This dissertation explores the role of brainstem structures in the development and expression of generalized tonic-clonic seizures. The functional consequences of brainstem seizures are investigated using the kindling paradigm in order to understand the behavioral and cognitive effects of generalized seizures. <BR><BR> I begin by investigating the general characteristics of brainstem kindling. The first experiment demonstrates that certain brainstem sites are indeed susceptible to kindling and begins to delineate the features that distinguish brainstem seizures from those evoked at other brain regions. Further investigation of the EEG signal features using wavelet analysis reveals that changes in the spectral properties of the electrographic activity during kindling include significant changes to high-frequency activity and organized low-frequency activity. I also identify transitions that include frequency sweeps and abrupt seizure terminations. The changing spectral features are shown to be critically associated with the evolution of the kindled seizures and may have important functional consequences. The surprising responsiveness of some brainstem structures to kindling forces us to reconsider the overall role of these structures in epileptogenesis as well as in the healthy dynamical functioning of the brain. <BR><BR> In order to study the functional consequences, a series of experiments examines the changes in behavior, cognition and affect that follow these brainstem seizures. Although the results show no effects on spatial learning or memory, there are significant and complex effects on anxiety- and depression-like behavior that appear to be related to motivation. In order to further study the cognitive effects, a second set of behavioral experiments considers how context (i.e., the environment) interacts with the behavioral changes. The results indicate that changes in affect may only be apparent when choice between seizure-related and seizure-free contexts is given, suggesting that the environment and choice can play key roles in the behavioral consequences of seizures. This thesis also includes an appendix that applies synchrotron imaging to investigate the anatomical consequences of electrode implantation in kindling and shows that significantly increased iron depositions occur even with purportedly biocompatible electrodes widely used in research and clinical settings. <BR><BR> Examination of the role of brainstem structures in generalized seizures in this dissertation offers new perspectives and insights to epileptogenesis and the behavioral effects of epilepsy. The changes in EEG features, behavior, affect and motivation observed after brainstem seizures and kindling may have important clinical implications. For example, the results suggest a need to reexamine the concept of psychogenic seizures, a potential connection to Sudden Unexplained Death in Epilepsy (SUDEP), and the contribution of environmental factors. It is hoped that these findings will help elucidate the complex issues involved in understanding and improving the quality of life for people with epilepsy.

Graph cuts have emerged as an important combinatorial optimization tool for many problems in vision. Most of the computer vision problems are discrete labeling problems. For example, in stereopsis, labels represent disparity and in image restoration, labels correspond to image intensities. Finding a good labeling involves optimization of an Energy Function. In computer vision, energy functions for discrete labeling problems can be elegantly formulated through Markov Random Field (MRF) based modeling and graph cut algorithms have been found to efficiently optimize wide class of such energy functions. The main contribution of this thesis lies in developing an efficient combinatorial optimization algorithm which can be applied to a wide class of energy functions. Generally, graph cut algorithms deal sequentially with each label in the labeling problem at hand. The time complexity of these algorithms increases linearly with number of labels. Our algorithm, finds a solution/labeling in logarithmic time complexity without compromising on quality of solution. In our work, we present an improved Logcut algorithm [24]. Logcut algorithm [24] deals with finding individual bit values in integer representation of labels. It has logarithmic time complexity, but requires training over data set. Our improved Logcut (Heuristic-Logcut or H-Logcut) algorithm eliminates the need for training and obtains comparable results in respect to original Logcut algorithm. Original Logcut algorithm cannot be initialized by a known labeling. We present a new algorithm, Sequential Bit Plane Correction (SBPC) which overcomes this drawback of Logcut algorithm. SBPC algorithm starts from a known labeling and individually corrects each bit of a label. This algorithm too has logarithmic time complexity. SBPC in combination with H-Logcut algorithm, further improves rate of convergence and quality of results. Finally, a hierarchical approach to graph cut optimization is used to further improve on rate of convergence of our algorithm. Generally, in a hierarchical approach first, a solution at coarser level is computed and then its result is used to initialize algorithm at a finer level. Here we have presented a novel way of initializing the algorithm at finer level through fusion move [25]. The SBPC and H-Logcut algorithms are extended to accommodate for hierarchical approach. It is found that this approach drastically improves the rate of convergence and attains a very low energy labeling. The effectiveness of our approach is demonstrated on stereopsis. It is found that the algorithm significantly out performs all existing algorithms in terms of quality of solution as well as rate of convergence.

