Imagens da anatomia interna são essenciais para as práticas médicas. Estabelecer correlação entre elas, é um importante procedimento para diagnóstico e tratamento. Nessa dissertação, é proposta uma abordagem para correlacionar dados multidimensionais de mesma modalidade de aquisição baseando-se somente nas informações de intensidade de pixels e voxels. O trabalho foi dividido em duas fases de implementação. Na primeira, foi explorado o problema de similaridade entre imagens médicas usando a perspectiva de análise de qualidade de imagem. Isso levou ao desenvolvimento de uma técnica de dois passos que estabelece um equilíbrio entre a velocidade de processamento e precisão de duas abordagens conhecidas. Avaliou-se a qualidade e aplicabilidade do algoritmo e, na segunda fase, o método foi estendido para analisar similaridade e encontrar a localização de uma imagem arbitrária (2D) em um volume (3D). A solução minimiza o número virtualmente infinito de possíveis orientações transversais e usa otimizações para reduzir a carga de trabalho e entregar resultados precisos. Uma visualização tridimensional volumétrica funde o volume (3D) com a imagem (2D) estabelecendo uma correspondência entre os dados. Uma análise experimental demonstrou que, apesar da complexidade computacional do algoritmo, o uso de amostragem, tanto na imagem quanto no volume, permite alcançar um bom equilíbrio entre desempenho e precisão, mesmo quando realizada com conjuntos de dados de baixa intensidade de gradiente. / Images of the inner anatomy are essential for clinical practice. To establish a correlation between them is an important procedure for diagnosis and treatment. In this thesis, we propose an approach to correlate within-modality 2D and 3D data from ordinary acquisition protocols based solely on the pixel/voxel information. The work was divided into two development phases. First, we explored the similarity problem between medical images using the perspective of image quality assessment. It led to the development of a 2-step technique that settles the compromise between processing speed and precision of two known approaches. We evaluated the quality and applicability of the 2-step and, in the second phase, we extended the method to use similarity analysis to, given an arbitrary slice image (2D), find the location of this slice within the volume data (3D). The solution minimizes the virtually infinite number of possible cross section orientations and uses optimizations to reduce the computational workload and output accurate results. The matching is displayed in a volumetric three-dimensional visualization fusing the 3D with the 2D. An experimental analysis demonstrated that despite the computational complexity of the algorithm, the use of severe data sampling allows achieving a great compromise between performance and accuracy even when performed with low gradient intensity datasets.

Made available in DSpace on 2015-04-11T14:03:07Z (GMT). No. of bitstreams: 1 Luiz Wanderley Nagata Balduino.pdf: 5703039 bytes, checksum: c8415ccb6f3d173592fe222f1118e05d (MD5) Previous issue date: 2006-11-09 / Fundação de Amparo à Pesquisa do Estado do Amazonas / In the present work it is shown the proposal and the development of extraction of images, as well as some improvements proposed in other segmentation techniques and classification, with objective of recognizing pathology in medical images of the blood. The automatic identification of those diseases makes possible the most necessary diagnosis and with larger speed. The approach of the present work follows the stages of segmentation of the images, extraction of the visual characteristics and the classification of the pathology. In the segmentation stage the separation technique is applied by color for obtaining of the red blood cells borders, of the leukocytes and of the platelets. The extraction is the stage where the cells are separate from the image, this extraction is accomplished through proposed procedures and developed for this aim at. Finally, the classification is executed through similarities among the images of the leucócitos, using statistical techniques among spatiograms. The results were shown promising as the viability of the application of the methodology in support systems to the diagnosis for images, due to simplicity of the application of the same ones as well as of the obtained results. / No presente trabalho mostra-se a proposta e o desenvolvimento de extração de imagens, bem como algumas melhorias propostas em outras técnicas de segmentação e classificação, com objetivo de reconhecer hematopatologias em imagens médicas do sangue. A identificação automática dessas doenças viabiliza o diagnóstico mais preciso e com maior rapidez. A abordagem do presente trabalho segue as etapas de segmentação das imagens, extração das características visuais e a classificação da patologia. Na etapa de segmentação é aplicada a técnica de separação por cor para obtenção das bordas de hemácias, dos leucócitos e das plaquetas. A extração é a etapa onde as células são separadas da imagem, esta extração é realizada através de procedimentos propostos e desenvolvidos para este objetivo. Por fim, a classificação é executada por meio de similaridades entre as imagens dos leucócitos, usando técnicas estatísticas entre spatiograms. Os resultados mostraram-se promissores quanto a viabilidade da aplicação da metodologia em sistemas de apoio ao diagnóstico por imagens, devido a simplicidade da aplicação das mesmas bem como dos resultados obtidos.

