Classical segmentation methods on novel MR imaging: a study of brain tissue segmentation of MP2RAGE vs MPRAGE

MP2RAGE has proven to be a bias-free MR acquisition with excellent contrast between grey and white matter. We investigated the ability of three state-of-the-art algorithms to automatically extract white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) from MPRAGE and MP2RAGE images: unified Segmentation (S) in SPM82 , its extension New Segment (NS), and an in-house Expectation-Maximization Markov Random Field tissue classification3 (EM-MRF) with Graph Cut (GC) optimization4 . Our goal is to quantify the differences between MPRAGE and MP2RAGE-based brain tissue probability maps.

Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.

MRI texture analysis as means for addressing rehydration and milk diffusion of cereals

Cereals microstructure is one of the primary quality attributes of cereals. Cereals rehydration and milk diffusion depends on such microstructure and thus, the crispiness and the texture, which will make it more palatable for the final consumer. Magnetic Resonance Imaging (MRI) is a very powerful topographic tool since acquisition parameter leads to a wide possibility for identifying textures, structures and liquids mobility. It is suited for non-invasive imaging of water and fats. Rehydration and diffusion cereals processes were measured by MRI at different times and using two different kinds of milk, varying their fat level. Several images were obtained. A combination of textural analysis (based on the analysis of histograms) and segmentation methods (in order to understand the rehydration level of each variety of cereals) were performed. According to the rehydration level, no advisable clustering behavior was found. Nevertheless, some differences were noticeable between the coating, the type of milk and the variety of cereals

Relationship between solar radiation on watercore on apple fruit assessed with MRI

This work is a preliminary studio of the possibility of assess a relationship between solar radiation and watercore development on apple fruit, during maturation, using a non destructive method such as Magnetic Resonance Imaging (MRI). For such purpose, several low cost solar radiation sensors were designed for the trial and placed at 2 different heights (1.5 and 2.5 m) on 6 adult ?Esperiega? apple trees, in a commercial orchard in Ademuz (Valencia). Sensors were connected along 27 days, during the end of the growth period and start of the fruit maturation process, and radiation measurements of the a-Si sensors were recorded every 1 minute. At the end of this period, fruits from the upper and the lower part of the canopy of each tree were harvested. In all, 152 apples were collected and images with MRI. A Principal Component Analysis, perfomed over the histograms of the images, as well as segmentation methods were performed on the MR images in order to find a pattern involving solar radiation and watercore incidence.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Methods and Techniques for Segmentation of Consumers in Social Media

Los medios sociales han revolucionado la manera en la que los consumidores se relacionan entre sí y con las marcas. Las opiniones publicadas en dichos medios tienen un poder de influencia en las decisiones de compra tan importante como las campañas de publicidad. En consecuencia, los profesionales del marketing cada vez dedican mayores esfuerzos e inversión a la obtención de indicadores que permitan medir el estado de salud de las marcas a partir de los contenidos digitales generados por sus consumidores. Dada la naturaleza no estructurada de los contenidos publicados en los medios sociales, la tecnología usada para procesar dichos contenidos ha menudo implementa técnicas de Inteligencia Artificial, tales como algoritmos de procesamiento de lenguaje natural, aprendizaje automático y análisis semántico. Esta tesis, contribuye al estado de la cuestión, con un modelo que permite estructurar e integrar la información publicada en medios sociales, y una serie de técnicas cuyos objetivos son la identificación de consumidores, así como la segmentación psicográfica y sociodemográfica de los mismos. La técnica de identificación de consumidores se basa en la huella digital de los dispositivos que utilizan para navegar por la Web y es tolerante a los cambios que se producen con frecuencia en dicha huella digital. Las técnicas de segmentación psicográfica descritas obtienen la posición en el embudo de compra de los consumidores y permiten clasificar las opiniones en función de una serie de atributos de marketing. Finalmente, las técnicas de segmentación sociodemográfica permiten obtener el lugar de residencia y el género de los consumidores. ABSTRACT Social media has revolutionised the way in which consumers relate to each other and with brands. The opinions published in social media have a power of influencing purchase decisions as important as advertising campaigns. Consequently, marketers are increasing efforts and investments for obtaining indicators to measure brand health from the digital content generated by consumers. Given the unstructured nature of social media contents, the technology used for processing such contents often implements Artificial Intelligence techniques, such as natural language processing, machine learning and semantic analysis algorithms. This thesis contributes to the State of the Art, with a model for structuring and integrating the information posted on social media, and a number of techniques whose objectives are the identification of consumers, as well as their socio-demographic and psychographic segmentation. The consumer identification technique is based on the fingerprint of the devices they use to surf the Web and is tolerant to the changes that occur frequently in such fingerprint. The psychographic profiling techniques described infer the position of consumer in the purchase funnel, and allow to classify the opinions based on a series of marketing attributes. Finally, the socio-demographic profiling techniques allow to obtain the residence and gender of consumers.

Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

Multimodal non-rigid image registration methods for therapy and surgery planning

La planificación pre-operatoria se ha convertido en una tarea esencial en cirugías y terapias de marcada complejidad, especialmente aquellas relacionadas con órgano blando. Un ejemplo donde la planificación preoperatoria tiene gran interés es la cirugía hepática. Dicha planificación comprende la detección e identificación precisa de las lesiones individuales y vasos así como la correcta segmentación y estimación volumétrica del hígado funcional. Este proceso es muy importante porque determina tanto si el paciente es un candidato adecuado para terapia quirúrgica como la definición del abordaje a seguir en el procedimiento. La radioterapia de órgano blando es un segundo ejemplo donde la planificación se requiere tanto para la radioterapia externa convencional como para la radioterapia intraoperatoria. La planificación comprende la segmentación de tumor y órganos vulnerables y la estimación de la dosimetría. La segmentación de hígado funcional y la estimación volumétrica para planificación de la cirugía se estiman habitualmente a partir de imágenes de tomografía computarizada (TC). De igual modo, en la planificación de radioterapia, los objetivos de la radiación se delinean normalmente sobre TC. Sin embargo, los avances en las tecnologías de imagen de resonancia magnética (RM) están ofreciendo progresivamente ventajas adicionales. Por ejemplo, se ha visto que el ratio de detección de metástasis hepáticas es significativamente superior en RM con contraste Gd–EOB–DTPA que en TC. Por tanto, recientes estudios han destacado la importancia de combinar la información de TC y RM para conseguir el mayor nivel posible de precisión en radioterapia y para facilitar una descripción precisa de las lesiones del hígado. Con el objetivo de mejorar la planificación preoperatoria en ambos escenarios se precisa claramente de un algoritmo de registro no rígido de imagen. Sin embargo, la gran mayoría de sistemas comerciales solo proporcionan métodos de registro rígido. Las medidas de intensidad de voxel han demostrado ser criterios de similitud de imágenes robustos, y, entre ellas, la Información Mutua (IM) es siempre la primera elegida en registros multimodales. Sin embargo, uno de los principales problemas de la IM es la ausencia de información espacial y la asunción de que las relaciones estadísticas entre las imágenes son homogéneas a lo largo de su domino completo. La hipótesis de esta tesis es que la incorporación de información espacial de órganos al proceso de registro puede mejorar la robustez y calidad del mismo, beneficiándose de la disponibilidad de las segmentaciones clínicas. En este trabajo, se propone y valida un esquema de registro multimodal no rígido 3D usando una nueva métrica llamada Información Mutua Centrada en el Órgano (Organ-Focused Mutual Information metric (OF-MI)) y se compara con la formulación clásica de la Información Mutua. Esto permite mejorar los resultados del registro en áreas problemáticas incorporando información regional al criterio de similitud, beneficiándose de la disponibilidad real de segmentaciones en protocolos estándares clínicos, y permitiendo que la dependencia estadística entre las dos modalidades de imagen difiera entre órganos o regiones. El método propuesto se ha aplicado al registro de TC y RM con contraste Gd–EOB–DTPA así como al registro de imágenes de TC y MR para planificación de radioterapia intraoperatoria rectal. Adicionalmente, se ha desarrollado un algoritmo de apoyo de segmentación 3D basado en Level-Sets para la incorporación de la información de órgano en el registro. El algoritmo de segmentación se ha diseñado específicamente para la estimación volumétrica de hígado sano funcional y ha demostrado un buen funcionamiento en un conjunto de imágenes de TC abdominales. Los resultados muestran una mejora estadísticamente significativa de OF-MI comparada con la Información Mutua clásica en las medidas de calidad de los registros; tanto con datos simulados (p<0.001) como con datos reales en registro hepático de TC y RM con contraste Gd– EOB–DTPA y en registro para planificación de radioterapia rectal usando OF-MI multi-órgano (p<0.05). Adicionalmente, OF-MI presenta resultados más estables con menor dispersión que la Información Mutua y un comportamiento más robusto con respecto a cambios en la relación señal-ruido y a la variación de