado cuatro configuraciones diferentes, utilizando distintos algoritmos de entrenamiento, arquitecturas, representaciones de los datos de entrada y definiciones del espacio de salida. Por tanto, la segmentación se reduce a realizar una clasificación de los píxeles de la ecografía. El post-procesado ha sido adaptado a cada una de las estrategias de segmentación propuestas para detectar y eliminar los posibles errores de clasificación de forma automática. Una parte importante del estudio realizado se dedica a la validación de las técnicas de segmentación desarrolladas. Para ello, se ha utilizado un conjunto de 79 ecografías adquiridas con el mismo equipo de ultrasonidos, pero utilizando diferentes sondas y con diferentes resoluciones espaciales. Además, se ha realizado la segmentación manual de todas las imágenes por parte de dos expertos diferentes. Considerando como ground-truth el promedio de cuatro segmentaciones manuales, dos de cada experto, se han evaluado los errores de segmentación de las estrategias automáticas planteadas. El proceso de validación se completa con la comparación de las medidas automáticas y manuales del IMT. Para la evaluación de los resultados, se han empleado diagramas de cajas, análisis de regresión lineal, diagramas de Bland-Altman y diferentes parámetros estadísticos. Los procedimientos desarrollados han demostrado ser robustos frente al ruido y artefactos que puedan presentar las ecografías. También se adaptan a la variabilidad anatómica e instrumental de las imágenes, lográndose una segmentación correcta con independencia de la apariencia que muestre la arteria en la imagen. Los errores medios obtenidos son similares, o incluso inferiores, a los errores propios de otros métodos automáticos y semiautomáticos encontrados en la literatura. Además, como consecuencia de utilizar máquinas de aprendizaje, el proceso de segmentación destaca por su eficiencia computacional. [ENG] Cardiovascular diseases are the leading cause of mortality, morbidity and disability worldwide. Large proportion of these diseases results from atherosclerosis, an illness that affects arterial blood vessels causing the hardening and loss of elasticity of the walls of arteries. Atherosclerosis is characterized by the thickening of the innermost layer of the arterial walls due to the accumulation of fatty material, cholesterol and other substances. Therefore, it produces a narrowing of the arterial lumen which hinders the normal blood flow. In the long term, it can lead to an entire occlusion of the affected vessel, preventing the flow of oxygen to the irrigated area and causing severe cardiovascular accidents. Thus, an early diagnosis of atherosclerosis is crucial for preventive purposes. In this sense, the intima-media thickness (IMT) of the common carotid artery is an early and reliable indicator of atherosclerosis and, therefore, of the cardiovascular risk. The walls of blood vessels consist of three layers, from the innermost to the outermost: intima, media and adventitia. The IMT is defined as the distance between the lumen-intima and media-adventitia interfaces and it is assessed by means of ultrasound images showing longitudinal cuts of the common carotid artery. This imaging modality is noninvasive and relatively low-cost, although it tends to be quite noisy and highly operator dependent. Usually, IMT is manually measured by the specialist, who marks pairs of points on the image. These aspects give a subjective character to the IMT measurement and affect its reproducibility. The motivation of this Ph.D. Thesis is the improvement of the evaluation process of IMT in ultrasound images of the common carotid artery. The main objective is the exploration and the proposition of different solutions based on Machine Learning for segmenting these images. In this way, it is intended to detect the lumen-intima and media-adventitia interfaces in the posterior wall of the vessel to measure the IMT without user interaction. This means that the proposed strategies are suitable both for the diagnosis in daily clinical practice and to facilitate the development of studies with a large number of images. In particular, the evaluation process of IMT is carried out in three fully automatic stages. The first stage is a pre-processing of the ultrasound image in which the region of interest (ROI), i.e. the far-wall of the common carotid artery, is detected. Then, it proceeds to the identification of the interfaces defining the IMT. Finally, a post-processing stage debugs the results and defines the final contours on which IMT is evaluated. Two different proposals have been studied for the ROI detection: one based on Mathematical Morphology and the other based on Machine Learning. Once the ROI is detected, the segmentation of the lumen-intima interface and the media-adventitia interface is posed as a Pattern Recognition problem and it is solved by Machine Learning techniques. Thus, four different configurations have been developed by using distinct architectures, training algorithms, representations of input information and output space definitions. Therefore, segmentation is reduced to perform a classification of the pixels belonging to