Este trabalho apresenta o estudo e implementação de um sistema para vinculação e visualização de imagens de ressonância magnética nuclear e tomografia computadorizada a partir de um sistema de informação em radiologia (RIS), possibilitando a recuperação e disponibilização dos exames (laudos e imagens), através de rede \"ethernet\", para visualização a partir de uma interface desenvolvida para \"browser\". Os exames podem ser recuperados através do número de registro (RGHC) ou do nome do paciente. As imagens utilizadas no trabalho estão nos padrões DICOM 3.0 (Digital Imaging and Communications in Medicine) e JPEG (Joint Photographic Experts Group). Para a vinculação dos exames que possuem suas imagens em JPEG foi desenvolvida uma interface para inclusão das informações necessárias que garantem a consistência deste processo. Para os exames que possuem suas imagens em formato DICOM 3.0 as informações foram extraídas automaticamente dos cabeçalhos e armazenadas no banco de dados. O sistema possui uma interface amigável ao usuário, podendo ser rapidamente incorporada ao projeto de um PACS (Picture Archiving and Communication System). A implementação foi idealizada para servir ao Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP/USP), com base no seu sistema de informação em radiologia. Os resultados demonstram que o tempo de retorno das imagens é clinicamente satisfatório e considerado bom pela avaliação qualitativa dos médicos. / This work presents the study and implementation of a system aiming the indexing and visualization of the images of nuclear magnetic resonance and computerized tomography from a radiology information system (RIS), allowing the retrieval and availability of the exams (results and images), through an ethernet net for visualization beginning with an interface developed to a browser. The exams may be recovered either through their register number (RGHC) or the patient\'s name. The images employed in the work follow the DICOM 3.0 (Digital Imaging and Communications in Medicine) and JPEG (Joint Photographic Experts Group) patterns. For the indexing of the exams that present images in JPEG, an interface was developed to include the information required to guarantee the consistency of the process. For the exams presenting the DICOM 3.0 format, information was extracted automatically from the heading and filed in the database. The system has a friendly interface for the user, it may be rapidly incorporated to the project of an PACS (Picture Archiving and Communication System). The implementation was idealized to serve the Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, of the Universidade de São Paulo (HCFMRP/USP), based on its radiology information system (RIS). The results demonstrated that the time of retrieval of the images is satisfactory and considered good by evaluation qualified of the doctors.

Neste trabalho é apresentada a implantação de um servidor de imagens médicas com a implementação e integração de módulos para recuperação textual e baseada em conteúdo para o Serviço de Radiodiagnóstico do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto (HCFMRP) da Universidade de São Paulo (USP). O sistema permite a aquisição, gerenciamento, armazenamento e disponibilização das informações dos pacientes, seus exames, laudos e imagens através da internet. Os exames radiológicos e suas respectivas imagens podem ser recuperados por informações textuais ou por similaridade do conteúdo pictório das imagens. As imagens utilizadas são de ressonância magnética nuclear e tomografia computadorizada e são geradas no padrão DICOM 3.0. O sistema foi desenvolvido contemplando tecnologias para Web com interfaces amigáveis para recuperação das informações. Ele é composto por três módulos integrados, sendo o servidor de imagens, o módulo de consulta textual e o módulo de consulta por similaridade. Os resultados apresentados indicam que as imagens são gerenciadas e armazenadas corretamente, bem como o tempo de retorno das imagens é clinicamente satisfatório, tanto para a consulta textual como para a consulta por similaridade. As avaliações da recuperação por similaridade apresentam que o extrator escolhido pode ser considerado relevante para separar as imagens por região anatômica. / This work introduces an the development of a server of medical images with the implementation and integration of modules to query/retrieve text information and content-based to Radiology Service of Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto (HCFMRP) at Universidade de São Paulo (USP). The system allows the acquisition, management, archiving and availability of the patients information, theirs exams, results and images through of internet. The radiological exams and theirs respectives images can be retrieved by text information or similarity of pictorial content of images. Images are from magnetic resonance nuclear and computadorized tomography and are given using DICOM 3.0 protocol. The system has been developed considering web technologies with friendly interfaces to retrieval of information. It is composed by three integrated modules: the image server module, the query text module and query by similarity module. Results show that images are managed and archived exactly, retrieval time of images is clinically satisfactory, considering both the text query as well as the query by similarity. The evaluation of the retrieval by similarity shows the chosen extractor can be considerated relevant to separate the images by anatomic region.