parámetros. La métrica OF-MI propuesta en esta tesis presenta siempre igual o mayor precisión que la clásica Información Mutua y consecuentemente puede ser una muy buena alternativa en aplicaciones donde la robustez del método y la facilidad en la elección de parámetros sean particularmente importantes. Abstract Pre-operative planning has become an essential task in complex surgeries and therapies, especially for those affecting soft tissue. One example where soft tissue preoperative planning is of high interest is liver surgery. It involves the accurate detection and identification of individual liver lesions and vessels as well as the proper functional liver segmentation and volume estimation. This process is very important because it determines whether the patient is a suitable candidate for surgical therapy and the type of procedure. Soft tissue radiation therapy is a second example where planning is required for both conventional external and intraoperative radiotherapy. It involves the segmentation of the tumor target and vulnerable organs and the estimation of the planned dose. Functional liver segmentations and volume estimations for surgery planning are commonly estimated from computed tomography (CT) images. Similarly, in radiation therapy planning, targets to be irradiated and healthy and vulnerable tissues to be protected from irradiation are commonly delineated on CT scans. However, developments in magnetic resonance imaging (MRI) technology are progressively offering advantages. For instance, the hepatic metastasis detection rate has been found to be significantly higher in Gd–EOB–DTPAenhanced MRI than in CT. Therefore, recent studies highlight the importance of combining the information from CT and MRI to achieve the highest level of accuracy in radiotherapy and to facilitate accurate liver lesion description. In order to improve those two soft tissue pre operative planning scenarios, an accurate nonrigid image registration algorithm is clearly required. However, the vast majority of commercial systems only provide rigid registration. Voxel intensity measures have been shown to be robust measures of image similarity, and among them, Mutual Information (MI) is always the first candidate in multimodal registrations. However, one of the main drawbacks of Mutual Information is the absence of spatial information and the assumption that statistical relationships between images are the same over the whole domain of the image. The hypothesis of the present thesis is that incorporating spatial organ information into the registration process may improve the registration robustness and quality, taking advantage of the clinical segmentations availability. In this work, a multimodal nonrigid 3D registration framework using a new Organ- Focused Mutual Information metric (OF-MI) is proposed, validated and compared to the classical formulation of the Mutual Information (MI). It allows improving registration results in problematic areas by adding regional information into the similitude criterion taking advantage of actual segmentations availability in standard clinical protocols and allowing the statistical dependence between the two modalities differ among organs or regions. The proposed method is applied to CT and T1 weighted delayed Gd–EOB–DTPA-enhanced MRI registration as well as to register CT and MRI images in rectal intraoperative radiotherapy planning. Additionally, a 3D support segmentation algorithm based on Level-Sets has been developed for the incorporation of the organ information into the registration. The segmentation algorithm has been specifically designed for the healthy and functional liver volume estimation demonstrating good performance in a set of abdominal CT studies. Results show a statistical significant improvement of registration quality measures with OF-MI compared to MI with both simulated data (p<0.001) and real data in liver applications registering CT and Gd–EOB–DTPA-enhanced MRI and in registration for rectal radiotherapy planning using multi-organ OF-MI (p<0.05). Additionally, OF-MI presents more stable results with smaller dispersion than MI and a more robust behavior with respect to SNR changes and parameters variation. The proposed OF-MI always presents equal or better accuracy than the classical MI and consequently can be a very convenient alternative within applications where the robustness of the method and the facility to choose the parameters are particularly important.