the ROI. The post-processing stage has been adapted to each one of the proposed segmentation strategies to detect and eliminate possible misclassifications in an automatic way. An important part of the present study is dedicated to the validation of the developed techniques. For this purpose, 79 images acquired with the same ultrasound equipment, but using different probes and different spatial resolutions, have been used. Two experts have performed the manual segmentation of all the images. Considering as ground-truth the average of four manual segmentations, two from each expert, the segmentation errors of the four different strategies have been evaluated. The validation process is completed with the comparison between automatic and manual IMT measurements. For an exhaustive characterization of the results, box plots, linear regression analysis, Bland-Altman plots and different statistical parameters have been used. Developed procedures have proven to be robust against noise and artifacts that may appear in the ultrasounds. They also adapt themselves to the anatomical and instrumental variability of the images, achieving a correct segmentation regardless of the appearance of the artery in the ultrasound. The obtained mean errors are similar, or even lower, than errors in other automatic and semi-automatic methods. Moreover, as a result of using learning machines, the segmentation process stands out for its computational efficiency.[ENG] Cardiovascular diseases are the leading cause of mortality, morbidity and disability worldwide. Large proportion of these diseases results from atherosclerosis, an illness that affects arterial blood vessels causing the hardening and loss of elasticity of the walls of arteries. Atherosclerosis is characterized by the thickening of the innermost layer of the arterial walls due to the accumulation of fatty material, cholesterol and other substances. Therefore, it produces a narrowing of the arterial lumen which hinders the normal blood flow. In the long term, it can lead to an entire occlusion of the affected vessel, preventing the flow of oxygen to the irrigated area and causing severe cardiovascular accidents. Thus, an early diagnosis of atherosclerosis is crucial for preventive purposes. In this sense, the intima-media thickness (IMT) of the common carotid artery is an early and reliable indicator of atherosclerosis and, therefore, of the cardiovascular risk. The walls of blood vessels consist of three layers, from the innermost to the outermost: intima, media and adventitia. The IMT is defined as the distance between the lumen-intima and media-adventitia interfaces and it is assessed by means of ultrasound images showing longitudinal cuts of the common carotid artery. This imaging modality is noninvasive and relatively low-cost, although it tends to be quite noisy and highly operator dependent. Usually, IMT is manually measured by the specialist, who marks pairs of points on the image. These aspects give a subjective character to the IMT measurement and affect its reproducibility. The motivation of this Ph.D. Thesis is the improvement of the evaluation process of IMT in ultrasound images of the common carotid artery. The main objective is the exploration and the proposition of different solutions based on Machine Learning for segmenting these images. In this way, it is intended to detect the lumen-intima and media-adventitia interfaces in the posterior wall of the vessel to measure the IMT without user interaction. This means that the proposed strategies are suitable both for the diagnosis in daily clinical practice and to facilitate the development of studies with a large number of images. In particular, the evaluation process of IMT is carried out in three fully automatic stages. The first stage is a pre-processing of the ultrasound image in which the region of interest (ROI), i.e. the far-wall of the common carotid artery, is detected. Then, it proceeds to the identification of the interfaces defining the IMT. Finally, a post-processing stage debugs the results and defines the final contours on which IMT is evaluated. Two different proposals have been studied for the ROI detection: one based on Mathematical Morphology and the other based on Machine Learning. Once the ROI is detected, the segmentation of the lumen-intima interface and the media-adventitia interface is posed as a Pattern Recognition problem and it is solved by Machine Learning techniques. Thus, four different configurations have been developed by using distinct architectures, training algorithms, representations of input information and output space definitions. Therefore, segmentation is reduced to perform a classification of the pixels belonging to the ROI. The post-processing stage has been adapted to each one of the proposed segmentation strategies to detect and eliminate possible misclassifications in an automatic way. An important part of the present study is dedicated to the validation of the developed techniques. For this purpose, 79 images acquired with the same ultrasound equipment, but using different probes and different spatial resolutions, have been used. Two experts have performed the manual segmentation