Sistemas de recuperação de imagens por conteúdo (Content-based image retrieval { CBIR) e de classificação dependem fortemente de vetores de características que são extraídos das imagens considerando critérios visuais específicos. É comum que o tamanho dos vetores de características seja da ordem de centenas de elementos. Conforme se aumenta o tamanho (dimensionalidade) do vetor de características, também se aumentam os graus de irrelevâncias e redundâncias, levando ao problema da \"maldição da dimensionalidade\". Desse modo, a seleção das características relevantes é um passo primordial para o bom funcionamento de sistemas CBIR e de classificação. Nesta tese são apresentados novos métodos de seleção de características baseados em algoritmos genéticos (do inglês genetic algorithms - GA), visando o aprimoramento de consultas por similaridade e modelos de classificação. A família Fc (\"Fitness coach\") de funções de avaliação proposta vale-se de funções de avaliação de ranking, para desenvolver uma nova abordagem de seleção de características baseada em GA que visa aprimorar a acurácia de sistemas CBIR. A habilidade de busca de GA considerando os critérios de avaliação propostos (família Fc) trouxe uma melhora de precisão de consultas por similaridade de até 22% quando comparado com métodos wrapper tradicionais para seleção de características baseados em decision-trees (C4.5), naive bayes, support vector machine, 1-nearest neighbor e mineração de regras de associação. Outras contribuições desta tese são dois métodos de seleção de características baseados em filtragem, com aplicações em classificação de imagens, que utilizam o cálculo supervisionado da estatística de silhueta simplificada como função de avaliação: o silhouette-based greedy search (SiGS) e o silhouette-based genetic algorithm search (SiGAS). Os métodos propostos superaram os métodos concorrentes na literatura (CFS, FCBF, ReliefF, entre outros). É importante também ressaltar que o ganho em acurácia obtido pela família Fc, e pelos métodos SiGS e SiGAS propostos proporcionam também um decréscimo significativo no tamanho do vetor de características, de até 90% / Content-based image retrieval (CBIR) and classification systems rely on feature vectors extracted from images considering specific visual criteria. It is common that the size of a feature vector is of the order of hundreds of elements. When the size (dimensionality) of the feature vector is increased, a higher degree of redundancy and irrelevancy can be observed, leading to the \"curse of dimensionality\" problem. Thus, the selection of relevant features is a key aspect in a CBIR or classification system. This thesis presents new methods based on genetic algorithms (GA) to perform feature selection. The Fc (\"Fitness coach\") family of fitness functions proposed takes advantage of single valued ranking evaluation functions, in order to develop a new method of genetic feature selection tailored to improve the accuracy of CBIR systems. The ability of the genetic algorithms to boost feature selection by employing evaluation criteria (fitness functions) improves up to 22% the precision of the query answers in the analyzed databases when compared to traditional wrapper feature selection methods based on decision-tree (C4.5), naive bayes, support vector machine, 1-nearest neighbor and association rule mining. Other contributions of this thesis are two filter-based feature selection algorithms for classification purposes, which calculate the simplified silhouette statistic as evaluation function: the silhouette-based greedy search (SiGS) and the silhouette-based genetic algorithm search (SiGAS). The proposed algorithms overcome the state-of-the-art ones (CFS, FCBF and ReliefF, among others). It is important to stress that the gain in accuracy of the proposed methods family Fc, SiGS and SIGAS is allied to a significant decrease in the feature vector size, what can reach up to 90%

This thesis deals with a graph cut approach for segmentation of the anatomical structures in volumetric medical images. The method used requires some voxels to be a priori identified as object or background seeds. The goal of this thesis is implementation of the graph cut method and construction of an interactive tool for segmentation. Selected metod's behaviour is examined on two datasets with manually-guided segmentation results. Testing is in one case focused on the influence of method parameters on segmentation results, while in the other deals with method tolerance towards various signal-to-noise and contrast-to-noise ratios on input. To assess the consistency of a given segmentation with the ground-truth the F-measure is used.

