Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems

Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning. For a long time, it has been recognized that looking at the dual of an optimization problem may drastically simplify its solution. Deriving efficient strategies which jointly brings into play the primal and the dual problems is however a more recent idea which has generated many important new contributions in the last years. These novel developments are grounded on recent advances in convex analysis, discrete optimization, parallel processing, and non-smooth optimization with emphasis on sparsity issues. In this paper, we aim at presenting the principles of primal-dual approaches, while giving an overview of numerical methods which have been proposed in different contexts. We show the benefits which can be drawn from primal-dual algorithms both for solving large-scale convex optimization problems and discrete ones, and we provide various application examples to illustrate their usefulness

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

The Ecology and Evolution of Human Reproductive Behavior

The complexity of human reproductive behavior has necessitated its examination through a variety of scientific disciplines, each focusing on specific elements of our biology, behavior, and society. However, this complexity also necessitates that we reintegrate the information learned from each discipline into a single framework, one rooted in the evolutionary principles that have shaped the development of all life on earth. In this dissertation, I use this framework to explore human reproductive behavior, with a particular focus on sexual coercion and fertility-mediated sexual behavior. In Chapter 1, I introduce the approach taken in this document, identify several key limitations, and outline the general structure. In Chapter 2, I conduct a comprehensive and interdisciplinary review that includes the fundamentals of sexual conflict and reproductive strategies; the evolution of human reproductive characteristics in response to socio-cognitive demands; the aspects of human sociality expected to influence reproductive behavior; the identified trends in human mating behavior; the proposed pressures behind concealed ovulation in primates; the essentials of the menstrual cycle; and the existing evidence for behavioral fertility in humans. In Chapter 3, I use a game-theory model to investigate the emergence of sexually coercive behavior across a variety of species, including humans, in which male coercion is a non-developmentally-determined reproductive strategy to identify several ecological and behavioral characteristics that predict the emergence of coercive behavior generally consistent with observed trends. In Chapter 4, I use face-trait research to investigate the degree to which women recognize and discriminate between images of men with personality traits associated with different male reproductive strategies as well as how these preferences might be mediated by her relationship and fertility status. In Chapter 5, explore the intersection of fertility, fertility belief, and sexuality, specifically testing the hypothesis that a woman’s sexual interest shifts in response to her fertility while taking into consideration her beliefs regarding her fertility. Finally, in Chapter 6, I review the primary take-home messages of this work and recommend that future research take these into consideration as they move forward. By taking an interdisciplinary approach rooted in evolutionary biology, this work reveals the need for an understanding of human reproductive behavior that incorporates a wider view of reproductive ecology. In doing so, we can gain a more accurate, comprehensive, and nuanced understanding of human reproductive behavior

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Feature-Based Correspondences to Infer the Location of Anatomical Landmarks

A methodology has been developed for automatically determining inter-image correspondences between cliques of features extracted from a reference and a query image. Cliques consist of up to threefeatures and correspondences between them are determined via a hierarchy of similarity metrics based on the inherent properties of the features and geometric relationships between those features. As opposed to approaches that determine correspondences solely by voxel intensity, features that also include shape description are used. Specifically, medial-based features areemployed because they are sparse compared to the number of image voxels and can be automatically extracted from the image.The correspondence framework has been extended to automatically estimate the location of anatomical landmarks in the query image by adding landmarks to the cliques. Anatomical landmark locationsare then inferred from the reference image by maximizing landmark correspondences. The ability to infer landmark locations has provided a means to validate the correspondence framework in thepresence of structural variation between images. Moreover, automated landmark estimation imparts the user with anatomical information and can hypothetically be used to initialize andconstrain the search space of segmentation and registration methods.Methods developed in this dissertation were applied to simulated MRI brain images, synthetic images, and images constructed from several variations of a parametric model. Results indicate that the methods are invariant to global translation and rotation and can operate in the presence of structure variation between images.The automated landmark placement method was shown to be accurate as compared to ground-truth that was established both parametrically and manually. It is envisioned that these automated methods could prove useful for alleviating time-consuming and tedious tasks in applications that currently require manual input, and eliminate intra-user subjectivity

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

Segmentation of brain MRI during early childhood

The objective of this thesis is the development of automatic methods to measure the changes in volume and growth of brain structures in prematurely born infants. Automatic tools for accurate tissue quantification from magnetic resonance images can provide means for understanding how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or behavioural impairment, are related to underlying changes in brain anatomy. Understanding these changes forms a basis for development of suitable treatments to improve the outcomes of premature birth. In this thesis we focus on the segmentation of brain structures from magnetic resonance images during early childhood. Most of the current brain segmentation techniques have been focused on the segmentation of adult or neonatal brains. As a result of rapid development, the brain anatomy during early childhood differs from anatomy of both adult and neonatal brains and therefore requires adaptations of available techniques to produce good results. To address the issue of anatomical differences of the brain during early childhood compared to other age-groups, population-specific deformable and probabilistic atlases are introduced. A method for generation of population-specific prior information in form of a probabilistic atlas is proposed and used to enhance existing segmentation algorithms. The evaluation of registration-based and intensity-based approaches shows the techniques to be complementary in the quality of automatic segmentation in different parts of the brain. We propose a novel robust segmentation method combining the advantages of both approaches. The method is based on multiple label propagation using B-spline non-rigid registration followed by EM segmentation. Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which significantly affects modern high resolution MR data acquired at higher magnetic field strengths. A novel template based method focused on correcting the intensity inhomogeneity in data acquired at higher magnetic field strengths is therefore proposed. The proposed segmentation method combined with proposed intensity inhomogeneity correction method offers a robust tool for quantification of volumes and growth of brain structures during early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age