Microstructural segmentation of 3D images of alsphalt specimen using Matlab engine

En este proyecto se ha desarrollado un código de MATLAB para el procesamiento de imágenes tomográficas 3D, de muestras de asfalto de carreteras en Polonia. Estas imágenes en 3D han sido tomadas por un equipo de investigación de la Universidad Tecnológica de Lodz (LUT). El objetivo de este proyecto es crear una herramienta que se pueda utilizar para estudiar las diferentes muestras de asfalto 3D y pueda servir para estudiar las pruebas de estrés que experimentan las muestras en el laboratorio. Con el objetivo final de encontrar soluciones a la degradación sufrida en las carreteras de Polonia, debido a diferentes causas, como son las condiciones meteorológicas. La degradación de las carreteras es un tema que se ha investigado desde hace muchos años, debido a la fuerte degradación causada por diferentes factores como son climáticos, la falta de mantenimiento o el tráfico excesivo en algunos casos. Es en Polonia, donde estos tres factores hacen que la composición de muchas carreteras se degrade rápidamente, sobre todo debido a las condiciones meteorológicas sufridas a lo largo del año, con temperaturas que van desde 30° C en verano a -20° C en invierno. Esto hace que la composición de las carreteras sufra mucho y el asfalto se levante, lo que aumenta los costos de mantenimiento y los accidentes de carretera. Este proyecto parte de la base de investigación que se lleva a cabo en la LUT, tratando de mejorar el análisis de las muestras de asfalto, por lo que se realizarán las pruebas de estrés y encontrar soluciones para mejorar el asfalto en las carreteras polacas. Esto disminuiría notablemente el costo de mantenimiento. A pesar de no entrar en aspectos muy técnicos sobre el asfalto y su composición, se ha necesitado realizar un estudio profundo sobre todas sus características, para crear un código capaz de obtener los mejores resultados. Por estas razones, se ha desarrollado en Matlab, los algoritmos que permiten el estudio de los especímenes 3D de asfalto. Se ha utilizado este software, ya que Matlab es una poderosa herramienta matemática que permite operar con matrices para realización de operaciones rápidamente, permitiendo desarrollar un código específico para el tratamiento y procesamiento de imágenes en 3D. Gracias a esta herramienta, estos algoritmos realizan procesos tales como, la segmentación de la imagen 3D, pre y post procesamiento de la imagen, filtrado o todo tipo de análisis microestructural de las muestras de asfalto que se están estudiando. El código presentado para la segmentación de las muestras de asfalto 3D es menos complejo en su diseño y desarrollo, debido a las herramientas de procesamiento de imágenes que incluye Matlab, que facilitan significativamente la tarea de programación, así como el método de segmentación utilizado. Respecto al código, este ha sido diseñado teniendo en cuenta el objetivo de facilitar el trabajo de análisis y estudio de las imágenes en 3D de las muestras de asfalto. Por lo tanto, el principal objetivo es el de crear una herramienta para el estudio de este código, por ello fue desarrollado para que pueda ser integrado en un entorno visual, de manera que sea más fácil y simple su utilización. Ese es el motivo por el cual todos estos algoritmos y funciones, que ha sido desarrolladas, se integrarán en una herramienta visual que se ha desarrollado con el GUIDE de Matlab. Esta herramienta ha sido creada en colaboración con Jorge Vega, y fue desarrollada en su proyecto final de carrera, cuyo título es: Segmentación microestructural de Imágenes en 3D de la muestra de asfalto utilizando Matlab. En esta herramienta se ha utilizado todo las funciones programadas en este proyecto, y tiene el objetivo de desarrollar una herramienta que permita crear un entorno gráfico intuitivo y de fácil uso para el estudio de las muestras de 3D de asfalto. Este proyecto se ha dividido en 4 capítulos, en un primer lugar estará la introducción, donde se presentarán los aspectos más importante que se va a componer el proyecto. En el segundo capítulo se presentarán todos los datos técnicos que se han tenido que estudiar para desarrollar la herramienta, entre los que cabe los tres temas más importantes que se han estudiado en este proyecto: materiales asfálticos, los principios de la tomografías 3D y el procesamiento de imágenes. Esta será la base para el tercer capítulo, que expondrá la metodología utilizada en la elaboración del código, con la explicación del entorno de trabajo utilizado en Matlab y todas las funciones de procesamiento de imágenes utilizadas. Además, se muestra todo el código desarrollado, así como una descripción teórica de los métodos utilizados para el pre-procesamiento y segmentación de las imagenes en 3D. En el capítulo 4, se mostrarán los resultados obtenidos en el estudio de una de las muestras de asfalto, y, finalmente, el último capítulo se basa en las conclusiones sobre el desarrollo de este proyecto. En este proyecto se ha llevado han realizado todos los puntos que se establecieron como punto de partida en el anteproyecto para crear la herramienta, a pesar de que se ha dejado para futuros proyectos nuevas posibilidades de este codigo, como por ejemplo, la detección automática de las diferentes regiones de una muestra de asfalto debido a su composición. Como se muestra en este proyecto, las técnicas de procesamiento de imágenes se utilizan cada vez más en multitud áreas, como pueden