Dans le monde de la recherche, l’analyse du signal et plus particulièrement d’image, est un domaine très actif, de par la variété des applications existantes, avec des problématiques telles que la compression de données, la vidéo-surveillance ou encore l’analyse d’images médicales pour ne prendre que quelques exemples. Le mémoire s’inscrit dans ce dernier domaine particulièrement actif. Le nombre d’appareils d’acquisition existant ainsi que le nombre de clichés réalisés, entraînent la production d’une masse importante d’informations à traiter par les praticiens. Ces derniers peuvent aujourd’hui être assistés par l’outil informatique. Dans cette thèse, l’objectif est l’élaboration d’un système d’aide au diagnostic, fondé sur l’analyse conjointe, et donc la comparaison d’images médicales. Notre approche permet de détecter des évolutions, ou des tissus aberrants dans un ensemble donné, plutôt que de tenter de caractériser, avec un très fort a priori, le type de tissu cherché.Cette problématique permet d’appréhender un aspect de l’analyse du dossier médical d’un patient effectuée par les experts qui est l’étude d’un dossier à travers le suivi des évolutions. Cette tâche n’est pas aisée à automatiser. L’œil humain effectue quasi-automatiquement des traitements qu’il faut reproduire. Avant de comparer des régions présentes sur deux images, il faut déterminer où se situent ces zones dans les clichés. Toute comparaison automatisée de signaux nécessite une phase de recalage, un alignement des composantes présentes sur les clichés afin qu’elles occupent la même position sur les deux images. Cette opération ne permet pas, dans le cadre d’images médicales, d’obtenir un alignement parfait des tissus en tous points, elle ne peut que minimiser les écarts entre tissus. La projection d’une réalité 3D sur une image 2D entraîne des différences liées à l’orientation de la prise de vue, et ne permet pas d’analyser une paire de clichés par une simple différence entre images. Différentes structurations des clichés ainsi que différents champs de déformation sont ici élaborés afin de recaler les images de manière efficace.Après avoir minimisé les différences entre les positions sur les clichés, l’analyse de l’évolution des tissus n’est pas menée au niveau des pixels, mais à celui des tissus eux-mêmes, comme le ferait un praticien. Afin de traiter les clichés en suivant cette logique, les images numériques sont réinterprétées, non plus en pixels de différentes luminosités, mais en motifs représentatifs de l’ensemble de l’image, permettant une nouvelle décomposition des clichés, une décomposition parcimonieuse. L’atout d’une telle représentation est qu’elle permet de mettre en lumière un autre aspect du signal, et d’analyser sous un angle nouveau, les informations nécessaires à l’aide au diagnostic.Cette thèse a été effectuée au sein du laboratoire LIPADE de l’Université Paris Descartes (équipe SIP, spécialisée en analyse d’images) en collaboration avec la Société Fenics (concepteur de stations d’aide au diagnostic pour l’analyse de mammographies) dans le cadre d’un contrat Cifre. / In the scientific world, signal analysis and especially image analysis is a very active area, due to the variety of existing applications, with issues such as file compression, video surveillance or medical image analysis. This last area is particularly active. The number of existing devices and the number of pictures taken, cause the production of a large amount of information to be processed by practitioners. They can now be assisted by computers.In this thesis, the problem addressed is the development of a computer diagnostic aided system based on conjoint analysis, and therefore on the comparison of medical images. This approach allows to look for evolutions or aberrant tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of fabric sought.This problem allows to apprehend an aspect of the analysis of medical file performed by experts which is the study of a case through the comparison of evolutions.This task is not easy to automate. The human eye performs quasi-automatically treatments that we need to replicate.Before comparing some region on the two images, we need to determine where this area is located on both pictures. Any automated comparison of signals requires a registration phase, an alignment of components present on the pictures, so that they occupy the same space on the two images. Although the characteristics of the processed images allow the development of a smart registration, the projection of a 3D reality onto a 2D image causes differences due to the orientation of the tissues observed, and will not allow to analyze a pair of shots with a simple difference between images. Different structuring of the pictures and different deformation fields are developed here to efficiently address the registration problem.After having minimized the differences on the pictures, the analysis of tissues evolution is not performed at pixels level, but the tissues themselves, as will an expert. To process the images in this logic, they will be reinterpreted, not as pixels of different brightness, but as patterns representative of the entire image, enabling a new decomposition of the pictures. The advantage of such a representation is that it allows to highlight another aspect of the signal, and analyze under a new perspective the information necessary to the diagnosis aid.This thesis has been carried out in the LIPADE laboratory of University Paris Descartes (SIP team, specialized in image analysis) and in collaboration with the Society Fenics (designer of diagnosis aid stations in the analysis of mammograms) under a Cifre convention. The convergence of the research fields of those teams led to the development of this document.

