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5.4pt;mso-para-margin:0in;mso-para-margin-bottom:.0001pt;mso-pagination:widow-orphan;font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;}</style><![endif]--><span style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:EN-US;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">The </span><span lang="sr-Latn-RS" style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:#241A;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">t</span><span style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:EN-US;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">hesis investigates development, improvement and evaluation of methods for quantitative characterization of objects from their digital images and similarity measurements between digital images. Methods for quantitative characterization of objects from their digital images are increasingly used in applications in which error can </span><span lang="sr-Latn-RS" style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:#241A;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">have crtical consequences, </span><span style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:EN-US;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">but the traditional methods for shape quantification are of low precision and accuracy. </span><span lang="sr-Latn-RS" style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:#241A;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">In the thesis is shown </span><span style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:EN-US;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">that the </span><span lang="sr-Latn-RS" style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:#241A;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">coverage of a pixel by a shape can</span><span style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:EN-US;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA"> be used to highly improve the accuracy and precision of using digital images to estimate the maximal distance between objects </span><span lang="sr-Latn-RS" style="font-size:9.0pt;font-family:&quot;Arial&quot;,&quot;sans-serif&quot;;mso-fareast-font-family:&quot;Times New Roman&quot;;mso-font-kerning:.5pt;mso-ansi-language:#241A;mso-fareast-language:AR-SA;mso-bidi-language:AR-SA">furthest points measured in a given direction. It is highly desirable that a distance measure between digital images can be related to a certain shape property and morphological operations are used when defining a distance for this purpose. Still, the distances defined in this manner turns out to be insufficiently sensitive to relevant data representing shape properties in images. We show that the idea of adaptive mathematical morphology can be used successfully to overcome problems related to sensitivity of distances defined via morphological operations when comparing objects from their digital image representations.</span></p> / <p>У тези су размотрени развој, побољшање и евалуација метода за квантитативну карактеризацију објеката приказаних дигиталним сликама, као и мере растојања између дигиталних слика. Методе за квантитативну карактеризацију објеката представљених дигиталним сликама се&nbsp; све више користе у применама у којима грешка може имати критичне последице, а традиционалне методе за&nbsp; квантитативну карактеризацију су мале прецизности и тачности. У тези се показује да се коришћењем информације о покривеност пиксела обликом може значајно побољшати прецизност и тачност оцене растојања између две најудаљеније тачке облика мерено у датом правцу. Веома је пожељно да мера растојања између дигиталних слика може да се веже за одређену особину облика и морфолошке операције се користе приликом дефинисања растојања у ту сврху. Ипак, растојања дефинисана на овај начин показују се недовољно осетљива на релевантне податке дигиталних слика који представљају особине облика. У тези се показује да идеја адаптивне математичке морфологије може успешно да се користи да би се превазишао поменути&nbsp; проблем осетљивости растојања дефинисаних користећи морфолошке операције.</p> / <p>U tezi su razmotreni razvoj, poboljšanje i evaluacija metoda za kvantitativnu karakterizaciju objekata prikazanih digitalnim slikama, kao i mere rastojanja između digitalnih slika. Metode za kvantitativnu karakterizaciju objekata predstavljenih digitalnim slikama se&nbsp; sve više koriste u primenama u kojima greška može imati kritične posledice, a tradicionalne metode za&nbsp; kvantitativnu karakterizaciju su male preciznosti i tačnosti. U tezi se pokazuje da se korišćenjem informacije o pokrivenost piksela oblikom može značajno poboljšati preciznost i tačnost ocene rastojanja između dve najudaljenije tačke oblika mereno u datom pravcu. Veoma je poželjno da mera rastojanja između digitalnih slika može da se veže za određenu osobinu oblika i morfološke operacije se koriste prilikom definisanja rastojanja u tu svrhu. Ipak, rastojanja definisana na ovaj način pokazuju se nedovoljno osetljiva na relevantne podatke digitalnih slika koji predstavljaju osobine oblika. U tezi se pokazuje da ideja adaptivne matematičke morfologije može uspešno da se koristi da bi se prevazišao pomenuti&nbsp; problem osetljivosti rastojanja definisanih koristeći morfološke operacije.</p>

Natural surfaces are usually associated with feature graphs, such as the cortical surface with anatomical atlas structure. Such a feature graph subdivides the whole surface into meaningful sub-regions. Existing brain mapping and registration methods did not integrate anatomical atlas structures. As a result, with existing brain mappings, it is difficult to visualize and compare the atlas structures. And also existing brain registration methods can not guarantee the best possible alignment of the cortical regions which can help computing more accurate shape similarity metrics for neurodegenerative disease analysis, e.g., Alzheimer’s disease (AD) classification. Also, not much attention has been paid to tackle surface parameterization and registration with graph constraints in a rigorous way which have many applications in graphics, e.g., surface and image morphing. This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints. (1) First, we propose structural brain mapping which maps the brain cortical surface onto a planar convex domain using Tutte embedding of a novel atlas graph and harmonic map with atlas graph constraints to facilitate visualization and comparison between the atlas structures. (2) Next, we propose a novel brain registration technique based on an intrinsic atlas-constrained harmonic map which provides the best possible alignment of the cortical regions. (3) After that, the proposed brain registration technique has been applied to compute shape similarity metrics for AD classification. (4) Finally, we propose techniques to compute intrinsic graph-constrained parameterization and registration for general genus-0 surfaces which have been used in surface and image morphing applications.