ser industriales o médicas. En consecuencia, este tipo de proyecto tiene multitud de posibilidades, y pudiendo ser la base para muchas nuevas aplicaciones que se puedan desarrollar en un futuro. Por último, se concluye que este proyecto ha contribuido a fortalecer las habilidades de programación, ampliando el conocimiento de Matlab y de la teoría de procesamiento de imágenes. Del mismo modo, este trabajo proporciona una base para el desarrollo de un proyecto más amplio cuyo alcance será una herramienta que puedas ser utilizada por el equipo de investigación de la Universidad Tecnológica de Lodz y en futuros proyectos. ABSTRACT In this project has been developed one code in MATLAB to process X-ray tomographic 3D images of asphalt specimens. These images 3D has been taken by a research team of the Lodz University of Technology (LUT). The aim of this project is to create a tool that can be used to study differents asphalt specimen and can be used to study them after stress tests undergoing the samples. With the final goal to find solutions to the degradation suffered roads in Poland due to differents causes, like weather conditions. The degradation of the roads is an issue that has been investigated many years ago, due to strong degradation suffered caused by various factors such as climate, poor maintenance or excessive traffic in some cases. It is in Poland where these three factors make the composition of many roads degrade rapidly, especially due to the weather conditions suffered along the year, with temperatures ranging from 30 o C in summer to -20 ° C in winter. This causes the roads suffers a lot and asphalt rises shortly after putting, increasing maintenance costs and road accident. This project part of the base that research is taking place at the LUT, in order to better analyze the asphalt specimens, they are tested for stress and find solutions to improve the asphalt on Polish roads. This would decrease remarkable maintenance cost. Although this project will not go into the technical aspect as asphalt and composition, but it has been required a deep study about all of its features, to create a code able to obtain the best results. For these reasons, there have been developed in Matlab, algorithms that allow the study of 3D specimens of asphalt. Matlab is a powerful mathematical tool, which allows arrays operate fastly, allowing to develop specific code for the treatment and processing of 3D images. Thus, these algorithms perform processes such as the multidimensional matrix sgementation, pre and post processing with the same filtering algorithms or microstructural analysis of asphalt specimen which being studied. All these algorithms and function that has been developed to be integrated into a visual tool which it be developed with the GUIDE of Matlab. This tool has been created in the project of Jorge Vega which name is: Microstructural segmentation of 3D images of asphalt specimen using Matlab engine. In this tool it has been used all the functions programmed in this project, and it has the aim to develop an easy and intuitive graphical environment for the study of 3D samples of asphalt. This project has been divided into 4 chapters plus the introduction, the second chapter introduces the state-of-the-art of the three of the most important topics that have been studied in this project: asphalt materials, principle of X-ray tomography and image processing. This will be the base for the third chapter, which will outline the methodology used in developing the code, explaining the working environment of Matlab and all the functions of processing images used. In addition, it will be shown all the developed code created, as well as a theoretical description of the methods used for preprocessing and 3D image segmentation. In Chapter 4 is shown the results obtained from the study of one of the specimens of asphalt, and finally the last chapter draws the conclusions regarding the development of this project.

Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.

MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

A new label fusion method using graph cuts: application to hippocampus segmentation

The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains.

Comparison of linear and non-linear 2D+T registration methods for DE-MRI cardiac perfusion studies

A series of motion compensation algorithms is run on the challenge data including methods that optimize only a linear transformation, or a non-linear transformation, or both – first a linear and then a non-linear transformation. Methods that optimize a linear transformation run an initial segmentation of the area of interest around the left myocardium by means of an independent component analysis (ICA) (ICA-*). Methods that optimize non-linear transformations may run directly on the full images, or after linear registration. Non-linear motion compensation approaches applied include one method that only registers pairs of images in temporal succession (SERIAL), one method that registers all image to one common reference (AllToOne), one method that was designed to exploit quasi-periodicity in free breathing acquired image data and was adapted to also be usable to image data acquired with initial breath-hold (QUASI-P), a method that uses ICA to identify the motion and eliminate it (ICA-SP), and a method that relies on the estimation of a pseudo ground truth (PG) to guide the motion compensation.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.