This Master thesis focuses on generative models for medical patient data for radiation therapy. The objective with the project is to implement and investigate the characteristics of a Variational Autoencoder applied to this diverse and versatile data. The questions this thesis aims to answer are: (i) whether the VAE can capture salient features of medical image data, and (ii) if these features can be used to compare similarity between patients. Furthermore, (iii) if the VAE network can successfully reconstruct its input and lastly (iv) if the VAE can generate artificial data having a reasonable anatomical appearance. The experiments carried out conveyed that the VAE is a promising method for feature extraction, since it appeared to ascertain similarity between patient images. Moreover, the reconstruction of training inputs demonstrated that the method is capable of identifying and preserving anatomical details. Regarding the generative abilities, the artificial samples generally conveyed fairly realistic anatomical structures. Future work could be to investigate the VAEs ability to generalize, with respect to both the amount of data and probabilistic considerations as well as probabilistic assumptions. / Fokuset i denna masteruppsats är generativa modeller för patientdata från strålningsbehandling. Syftet med projektet är att implementera och undersöka egenskaperna som en “Variational Autoencoder” (VAE) har på denna typ av mångsidiga och varierade data. Frågorna som ska besvaras är: (i) kan en VAE fånga särdrag hos medicinsk bild-data, och (ii) kan dessa särdrag användas för att jämföra likhet mellan patienter. Därutöver, (iii) kan VAE-nätverket återskapa sin indata väl och slutligen (iv) kan en VAE skapa artificiell data med ett rimligt anatomiskt utseende. De experiment som utfördes pekade på att en VAE kan vara en lovande metod för att extrahera framtydande drag hos patienter, eftersom metoden verkade utröna likheter mellan olika patienters bilder. Dessutom påvisade återskapningen av träningsdata att metoden är kapabel att identifiera och bevara anatomiska detaljer. Vidare uppvisade generellt den artificiellt genererade datan, en realistisk anatomisk struktur. Framtida arbete kan bestå i att undersöka hur väl en VAE kan generalisera, med avseende på både mängd data som krävs och sannolikhetsteorietiska avgränsningar och antaganden.

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This research project presents a study of the fixation of screws in augmented and non-augmented cancellous bone at a microscopic scale. It is estimated that somewhere close to one million screws are failing each year. Therefore, the aim is to identify the key parameters affecting screw pull-out in order to improve screw fixation in cancellous bone, and hence screw design. The background for this study comes from work by Stryker, comparing screw pull-out from augmented and non-augmented cancellous bone, where a few cases of screw pull-out gave better results without bone augmentation. This is contrary to most evidence and the hypothesis to explain these results is that the screw pull-out from cancellous bone could be strongly affected by the cancellous bone micro architecture. The effect of the influence of the screw’s initial position was first verified with 2D finite element (FE) models of screw pull-out from simplified cancellous bone models. The results showed a force reaction variation up to 28% with small change in position. The hypothesis was then tested with 3D FE models of screw pull-out from more complex cancellous bone models with different volume fractions. Three volume fractions were tested and again the effects were confirmed, but only in models with the lower volume fraction. A variation up to 30% of the force reaction was observed. The 3D simplified cancellous bone models with 5.3% volume fraction were also used to study the influence of augmentation using calcium phosphate cement. A significant improvement of the screw holding power (almost 2 times) as well as an important diminution of the variability of the pull-out force due to the screw initial position was found. Other augmentation geometries were used to model cement. They all showed an increase of the screw pull-out force reaction with an increase of the cement volume. Validation of FE results was achieved by comparing screw pull-out from a cadaver cancellous bone and the FE model constructed from the same bone sample. New studies were then carried out from the cadaver cancellous bone model. The first study examined the screw initial position influence with cancellous and cortical screws and again showed that there is a strong correlation between screw pull-out stiffness and bone volume fraction. The cortical screw showed improved performance over the cancellous screw. Augmentation cases were explored using three bone samples with a range of volume fractions obtained from different sites within the cadaver bone sample. The cancellous screw was tested with 3 types of augmentation and the cortical screw was tested with one augmentation in these 3 samples. The results showed each time a significant improvement of stiffness with augmentation but when compared with the effect of volume variation inside the bone sample, it appeared that the improvement of stiffness from augmentation might not cover the loss in stiffness from a small change in bone structure. Finally, screw design parameters were investigated, as cortical screws seemed to give as good or better stiffness results than cancellous screw. The thread pitch, the thread angle and the core diameter were analysed independently and it appeared that the most important parameter was the thread pitch with an improvement of the stiffness of +46% for cancellous screws with a smaller thread pitch. The two other factors studied (core diameter and thread angle) showed somewhat stiffer results but with a relatively small influence (less than 10%). From this study, the best screw for use in cancellous bone could be a cortical screw (diameter and pitch) with thread angles similar to a cancellous screw.