Failed fuel refers to the breach in the fuel-clad of an irradiated fuel assembly in a nuclear reactor. Neutron detection or gamma detection is commonly used in Failed Fuel Detection and Locating (FFDL) system to monitor the activity of the coolant. Though these methods offer specific advantages under different conditions of the coolant, providing both types of detectors in FFDL system is impractical. This limitation is the motivation for the detector system developed in the present work. In the present work, effort has been made for realising a detector system for simultaneous measurement of neutron and gamma activity of the coolant, thus offering a two-parameter basis for failed fuel detection. NE213 liquid scintillator was chosen for this work as it has good detection capability for both neutrons and gammas. Additionally, the neutrons and gammas interacting with NE213 detector can be separated based on pulse shape discrimination. The work reported in this thesis includes fabrication details and different steps followed in assembling the NE213 detector. Details of experimental set-up developed for pulse height analysis and pulse shape analysis are covered. Results of experiments carried out to study the response of the NE213 detector to gamma and neutron sources using pulse height analyser are presented. The absolute gamma efficiency and relative gamma efficiency of NE213 detector are calculated. Neutron–gamma separation capability of NE213 detector based pulse shape analysis system is reported. Application of the developed detector system to analyse the coolant activity in FFDL system in a reactor is described. Response of the detector is compared with the existing FFDL system at different power levels of the reactor. Since failed fuel is a rare event, it was simulated using neutron and gamma sources. Pulse shape analysis spectra obtained under simulated failed fuel condition are presented.

Les pays industrialisés comme le Canada doivent faire face au vieillissement de leur population. En particulier, la majorité des personnes âgées, vivant à domicile et souvent seules, font face à des situations à risques telles que des chutes. Dans ce contexte, la vidéosurveillance est une solution innovante qui peut leur permettre de vivre normalement dans un environnement sécurisé. L’idée serait de placer un réseau de caméras dans l’appartement de la personne pour détecter automatiquement une chute. En cas de problème, un message pourrait être envoyé suivant l’urgence aux secours ou à la famille via une connexion internet sécurisée. Pour un système bas coût, nous avons limité le nombre de caméras à une seule par pièce ce qui nous a poussé à explorer les méthodes monoculaires de détection de chutes. Nous avons d’abord exploré le problème d’un point de vue 2D (image) en nous intéressant aux changements importants de la silhouette de la personne lors d’une chute. Les données d’activités normales d’une personne âgée ont été modélisées par un mélange de gaussiennes nous permettant de détecter tout événement anormal. Notre méthode a été validée à l’aide d’une vidéothèque de chutes simulées et d’activités normales réalistes. Cependant, une information 3D telle que la localisation de la personne par rapport à son environnement peut être très intéressante pour un système d’analyse de comportement. Bien qu’il soit préférable d’utiliser un système multi-caméras pour obtenir une information 3D, nous avons prouvé qu’avec une seule caméra calibrée, il était possible de localiser une personne dans son environnement grâce à sa tête. Concrêtement, la tête de la personne, modélisée par une ellipsoide, est suivie dans la séquence d’images à l’aide d’un filtre à particules. La précision de la localisation 3D de la tête a été évaluée avec une bibliothèque de séquence vidéos contenant les vraies localisations 3D obtenues par un système de capture de mouvement (Motion Capture). Un exemple d’application utilisant la trajectoire 3D de la tête est proposée dans le cadre de la détection de chutes. En conclusion, un système de vidéosurveillance pour la détection de chutes avec une seule caméra par pièce est parfaitement envisageable. Pour réduire au maximum les risques de fausses alarmes, une méthode hybride combinant des informations 2D et 3D pourrait être envisagée. / Developed countries like Canada have to adapt to a growing population of seniors. A majority of seniors reside in private homes and most of them live alone, which can be dangerous in case of a fall, particularly if the person cannot call for help. Video surveillance is a new and promising solution for healthcare systems to ensure the safety of elderly people at home. Concretely, a camera network would be placed in the apartment of the person in order to automatically detect a fall. When a fall is detected, a message would be sent to the emergency center or to the family through a secure Internet connection. For a low cost system, we must limit the number of cameras to only one per room, which leads us to explore monocular methods for fall detection. We first studied 2D information (images) by analyzing the shape deformation during a fall. Normal activities of an elderly person were used to train a Gaussian Mixture Model (GMM) to detect any abnormal event. Our method was tested with a realistic video data set of simulated falls and normal activities. However, 3D information like the spatial localization of a person in a room can be very useful for action recognition. Although a multi-camera system is usually preferable to acquire 3D information, we have demonstrated that, with only one calibrated camera, it is possible to localize a person in his/her environment using the person’s head. Concretely, the head, modeled by a 3D ellipsoid, was tracked in the video sequence using particle filters. The precision of the 3D head localization was evaluated with a video data set containing the real 3D head localizations obtained with a Motion Capture system. An application example using the 3D head trajectory for fall detection is also proposed. In conclusion, we have confirmed that a video surveillance system for fall detection with only one camera per room is feasible. To reduce the risk of false alarms, a hybrid method combining 2D and 3D information could be considered.

The aim of this thesis is to investigate the shape change in hippocampus due to the atrophy in Alzheimer's disease (AD). To this end, specific algorithms and methodologies were developed to segment the hippocampus from structural magnetic resonance (MR) images and model variations in its shape. We use a multi-atlas based segmentation propagation approach for the segmentation of hippocampus which has been shown to obtain accurate parcellation of brain structures. We developed a supervised method to build a population specific atlas database, by propagating the parcellations from a smaller generic atlas database. Well segmented images are inspected and added to the set of atlases, such that the segmentation capability of the atlas set may be enhanced. The population specific atlases are evaluated in terms of the agreement among the propagated labels when segmenting new cases. Compared with using generic atlases, the population specific atlases obtain a higher agreement when dealing with images from the target population. Atlas selection is used to improve segmentation accuracy. In addition to the conventional selection by image similarity ranking, atlas selection based on maximum marginal relevance (MMR) re-ranking and least angle regression (LAR) sequence are developed for atlas selection. By taking the redundancy among atlases into consideration, diversity criteria are shown to be more efficient in atlas selection which is applicable in the situation where the number of atlases to be fused is limited by the computational resources. Given the segmented hippocampal volumes, statistical shape models (SSMs) of hippocampi are built on the samples to model the shape variation among the population. The correspondence across the training samples of hippocampi is established by a groupwise optimization of the parameterized shape surfaces. The spherical parameterization of the hippocampal surfaces are flatten to facilitate the reparameterization and interpolation. The reparameterization is regularized by viscous fluid, which is solved by a fast implementation based on discrete sine transform. In order to use the hippocampal SSM to describe the shape of an unseen hippocampal surface, we developed a shape parameter estimator based on the expectationmaximization iterative closest points (EM-ICP) algorithm. A symmetric data term is included to achieve the inverse consistency of the transformation between the model and the shape, which gives more accurate reconstruction of the shape from the model. The shape prior modeled by the SSM is used in the maximum a posteriori estimation of the shape parameters, which is shown to enforce the smoothness and avoid the effect of over-fitting. In the study of the hippocampus in AD, we use the SSM to model the hippocampal shape change between the healthy control subjects and patients diagnosed with AD. We identify the regions affected by the atrophy in AD by assessing the spatial difference between the control and AD groups at each corresponding landmark. Localized shape analysis is performed on the regions exhibiting significant inter-group difference, which is shown to improve the discrimination ability of the principal component analysis (PCA) based SSM. The principal components describing the localized shape variability among the population are also shown to display stronger correlation with the decline of episodic memory scores linked to the pathology of hippocampus in AD.

Gegenstand der vorliegenden Dissertation ist die Entwicklung von Algorithmen und Methoden mit dem Ziel, Archäologen bei der täglichen wissenschaftlichen Arbeit zu unterstützen. Im Teil I werden Ideen präsentiert, mit denen sich die extrem zeitintensive und stellenweise stupide Funddokumentation beschleunigen lässt. Es wird argumentiert, dass das dreidimensionale Erfassen der Fundobjekte mittels Laser- oder Streifenlichtscannern trotz hoher Anschaffungskosten wirtschaftlich und vor allem qualitativ attraktiv ist. Mithilfe von nicht fotorealistischen Visualisierungstechniken können dann wieder aussagekräftige, aber dennoch objektive Bilder generiert werden. Außerdem ist speziell für Gefäße eine vollautomatische und umfassende Merkmalserhebung möglich. Im II. Teil gehen wir auf das Problem der automatisierten Gefäßklassifikation ein. Nach einer theoretischen Betrachtung des Typbegriffs in der Archäologie präsentieren wir eine Methodologie, in der Verfahren sowohl aus dem Bereich des unüberwachten als auch des überwachten Lernens zum Einsatz kommen. Besonders die letzteren haben sich dabei als überaus praktikabel erwiesen, um einerseits unbekanntes Material einer bestehenden Typologie zuzuordnen, andererseits aber auch die Struktur der Typologie selbst kritisch zu hinterfragen. Sämtliche Untersuchungen haben wir beispielhaft an den bronzezeitlichen Gräberfeldern von Kötitz, Altlommatzsch (beide Lkr. Meißen), Niederkaina (Lkr. Bautzen) und Tornow (Lkr. Oberspreewald-Lausitz) durchgeführt und waren schließlich sogar in der Lage, archäologisch relevante Zusammenhänge zwischen diesen Fundkomplexen herzustellen. / The topic of the dissertation at hand is the development of algorithms and methods aiming at supporting the daily scientific work of archaeologists. Part I covers ideas for accelerating the extremely time-consuming and often tedious documentation of finds. It is argued that digitizing the objects with 3D laser or structured light scanners is economically reasonable and above all of high quality, even though those systems are still quite expensive. Using advanced non-photorealistic visualization techniques, meaningful but at the same time objective pictures can be generated from the virtual models. Moreover, specifically for vessels a fully-automatic and comprehensive feature extraction is possible. In Part II, we deal with the problem of automated vessel classification. After a theoretical consideration of the type concept in archaeology we present a methodology, which employs approaches from the fields of both unsupervised and supervised machine learning. Particularly the latter have proven to be very valuable in order to assign unknown entities to an already existing typology, but also to challenge the typology structure itself. All the analyses have been exemplified by the Bronze Age cemeteries of Kötitz, Altlommatzsch (both district of Meißen), Niederkaina (district of Bautzen), and Tornow (district Oberspreewald-Lausitz). Finally, we were even able to discover archaeologically relevant relationships between these sites.

Analyse de la forme 3D est un sujet de recherche extrêmement actif dans les deux l'infographie et vision par ordinateur. Dans la vision par ordinateur, l'acquisition de formes et de modélisation 3D sont généralement le résultat du traitement des données complexes et des méthodes d'analyse de données. Il existe de nombreuses situations concrètes où une forme visuelle est modélisé par un nuage de points observés avec une variété de capteurs 2D et 3D. Contrairement aux données graphiques, les données sensorielles ne sont pas, dans le cas général, uniformément répartie sur toute la surface des objets observés et ils sont souvent corrompus par le bruit du capteur, les valeurs aberrantes, les propriétés de surface (diffusion, spécularités, couleur, etc), l'auto occlusions, les conditions d'éclairage variables. Par ailleurs, le même objet que l'on observe par différents capteurs, à partir de points de vue légèrement différents, ou à des moments différents cas peuvent donner la répartition des points tout à fait différentes, des niveaux de bruit et, plus particulièrement, les différences topologiques, par exemple, la fusion des mains. Dans cette thèse, nous présentons une représentation de multi-échelle des formes articulés et concevoir de nouvelles méthodes d'analyse de forme, en gardant à l'esprit les défis posés par les données de forme visuelle. En particulier, nous analysons en détail le cadre de diffusion de chaleur pour représentation multi-échelle de formes 3D et proposer des solutions pour la segmentation et d'enregistrement en utilisant les méthodes spectrales graphique et divers algorithmes d'apprentissage automatique, à savoir, le modèle de mélange gaussien (GMM) et le Espérance-Maximisation (EM). Nous présentons d'abord l'arrière-plan mathématique sur la géométrie différentielle et l'isomorphisme graphique suivie par l'introduction de la représentation spectrale de formes 3D articulés. Ensuite, nous présentons une nouvelle méthode non supervisée pour la segmentation de la forme 3D par l'analyse des vecteurs propres Laplacien de graphe. Nous décrivons ensuite une solution semi-supervisé pour la segmentation de forme basée sur un nouveau paradigme d'apprendre, d'aligner et de transférer. Ensuite, nous étendre la représentation de forme 3D à une configuration multi-échelle en décrivant le noyau de la chaleur cadre. Enfin, nous présentons une méthode d'appariement dense grâce à la représentation multi-échelle de la chaleur du noyau qui peut gérer les changements topologiques dans des formes visuelles et de conclure par une discussion détaillée et l'orientation future des travaux. / 3D shape analysis is an extremely active research topic in both computer graphics and computer vision. In computer vision, 3D shape acquisition and modeling are generally the result of complex data processing and data analysis methods. There are many practical situations where a visual shape is modeled by a point cloud observed with a variety of 2D and 3D sensors. Unlike the graphical data, the sensory data are not, in the general case, uniformly distributed across the surfaces of the observed objects and they are often corrupted by sensor noise, outliers, surface properties (scattering, specularities, color, etc.), self occlusions, varying lighting conditions. Moreover, the same object that is observed by different sensors, from slightly different viewpoints, or at different time instances may yield completely different point distributions, noise levels and, most notably, topological differences, e.g., merging of hands. In this thesis we outline single and multi-scale representation of articulated 3D shapes and devise new shape analysis methods, keeping in mind the challenges posed by visual shape data. In particular, we discuss in detail the heat diffusion framework for multi-scale shape representation and propose solutions for shape segmentation and dense shape registration using the spectral graph methods and various other machine learning algorithms, namely, the Gaussian Mixture Model (GMM) and the Expectation Maximization (EM). We first introduce the mathematical background on differential geometry and graph isomorphism followed by the introduction of pose-invariant spectral embedding representation of 3D articulated shapes. Next we present a novel unsupervised method for visual shape segmentation by analyzing the Laplacian eigenvectors. We then outline a semi-supervised solution for shape segmentation based upon a new learn, align and transfer paradigm. Next we extend the shape representation to a multi-scale setup by outlining the heat-kernel framework. Finally, we present a topologically-robust dense shape matching method using the multi-scale heat kernel representation and conclude with a detailed discussion and future direction of work.

Cette thèse a pour objet l'analyse statistique de formes, dans le contexte de l'imagerie médicale.Dans le champ de l'imagerie médicale, l'analyse de formes est utilisée pour décrire la variabilité morphologique de divers organes et tissus. Nous nous focalisons dans cette thèse sur la construction d'un modèle génératif et discriminatif, compact et non-linéaire, adapté à la représentation de formes.Ce modèle est évalué dans le contexte de l'étude d'une population de patients atteints de la maladie d'Alzheimer et d'une population de sujets contrôles sains. Notre intérêt principal ici est l'utilisationdu modèle discriminatif pour découvrir les différences morphologiques les plus discriminatives entre une classe de formes donnée et des formes n'appartenant pas à cette classe. L'innovation théorique apportée par notre modèle réside en deux points principaux : premièrement, nous proposons un outil pour extraire la différence discriminative dans le cadre Support Vector Data Description (SVDD) ; deuxièmement, toutes les reconstructions générées sont anatomiquementcorrectes. Ce dernier point est dû au caractère non-linéaire et compact du modèle, lié à l'hypothèse que les données (les formes) se trouvent sur une variété non-linéaire de dimension faible. Une application de notre modèle à des données médicales réelles montre des résultats cohérents avec les connaissances médicales. / This thesis addresses statistical shape analysis, in the context of medical imaging. In the field of medical imaging, shape analysis is used to describe the morphological variability of various organs and tissues. Our focus in this thesis is on the construction of a generative and discriminative, compact and non-linear model, suitable to the representation of shapes. This model is evaluated in the context of the study of a population of Alzheimer's disease patients and a population of healthy controls. Our principal interest here is using the discriminative model to discover morphological differences that are the most characteristic and discriminate best between a given shape class and forms not belonging in that class. The theoretical innovation of our work lies in two principal points first, we propose a tool to extract discriminative difference in the context of the Support Vector Data description (SVDD) framework ; second, all generated reconstructions are anatomicallycorrect. This latter point is due to the non-linear and compact character of the model, related to the hypothesis that the data (the shapes) lie on a low-dimensional, non-linear manifold. The application of our model on real medical data shows results coherent with well-known findings in related research.

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Estudos clínicos indicam que pacientes com o diagnóstico de epilepsia generalizada idiopática (EGI) exibem anormalidades focais que envolvem o circuito tálamo-cortical no eletroencefalograma (EEG) e na neuroimagem. Objetivos: Investigar a presença de características focais nas descargas generalizadas interictais usando análise quantitativa do EEG (EEGq) e avaliar o córtex do giro do cíngulo usando múltiplas abordagens quantitativas de neuroimagem. Métodos: 75 EEGs de 64 pacientes foram analisados. A primeira espícula generalizada inequívoca foi marcada para cada descarga. Três métodos de análise de fonte geradora da atividade observada foram aplicados: transformação do dipolo em imagem (dipole source imaging-DSI), abordagem LORETA aplicada iterativamente (CLARA), e análise de dipolo equivalente de componentes independentes com análise de agrupamentos. Após processamento do EEG, 32 pacientes (18 mulheres, 32 ± 11) fizeram ressonância magnética. Foram utilizados três métodos para comparar o giro do cíngulo de pacientes e controles: morfometria baseada em voxel (VBM), análise cortical e análise de formato. Resultados: 753 descargas generalizadas foram analisadas. Usando as três técnicas, o lobo frontal foi a principal fonte das descargas (70%), seguido pelos lobos parietal e occipital (14%) e, por fim, os núcleos da base (12%). As principais fontes anatômicas das descargas generalizadas foram o córtex da porção anterior do giro do cíngulo (36%) e giro frontal medial (23%). A VBM mostrou atrofia de substância cinzenta na porção anterior do giro do cíngulo (972 mm3) e no istmo (168 mm3). Análises individuais do córtex do giro do cíngulo mostraram resultados semelhantes. Comparações de superfície mostraram anormalidades principalmente na porção posterior do giro do cíngulo (718.12 mm2). A análise de formato demonstrou uma predominância de anormalidades nas porções anterior e posterior do giro do cíngulo. Discussão: A análise de fonte não mostrou uma fonte única comum a todas as descargas generalizadas mas indicou predominância do giro do cíngulo e lobo frontal. Além disso, o estudo sugere a existência de anormalidades estruturais sutis no giro do cíngulo, principalmente nas porções anterior e posterior. / Background: Experimental evidence from animal models of absence seizures suggests a focal source for the initiation of generalized spike-and-wave (GSW) discharges. Clinical studies indicate that patients diagnosed with idiopathic generalized epilepsy (IGE) exhibit focal electroencephalographic and subtle structural abnormalities, which involve the thalamo-cortical circuitry. Aims: The objectives of the current investigation were to investigate whether interictal generalized discharges exhibit focal characteristics using qEEG analysis and to perform a comprehensive analysis of the cingulate cortex using multiple quantitative structural neuroimaging techniques. Methods: 75 EEG recordings from 64 patients were analyzed. The first unequivocally confirmed generalized spike was marked for each discharge. Three methods of source imaging analysis were applied: dipole source imaging (DSI), classical LORETA analysis recursively applied (CLARA), and equivalent dipole of independent components with cluster analysis. After EEG analysis, 32 patients (18 women, 30± 10 years) and 36 controls (18 women, 32 ±11 years) were imaged by 3 Tesla magnetic resonance (MRI). We used three models to compare cingulate gyrus of patients and the control group: voxel-based morphometry (VBM), cortical analyses and shape analyses. Results: A total of 753 GSW discharges were spatiotemporally analyzed. Source analysis using all three techniques revealed that the frontal lobe was the principal source of GSW discharges (70%), followed by the parietal and occipital lobes (14%), and the basal ganglia (12%). The main anatomical sources of the generalized discharges were the anterior cingulate cortex (36%) and the medial frontal gyrus (23%). VBM analyses of cingulate gyrus showed areas of gray matter atrophy, mainly in the anterior cingulate gyrus (972 mm3) and the isthmus (168 mm3). Individual analyses of the cingulate cortex were similar between patients with IGE and controls. Surface- based comparisons revealed abnormalities located mainly in the posterior cingulate cortex (718.12 mm2). Shape analyses demonstrated a predominance of abnormalities in the anterior and posterior portions of cingulate gyrus abnormalities. Discussion: Source analysis did not reveal a common focal source of generalized discharges. However, there was a predominance of GSW discharges originating from the cingulate gyrus and the frontal lobe. Furthermore, this study suggests that patients with IGE have structural abnormalities in the cingulate gyrus mainly localized at the anterior and posterior portions. This finding is subtle and variable among patients. / FAPEMAT: 11/16452-2

Myocardial scar, a non-viable tissue which occurs on the myocardium due to the insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Analysis of myocardial scar is important for predicting the risk of arrhythmia and locations of re-entrant circuits in patients’ hearts. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, reconstruction of the intact geometry of scar is required. Currently, 2D multi-slice late gadolinium-enhanced magnetic resonance imaging (LGEMRI) is widely used to detect and quantify myocardial scar regions of the heart. However, due to the anisotropic spatial dimensions in 2D LGE-MR images, creating scar geometry from these images results in substantial reconstruction errors. For applications requiring reconstructing the intact geometry of scar surfaces, 3D LGE-MR images are more suited as they are isotropic in voxel dimensions and have a higher resolution. While many techniques have been reported for segmentation of scar using 2D LGEMR images, the equivalent studies for 3D LGE-MRI are limited. Most of these 2D and 3D techniques are basic intensity threshold-based methods. However, due to the lack of optimum threshold (Th) value, these intensity threshold-based methods are not robust in dealing with complex scar segmentation problems. In this study, we propose an algorithm for segmentation of myocardial scar from 3D LGE-MR images based on Markov random field based continuous max-flow (CMF) method. We utilize the segmented myocardium as the region of interest for our algorithm. We evaluated our CMF method for accuracy by comparing its results to manual delineations using 3D LGE-MR images of 34 patients. We also compared the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yields a Dice similarity coefficient (DSC) of 71 +- 8.7% and an absolute volume error (|VE|) of 7.56 +- 7 cm3. Overall, the CMF method outperformed the conventional methods for almost all reported metrics in scar segmentation. We present a comparison study for scar geometries obtained from 2D vs 3D LGE-MRI. As the myocardial scar geometry greatly influences the sensitivity of risk prediction in patients, we compare and understand the differences in reconstructed geometry of scar generated using 2D versus 3D LGE-MR images beside providing a scar segmentation study. We use a retrospectively acquired dataset of 24 patients with a myocardial scar who underwent both 2D and 3D LGE-MR imaging. We use manually segmented scar volumes from 2D and 3D LGE-MRI. We then reconstruct the 2D scar segmentation boundaries to 3D surfaces using a LogOdds-based interpolation method. We use numerous metrics to quantify and analyze the scar geometry including fractal dimensions, the number-of-connected-components, and mean volume difference. The higher 3D fractal dimension results indicate that the 3D LGE-MRI produces a more complex surface geometry by better capturing the sparse nature of the scar. Finally, 3D LGE-MRI produces a larger scar surface volume (27.49 +- 20.38 cm3) than 2D-reconstructed LGE-MRI (25.07 +- 16.54 cm3).