In dieser Dissertation wird das viskoplastische Schubverhalten eines magnetorheologischen Fluids (MRF) modelliert. Mithilfe eines phänomenologischen Modellierungsansatzes auf Basis nichtlinearer rheologischer Elemente können die gemessenen Fließkurven sowie Speicher- und Verlustmoduli abgebildet werden. Ein MRF ist ein Material mit fest-flüssig Übergang. Es besitzt von einem Magnetfeld abhängige Materialeigenschaften. Um diese beschreiben zu können, wird zunächst eine phänomenologische Stoffklassifizierung eingeführt. Auf deren Grundlage teilen sich Stoffe allgemein in Flüssigkeiten, Festkörper und Materialien mit fest-flüssig Übergang auf. Zur Beschreibung des Materialverhaltens von MRF werden drei viskoplastische Modelle formuliert und gegenübergestellt. Zur Identifikation der Materialparameter wird eine Identifikationsstrategie auf der Grundlage charakteristischer Punkte entwickelt. Charakteristische Punkte sind exklusive Punkte von Materialfunktionen, die analytisch beschrieben und ohne Weiteres experimentell ermittelt werden können. Analytische Ausdrücke für charakteristische Punkte der Speicher- und Verlustmoduli werden über das Analogieprinzip unter Verwendung von Lissajous Diagrammen abgeleitet. Infolgedessen können die Materialparameter durch das Auswerten algebraischer Zusammenhänge identifiziert werden, ohne nichtlineare Optimierungsverfahren anwenden zu müssen. Hierbei stellt die Fließspannung einen signifikanten Materialparameter dar. Deswegen werden die Standardverfahren zur Bestimmung der Fließspannung auf rheologische Modelle angewendet und bewertet.

Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm. / <p>QC 20130610</p>

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This thesis addresses three problems related to image registration, prediction and tracking, applied to Angiography and Oncology. For image analysis, various probabilistic models have been employed to characterize the image deformations, target motions and state estimations. (i) In Digital Subtraction Angiography (DSA), having a high quality visualization of the blood motion in the vessels is essential both in diagnostic and interventional applications. In order to reduce the inherent movement artifacts in DSA, non-rigid image registration is used before subtracting the mask from the contrast image. DSA image registration is a challenging problem, as it requires non-rigid matching across spatially non-uniform control points, at high speed. We model the problem of sub-pixel matching, as a labeling problem on a non-uniform Markov Random Field (MRF). We use quad-trees in a novel way to generate the non uniform grid structure and optimize the registration cost using graph-cuts technique. The MRF formulation produces a smooth displacement field which results in better artifact reduction than with the conventional approach of independently registering the control points. The above approach is further improved using two models. First, we introduce the concept of pivotal and non-pivotal control points. `Pivotal control points' are nodes in the Markov network that are close to the edges in the mask image, while 'non-pivotal control points' are identified in soft tissue regions. This model leads to a novel MRF framework and energy formulation. Next, we propose a Gaussian MRF model and solve the energy minimization problem for sub-pixel DSA registration using Random Walker (RW). An incremental registration approach is developed using quad-tree based MRF structure and RW, wherein the density of control points is hierarchically increased at each level M depending of the features to be used and the required accuracy. A novel numbering scheme of the control points allows us to reuse the computations done at level M in M + 1. Both the models result in an accelerated performance without compromising on the artifact reduction. We have also provided a CUDA based design of the algorithm, and shown performance acceleration on a GPU. We have tested the approach using 25 clinical data sets, and have presented the results of quantitative analysis and clinical assessment. (ii) In External Beam Radiation Therapy (EBRT), in order to monitor the intra fraction motion of thoracic and abdominal tumors, the lung diaphragm apex can be used as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. We propose a novel approach for tracking the ipsilateral hemidiaphragm apex (IHDA) position on CBCT projection images. We model the diaphragm state as a spatiotemporal MRF, and obtain the trace of the apex by solving an energy minimization problem through graph-cuts. We have tested the approach using 15 clinical data sets and found that this approach outperforms the conventional full search method in terms of accuracy. We have provided a GPU based heterogeneous implementation of the algorithm using CUDA to increase the viability of the approach for clinical use. (iii) In an adaptive radiotherapy system, irrespective of the methods used for target observations there is an inherent latency in the beam control as they involve mechanical movement and processing delays. Hence predicting the target position during `beam on target' is essential to increase the control precision. We propose a novel prediction model (called o set sine model) for the breathing pattern. We use IHDA positions (from CBCT images) as measurements and an Unscented Kalman Filter (UKF) for state estimation. The results based on 15 clinical datasets show that, o set sine model outperforms the state of the art LCM model in terms of prediction accuracy.

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