of all the images. Considering as ground-truth the average of four manual segmentations, two from each expert, the segmentation errors of the four different strategies have been evaluated. The validation process is completed with the comparison between automatic and manual IMT measurements. For an exhaustive characterization of the results, box plots, linear regression analysis, Bland-Altman plots and different statistical parameters have been used. Developed procedures have proven to be robust against noise and artifacts that may appear in the ultrasounds. They also adapt themselves to the anatomical and instrumental variability of the images, achieving a correct segmentation regardless of the appearance of the artery in the ultrasound. The obtained mean errors are similar, or even lower, than errors in other automatic and semi-automatic methods. Moreover, as a result of using learning machines, the segmentation process stands out for its computational efficiency.Programa de doctorado en Tecnologías de la Información y Comunicacione

Análise de desempenho de métricas de grafos para reconhecimento de tarefas de imaginação motora das mãos a partir de dados de eletroencefalografia

Orientadores: Gabriela Castellano, Romis Ribeiro de Faissol AttuxDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Interfaces cérebro-computador (BCIs, brain-computer interfaces) são sistemas cuja finalidade é fornecer um canal de comunicação direto entre o cérebro e um dispositivo externo, como um computador, uma prótese ou uma cadeira de rodas. Por não utilizarem as vias fisiológicas convencionais, BCIs podem constituir importantes tecnologias assistivas para pessoas que sofreram algum tipo de lesão e, por isso, tiveram sua interação com o ambiente externo comprometida. Os sinais cerebrais a serem extraídos para utilização nestes sistemas devem ser gerados mediante estratégias específicas. Nesta dissertação, trabalhamos com a estratégia de imaginação motora (MI, motor imagery), e extraímos a resposta cerebral correspondente a partir de dados de eletroencefalografia (EEG). Os objetivos do trabalho foram caracterizar as redes cerebrais funcionais oriundas das tarefas de MI das mãos e explorar a viabilidade de utilizar métricas da teoria de grafos para a classificação dos padrões mentais, gerados por esta estratégia, de usuários de um sistema BCI. Para isto, fez-se a hipótese de que as alterações no espectro de frequências dos sinais de eletroencefalografia devidas à MI das mãos deveria, de alguma forma, se refletir nos grafos construídos para representar as interações cerebrais corticais durante estas tarefas. Em termos de classificação, diferentes conjuntos de pares de eletrodos foram testados, assim como diferentes classificadores (análise de discriminantes lineares ¿ LDA, máquina de vetores de suporte ¿ SVM ¿ linear e polinomial). Os três classificadores testados tiveram desempenho similar na maioria dos casos. A taxa média de classificação para todos os voluntários considerando a melhor combinação de eletrodos e classificador foi de 78%, sendo que alguns voluntários tiveram taxas de acerto individuais de até 92%. Ainda assim, a metodologia empregada até o momento possui várias limitações, sendo a principal como encontrar os pares ótimos de eletrodos, que variam entre voluntários e aquisições; além do problema da realização online da análiseAbstract: Brain-computer interfaces (BCIs) are systems that aim to provide a direct communication channel between the brain and an external device, such as a computer, a prosthesis or a wheelchair. Since BCIs do not use the conventional physiological pathways, they can constitute important assistive technologies for people with lesions that compromised their interaction with the external environment. Brain signals to be extracted for these systems must be generated according to specific strategies. In this dissertation, we worked with the motor imagery (MI) strategy, and we extracted the corresponding cerebral response from electroencephalography (EEG) data. Our goals were to characterize the functional brain networks originating from hands¿ MI and investigate the feasibility of using metrics from graph theory for the classification of mental patterns, generated by this strategy, of BCI users. We hypothesized that frequency alterations in the EEG spectra due to MI should reflect themselves, in some manner, in the graphs representing cortical interactions during these tasks. For data classification, different sets of electrode pairs were tested, as well as different classifiers (linear discriminant analysis ¿ LDA, and both linear and polynomial support vector machines ¿ SVMs). All three classifiers tested performed similarly in most cases. The mean classification rate over subjects, considering the best electrode set and classifier, was 78%, while some subjects achieved individual hit rates of up to 92%. Still, the employed methodology has yet some limitations, being the main one how to find the optimum electrode pairs¿ sets, which vary among subjects and among acquisitions; in addition to the problem of performing an online analysisMestradoFísicaMestre em Física165742/2014-31423625/2014CNPQCAPE