Doctor of Philosophy

dissertationFunctional magnetic resonance imaging (fMRI) measures the change of oxygen consumption level in the blood vessels of the human brain, hence indirectly detecting the neuronal activity. Resting-state fMRI (rs-fMRI) is used to identify the intrinsic functional patterns of the brain when there is no external stimulus. Accurate estimation of intrinsic activity is important for understanding the functional organization and dynamics of the brain, as well as differences in the functional networks of patients with mental disorders. This dissertation aims to robustly estimate the functional connectivities and networks of the human brain using rs-fMRI data of multiple subjects. We use Markov random field (MRF), an undirected graphical model to represent the statistical dependency among the functional network variables. Graphical models describe multivariate probability distributions that can be factorized and represented by a graph. By defining the nodes and the edges along with their weights according to our assumptions, we build soft constraints into the graph structure as prior information. We explore various approximate optimization methods including variational Bayesian, graph cuts, and Markov chain Monte Carlo sampling (MCMC). We develop the random field models to solve three related problems. In the first problem, the goal is to detect the pairwise connectivity between gray matter voxels in a rs-fMRI dataset of the single subject. We define a six-dimensional graph to represent our prior information that two voxels are more likely to be connected if their spatial neighbors are connected. The posterior mean of the connectivity variables are estimated by variational inference, also known as mean field theory in statistical physics. The proposed method proves to outperform the standard spatial smoothing and is able to detect finer patterns of brain activity. Our second work aims to identify multiple functional systems. We define a Potts model, a special case of MRF, on the network label variables, and define von Mises-Fisher distribution on the normalized fMRI signal. The inference is significantly more difficult than the binary classification in the previous problem. We use MCMC to draw samples from the posterior distribution of network labels. In the third application, we extend the graphical model to the multiple subject scenario. By building a graph including the network labels of both a group map and the subject label maps, we define a hierarchical model that has richer structure than the flat single-subject model, and captures the shared patterns as well as the variation among the subjects. All three solutions are data-driven Bayesian methods, which estimate model parameters from the data. The experiments show that by the regularization of MRF, the functional network maps we estimate are more accurate and more consistent across multiple sessions

Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research. In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques. In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis

El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostáticoLa ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels mAlcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing.Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11316

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization