Work is focused on main problems of segmentation in practical life taking complicated financial services showing difficulties the market is facing. Aa main stream is author describing czech insurance market where segmentation has held a prime place within media and insurers interest. On the other hand is also showing banking sector where easy and simple marketing segmentation has been adopted. After analyzing the real situation, author is taking best practice advise from real world and also recommendation on what to do next from theory.

The theoretical part covers general market segmentation, namely the marketing importance of differences among consumers, the essence of market segmentation, its main conditions and the process of segmentation, which consists of four consecutive phases - defining the market, determining important criteria, uncovering segments and developing segment profiles. The segmentation criteria, segmentation approaches, methods and techniques for the process of market segmentation are also described in the thesis. The practical part is focused on infant food market analysis and segmentation. It includes secondary as well as primary research and data analysis, which provides the basis for uncovering 3 segments, and for which I designed suitable marketing recommendations. The aim of the thesis is to analyze the infant food market, uncovering segments on this market, developing segment profiles, and proposing an appropriate marketing approach.

The aim of the thesis is the segmentation of the Czech population which is serviceable in social marketing, specifically for creation of social program with the theme of responsible consumption of alcohol. It is based on a segmented group's approach to alcohol and furthermore the segments are profiled according to their lify style. First of all in the theoretical part of the thesis there is explained why it is important to pay attention to the issue of alcohol consumption in the Czech republic and what kind of consequences of increased alcohol consumption can have for individuals, society and the state. Subsequently it is described in which programs associated with the problem of alcohol already the state was involved. Later on the segmentation in social marketing is characterized and there are explained the approaches which are used in it. In the practical part through the analytical program Data Analyzer the segmentation is performed using database of data MML - TGI provided by Median. In the segmentation there were used demographic, geographic, and especially life style criteria for detailed profiling of all segments. Segmentation outputs can be used by non-profit organizations or the National Drugs Coordinator in creating a social program, of which theme is the responsible consumption of alcohol.

La compréhension des mécanismes impliqués dans les maladies neurodégénératives ou développementales ainsi que la mise en place de nouvelles approches thérapeutiques reposent sur l’utilisation de modèles expérimentaux pertinents et de techniques d’imagerie adaptées. Dans ce contexte, l’IRM est un outil de choix pour l’exploration anatomique in vivo dans la mesure où elle permet d’effectuer un suivi longitudinal. Le succès translationnel des thérapies du laboratoire au patient repose sur une bonne caractérisation des modèles et une continuité des biomarqueurs utilisés. Or, si l'IRM est disponible en préclinique et en clinique, les outils d'analyse sont peu « génériques ». Au cours de cette thèse, en s'inspirant des travaux menés chez l'Homme, nous avons développé et validé des outils automatiques de segmentation des structures neuroanatomiques chez le Macaque. La méthode proposée repose sur la mise en registre avec l'IRM du sujet d'un atlas digital probabiliste suivi de l'optimisation d'un modèle statistique par mélanges de gaussiennes et champs aléatoires de Markov. Elle a été validée chez un ensemble de sujets sains adultes puis mise en application dans le contexte du développement néonatal normal du cerveau. Afin de poser les bases d'une évaluation permettant une comparaison des biomarqueurs IRM avec les biomarqueurs post mortem de référence, nous avons également mis au point une chaîne de traitement permettant la reconstruction 3D de volumes histologiques du cerveau de Macaque et l'avons appliqué à la caractérisation du contraste IRM au cours d'une greffe de cellules souches après lésion excitotoxique. / Understanding the mechanisms involved in neurodegenerative or developmental diseases and designing new therapeutic approaches are based on the use of relevant experimental models as well as appropriate imaging techniques. In this context, MRI is a prominent tool for in vivo investigation as it allows for longitudinal follow-up. Successful translation from bench to bedside calls for well-characterized models as well as transferable biomarkers. Yet, despite the existence of both clinical and preclinical scanners, analysis tools are hardly translational. In this work, inspired by standards developed in Humans, we've built and validated tools for the automated segmentation of neuroanatomical structures in the Macaque. This method is based on the registration of a digital probabilistic atlas followed by the fitting of a statistical model consisting of a gaussian mixture and Markov random fields. It was first validated in healthy adults and then applied to the study of neonatal brain development. Furthermore, to pave the way for comparisons with gold standard post mortem biomarkers, we developed a pipeline for the automated 3D reconstruction of histological volumes that we applied to the characterization of MRI contrast in a stem-cell graft following an excitotoxic lesion.

La planification chirurgicale repose sur l’anatomie du patient, et repose souvent sur l’analyse d’images médicales acquises avant la chirurgie. En particulier, c’est le cas pour les interventions de chirurgie pelvienne en pédiatrie, pour de nombreuses pathologies telles que des tumeurs et des malformations. Dans cette zone anatomique, en raison de sa forte vascularisation et innervation, une bonne planification chirurgicale est extrêmement importante pour éviter des lésions fonctionnelles des organes du patient, qui pourraient nuire a sa qualité de vie. En pratique clinique, la procédure standard repose sur l’analyse visuelle, coupe par coupe, des images de la région pelvienne. Cette tâche, même si elle est facilement accomplie par des radiologues experts, est très complexe et fastidieuse pour les chirurgiens, en raison de la complexité et de la variabilité des structures anatomiques et, par conséquent, de leurs images. De plus, en raison des variations anatomiques selon l’age du patient, toutes ces difficultés sont accentuées en pédiatrie et une compréhension anatomique claire est encore plus importante que pour les adultes. Pour ces raisons, il est important et utile d’être capable de fournir aux chirurgiens des modèles anatomiques 3D spécifiques aux patients, obtenus par traitement et analyse des images IRM.Dans cette thèse, nous proposons un ensemble de méthodes de segmentation d’images IRM de patients pédiatriques. Nous nous concentrons sur trois structures pelviennes importantes : les os du bassin, les vaisseaux sanguins et la vessie. Pour les os, nous proposons une méthode semi-automatique comportant une première étape de recalage de modèles osseux puis une étape de segmentation fine par modèles déformables. La principale contribution de la méthode proposée est l’introduction d’un ensemble de modèles osseux pour différentes tranches d’age, ce qui permet de prendre en compte la variabilité des os pendant la croissance. Pour les vaisseaux, nous proposons une méthode par patchs, apprentissage profond et transfert d’apprentissage, donc ne nécessitant que peu de donnes d’apprentissage. La principale contribution de ce travail est la conception d’une procédure semi-automatique pour l’extraction des patchs, qui permet a l’utilisateur de se focaliser uniquement sur les vaisseaux d’intérêt; et pour la planification chirurgicale. Pour la segmentation de la vessie, nous proposons d’utiliser une approche par modèles déformables, particulièrement robuste aux hétérogénéités de l’image et aux effets de volume partiel, souvent présents dans les images IRM pédiatriques. Toutes les méthodes proposées sont intégrées dans une plateforme logicielle libre pour le traitement d’images médicales, donnant aux chirurgiens des outils performants avec des interfacesutilisateur faciles a utiliser. De plus, nous mettons en place une stratégie de traitement et de portabilité pour la visualisation des modèles 3D du patient, permettant aux chirurgiens de générer, visualiser et partager ces modèles au sein de l’hôpital. En conclusion, les résultats obtenus avec les méthodes proposées sont quantitativement et qualitativement évalués de manière très positive par des chirurgiens pédiatriques, démontrant leurs potentialités pour l’utilisation en pratique clinique dans des procédures de planification chirurgicale. / Surgical planning relies on the patient’s anatomy, and it is often based on medical images acquired before the surgery. This is in particular the case for pelvic surgery on children, for various indications such as malformations or tumors. In this particular anatomical region, due to its high vascularization and innervation, a good surgical planning is extremely important to avoid potential functional damages to the patient’s organs that could strongly affect their quality of life. In clinical practice the standard procedure is still to visually analyze, slice by slice, the images of the pelvic region. This task, even if quite easily performed by the expert radiologists, is difficult and tedious for the surgeons due to the complexity and variability of the anatomical structures and hence their images. Moreover, due to specific anatomy depending on the age of the patient, all the difficulties of the surgical planning are emphasized in the case of children, and a clear anatomical understanding is even more important than for the adults. For these reasons, it is very important and challenging to provide the surgeons with patient-specific 3D reconstructions, obtained from the segmentation of MRI images. In this work we propose a set of segmentation tools for pelvic MRI images of pediatric patients. In particular, we focus on three important pelvic structures: the pelvic bones, the pelvic vessels and the urinary bladder. For pelvic bones, we propose a semi-automatic approach based on template registration and deformable models. The main contribution of the proposed method is the introduction of a set of bones templates for different age ranges, which allows us to take into account the bones variability during growth. For vessels segmentation, we propose a patch-based deep learning approach using transfer learning, thus requiring few training data. The main contribution of this work is the design of a semi-automatic strategy for patches extraction, which allows the user to focus only on the vessels of interest for surgical planning. For bladder segmentation, we propose to use a deformable model approach that is particularly robust to image inhomogeneities and partial volume effects, which are often present in pediatric MRI images. All the developed segmentation methods are integrated in an open-source platform for medical imaging, delivering powerful tools and user-friendly GUIs to the surgeons. Furthermore, we set up a processing and portability workflow for visualization of the 3D patient specific models, allowing surgeons to generate, visualize and share within the hospital the patient specific 3D models. Finally, the results obtained with the proposed methods are quantitatively and qualitatively evaluated by pediatric surgeons, which demonstrates their potentials for clinical use in surgical planning procedures.

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This thesis deals with the web page segmentation in a web browser. The implementation of Box Clustering Segmentation (BCS) method in JavaScript using an automated browser was created. The actual implementation consists of two main steps, which are the box extraction (leaf DOM nodes) from the browser context and their subsequent clustering based on the similarity model defined in BCS. Main result of this thesis is a functional implementation of BCS method usable for web page segmentation. The evaluation of the functionality and accuracy of the implementation is based on a comparison with a reference implementation created in Java.

Segmentation of WWW pages or page division on di erent semantics blocks is one of the disciplines of information extraction. Master's thesis deals with Vision-based Page Segmentation - VIPS method, which consist in division based on visual properties of page's elements. The method is given in context of other prominent segmentation procedures. In this work, the key steps, that this method consist of are shown and described on examples. For VIPS method it is necessary to cooperate with WWW pages rendering engine in order to obtain Document Object Model of page. The paper presents and describes four most important engines for Java programming language. The output of this work is implementation of VIPS algorithm just in Java language with usage of CSSBox core. The original algorithm implementation from Microsoft's labs is presented. The di erent development stages of library implementing VIPS method and my approach to it's solution are described. In the end of this work the work's outcome is demonstrated on several pages segmentation.

This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.

En biologie développementale, l'étude d'organismes modèles vise à comprendreles mécanismes génétiques responsables de la morphogenèse chez le vivant. Lamicroscopie confocale à fluorescence permet aujourd'hui d'observer in vivo àl'échelle de la cellule et avec une haute fréquence temporelle le développementd'organismes. Les séquences d'images 3D+t ainsi obtenues nécessitent d'avoirdes outils de traitement d'images adaptés.Dans cette thèse, nous construisons des outils dédiés à l'étude dudéveloppement de deux organismes, l'embryon de l'ascidie Phallusiamammillata et le bouton floral d'Arabidopsis thaliana.Nous développons d'abord une méthode de comparaison de segmentationsadaptée aux images de tissus épithéliaux d'organismes en développement.Nous nous appuyons sur cet outil pour valider notre seconde contribution quiporte sur la mise en place d'un outil de détection et de reconstruction demembranes cellulaires conçu afin de procéder à la segmentation de cellulesdans les images d'ascidies et d'arabidopsis.Nous utilisons ensuite l'outil de segmentation de membranes précédemmentintroduit pour construire une stratégie de recalage spatial inter-individusappliquée aux embryons d'ascidies. Enfin, nous élaborons une stratégie derecalage spatio-temporel inter-individus appliquée à des séquences d'images 3Dde méristèmes floraux / In developmental biology, the study of model organisms aims for theunderstanding of genetic mechanisms responsible of morphogenesis. Today,fluorescent confocal microscopy is a means for in vivo imaging of developingorganisms at cell level with a high spatio-temporal resolution. To handle such3D+t image sequences, adapted computer-assisted methods are highlydesirable.In this thesis, we build dedicated tools for the study of two developingorganisms, the ascidian Phallusia mammillata's embryo and the Arabidopsisthaliana's floral meristem.We first develop a method for segmentation comparison adapted to developingorganism epithelial tissue images. This tool is then used to validate our secondcontribution that is about the development of a cell membranes detection andreconstruction tool for cell shape segmentation process applied to ascidian andarabidopsis images.We then use the previously introduced membrane detection tool to build aninter-individual spatial registration strategy applied to ascidian embryo images.Finally, we develop an inter-individual spatio-temporal registration strategyapplied to 3D image sequences of arabidopsis floral meristems