Research Letter Generalized Cumulative Residual Entropy for Distributions with Unrestricted Supports

We consider the cumulative residual entropy (CRE) a recently introduced measure of entropy. While in previous works distributions with positive support are considered, we generalize the definition of CRE to the case of distributions with general support. We show that several interesting properties of the earlier CRE remain valid and supply further properties and insight to problems such as maximum CRE power moment problems. In addition, we show that this generalized CRE can be used as an alternative to differential entropy to derive information-based optimization criteria for system identification purpose

Connectivity-Based Parcellation of the Cortical Mantle Using q-Ball Diffusion Imaging

This paper exploits the idea that each individual brain region has a specific connection profile to create parcellations of the cortical mantle using MR diffusion imaging. The parcellation is performed in two steps. First, the cortical mantle is split at a macroscopic level into 36 large gyri using a sulcus recognition system. Then, for each voxel of the cortex, a connection profile is computed using a probabilistic tractography framework. The tractography is performed from q fields using regularized particle trajectories. Fiber ODF are inferred from the q-balls using a sharpening process focusing the weight around the q-ball local maxima. A sophisticated mask of propagation computed from a T1-weighted image perfectly aligned with the diffusion data prevents the particles from crossing the cortical folds. During propagation, the particles father child particles in order to improve the sampling of the long fascicles. For each voxel, intersection of the particle trajectories with the gyri lead to a connectivity profile made up of only 36 connection strengths. These profiles are clustered on a gyrus by gyrus basis using a K-means approach including spatial regularization. The reproducibility of the results is studied for three subjects using spatial normalization

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured.EThOS - Electronic Theses Online ServiceConsejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT)GBUnited Kingdo

Neurobiological correlates of avatar identification processing and emotional inhibitory control in internet gaming disorder

Internet gaming disorder (IGD) is the most prevalent subcategory of internet addiction. It has been associated with self-concept deficits and related characteristics such as emotional as well as social competence deficits, increased social anxiety and a stronger identification with the own avatar (i.e. a graphical agent that often seems to be constructed according to gamers’ ideal). In addition, IGD seems to be linked with inhibitory control deficits, definable as impairments in the inhibition of reactions to irrelevant stimuli during the pursuit of cognitively represented goals. However, the neurobiological correlates of avatar compared to self and ideal-related identification processing as well as emotional inhibitory control in (socially) anxious contexts as potentially important factors in IGD development have not been explored yet. The brain region of the left angular gyrus (AG) has been associated with self-identification from a third-person perspective in healthy controls and showed avatar-related hyperactivation in long-term online gamers during a task on self and avatar reflection in functional magnetic resonance imaging (fMRI). The dorsal anterior cingulate cortex (dACC) seems to be involved in the integration of negative affect and cognitive control. Based on these observations, internet gaming addicts were neurobiologically examined by means of fMRI with a focus on the left AG as well as the dACC while completing specific tasks and compared to non-addicted controls as well as social media addicts. Hereby, participants’ concepts of self, ideal and avatar were assessed with a reflection task asking for the evaluation of characteristics regarding the self, ideal and own avatar. Emotional inhibitory control in a socially anxious context was neurobiologically explored by means of an emotional Stroop task (EST) assessing the inhibition on socially anxious words compared to positive, negative and neutral word stimuli under parallel reaction time recording. In addition, the emotional inhibitory control at anxious stimuli was examined neuropsychologically by means of an affective Go/No-Go task (AGN). Besides, psychometric questionnaires assessing impulsivity, emotional competence and social anxiety were applied. Internet gaming addicts showed significantly higher levels of impulsivity, social anxiety and emotional competence deficits relative to non-addicted controls in psychometric measures. Neurobiologically, internet gaming addicts exhibited left AG hyperactivations during the reflection on their own avatar relative to self and ideal reflection within their group as well as compared to non-addicted controls. In the EST, internet gaming addicts had longer reaction times during the inhibition on socially anxious compared to positive and negative words as well as compared to positive, negative and neutral words together. During the latter comparison, internet gaming addicts neurobiologically showed significant hypoactivations in the left middle and superior temporal gyrus (MTG and STG), which was also significantly lower relative to social media addicts. Functional alterations in the dACC were not observed. Neuropsychologically, no significant differences in emotional inhibitory control at anxious stimuli between internet gaming addicts and non-addicted controls were detected by means of the AGN. In summary, the virtually concretized avatar might replace the rather abstract ideal in IGD as a construct to identify with. The need for such a construct might arise from the urge to compensate dissatisfaction with the own person as a facet of self-concept deficits. The MTG and STG have previously been associated with the retrieval of words or expressions during communication, social perception and emotion regulation (based on a study in social anxiety disorder). The present finding of these regions’ hypoactivation in relation to socially anxious stimuli might indicate that 1) socially anxious words are less retrievable from the semantic storage of internet gaming addicts than positive, negative or neutral words, 2) in IGD, emotional inhibitory control in the socially anxious context is represented by brain regions involved in the processing of social information (such as the MTG and STG) and that 3) internet gaming addicts have deficiencies in the cognitive regulation of emotions as well as in the processing of social information, with the MTG and STG hypoactivation during socially anxious word blocks possibly serving as a neurobiological correlate of IGD-related social and emotional competence deficits as facets of self-concept impairments

Contributions à la segmentation d'image : phase locale et modèles statistiques

Ce document presente une synthèse de mes travaux apres these, principalement sur la problematique de la segmentation d’images

Microstructure Characterization of Continuous-Discontinuous Fibre Reinforced Polymers based on Volumetric Images

Die quantitative Beschreibung der Mikrostruktur von Faserverbundwerkstoffen ist elementar für die Modellierung von thermischen und mechanischen Eigenschaften. Durch die stetige Entwicklung der Computertomographie ist es heute möglich dreidimensionale Bilddaten von Werkstoffen mit einer Auflösung von unter einem Mikrometer zu erzeugen. Moderne Computersysteme bieten ausreichend Rechenleistung um die resultierenden volumetrischen Bilddaten automatisiert auszuwerten und relevante Statistiken zu erzeugen. Die vorliegende Arbeit befasst sich mit der Quantifizierung von mikrostrukturellen Merkmalen von faserverstärkten Polymeren unter Verwendung von computertomographischen Aufnahmen. Diverse Verfahren zur Bestimmung von lokalen Faserorientierungen, -volumengehalt, -krümmungen und -längen wurden implementiert und validiert. Des Weiteren wurden zwei Ansätze zur Berechnung von lokalen Oberflächenkrümmungen zur Porositätsanalyse verglichen. Die Ergebnisse zeigen, dass einige der bereits verfügbaren Orientierungsanalyseverfahren bereits sehr robust sind und auch mit stark verrauschten Aufnahmen mit geringem Kontrast sehr gute Resultate erzielen. Faserlängenverteilungen, die mittels Fasertrackingverfahren aus computertomographischen Aufnahmen extrahiert wurden lieferten nur bis zu einer Probengröße von 5mm verlässliche Faserlängenverteilungen und sind daher nur bedingt für die Anwendung an langfaserverstärkten Polymeren geeignet