FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / O auxÃlio ao diagnÃstico atravÃs da visualizaÃÃo de imagens mÃdicas vÃm sendo utilizado em diversas Ãreas da Medicina tais como pneumologia, cardiologia, traumatologia, neurologia, dentre outras. Na Ãrea da cardiologia, vÃrias aplicaÃÃes clÃnicas tÃm sido propostas para a anÃlise de doenÃas cardÃacas atravÃs da quantificaÃÃo e avaliaÃÃo da dessincronia ventricular esquerda durante a deformaÃÃo do mÃsculo cardÃaco (miocÃrdio). Existem, atualmente, duas tÃcnicas utilizadas na aferiÃÃo da deformaÃÃo miocÃrdica em duas dimensÃes: Doppler Tecidual (DT) e Strain 2D (St2D). A primeira tÃcnica possui desvantagens quanto à dependÃncia do Ãngulo de insonaÃÃo do transdutor durante o exame ecocardiogrÃfico, diminuindo a chance de reprodutibilidade do resultado das mediÃÃes entre especialistas. A segunda tÃcnica, recentemente introduzida e tambÃm chamada de Speckle Tracking, consiste no acompanhamento de marcadores acÃsticos naturais existentes na imagem produzida pelo ultrassom. Neste sentido, vÃ-se a importÃncia do estudo de tÃcnicas para rastrear esses marcadores acÃsticos. A presente dissertaÃÃo realiza uma anÃlise comparativa entre oito algoritmos de estimaÃÃo de deslocamento baseados na tÃcnica de Casamento de Blocos (CB) e trÃs algoritmos baseados na tÃcnica de Fluxo Ãptico (FO), que sÃo as duas atuais tÃcnicas amplamente citadas na literatura. A anÃlise à realizada mediante vÃdeos sintÃticos e vÃdeos mÃdicos de exames ecocardiogrÃficos. A avaliaÃÃo das tÃcnicas em vÃdeos sintÃticos à realizada quanto à trajetÃria e à deformaÃÃo. Jà a avaliaÃÃo em vÃdeos de exames ecocardiogrÃficos à realizada quanto Ãs curvas e taxas de deformaÃÃo. Na anÃlise da trajetÃria sÃo aplicadas duas mÃtricas de avaliaÃÃo das tÃcnicas: correlaÃÃo mÃdia e erro quadrÃtico mÃdio. Para a anÃlise das curvas e das taxas de deformaÃÃo a mÃtrica usada à o valor do erro quadrÃtico mÃdio em relaÃÃo à deformaÃÃo global (global strain) do miocÃrdio. Os resultados indicam que o desempenho idÃntico de alguns estimadores de deslocamento os reduzem de oito para seis algoritmos. A tÃcnica de CB mostra-se viÃvel para o rastreamento de marcas acÃsticas mas à dependente das dimensÃes adotadas nos blocos. Em relaÃÃo Ãs tÃcnicas de FO, o algoritmo de Lucas e Kanade Piramidal à o que obtÃm melhor resultado nos testes realizados, produzindo curvas de deformaÃÃo global com erro mÃdio de 0,47%, enquanto os valores de erro dos outros algoritmos de FO estÃo em torno de 10%. No caso, os erros dos estimadores de CB variam de 1% a 16%. / Aided diagnosis by visualization of medical images has been used in several medical fields such as pulmonology, cardiology, traumatology, neurology, and others. In cardiology, several clinical applications have been proposed for the analysis of heart disease by quantification and evaluation of ventricular dyssynchrony during deformation of the heart muscle (myocardium). There are currently two techniques used in the measurement of myocardial deformation in two dimensions: Tissue Doppler and 2D Strain. The first technique has drawbacks regarding the dependence on the angle of insonation of the transducer during the echocardiographic examination, which reduce the chance of reproducibility of measurements among experts. The second technique, recently introduced and also called Speckle Tracking, consists of tracking the natural acoustic markers in the image produced by ultrasound. In this sense we see the importance of studying techniques to track these acoustic markers. This thesis performs a comparative analysis of eight algorithms from time-delay estimators based on the block matching technique and three algorithms based on the optical flow technique, which are the two current techniques widely presented in the literature. The analysis is performed using synthetic videos and medical videos from echocardiographic examinations. The evaluation of the techniques in synthetic videos is performed on the trajectory and deformation. The assessment in echocardiographic videos is held regarding the strain curves and strain rates. In the analysis of the trajectory are applied two metrics for evaluating techniques: mean correlation and mean square error. For the analysis of strain curves and of strain rate the measure used is the value of the mean square error relative to global strain of myocardium. The results indicate that the identical performance of some estimators reduce the time-delay estimators from eight to six algorithms. The block matching technique appears to be a viable technique for tracking acoustic marks but is dependent on the dimensions adopted in the blocks. Regarding optical flow techniques, the Lucas and Kanade Pyramidal algorithm is the one which gets the best results in the tests performed herein and produce global strain curves average error of 0.47 %, while the error values of the other optical flow algorithms are around 10 %. In case, the block matching time-delay estimators errors vary from 1% to 16%.

This study aims to investigate how dimensionality reduction of neuroimaging data prior to training support vector machines (SVMs) affects the classification accuracy of bipolar disorder. This study uses principal component analysis (PCA) for dimensionality reduction. An open source data set of 19 bipolar and 31 control structural magnetic resonance imaging (sMRI) samples was used, part of the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study funded by the NIH Roadmap Initiative aiming to foster breakthroughs in the development of novel treatments for neuropsychiatric disorders. The images underwent smoothing, feature extraction and PCA before they were used as input to train SVMs. 3-fold cross-validation was used to tune a number of hyperparameters for linear, radial, and polynomial kernels. Experiments were done to investigate the performance of SVM models trained using 1 to 29 principal components (PCs). Several PC sets reached 100% accuracy in the final evaluation, with the minimal set being the first two principal components. Accumulated variance explained by the PCs used did not have a correlation with the performance of the model. The choice of kernel and hyperparameters is of utmost importance as the performance obtained can vary greatly. The results support previous studies that SVM can be useful in aiding the diagnosis of bipolar disorder, and that the use of PCA as a dimensionality reduction method in combination with SVM may be appropriate for the classification of neuroimaging data for illnesses not limited to bipolar disorder. Due to the limitation of a small sample size, the results call for future research using larger collaborative data sets to validate the accuracies obtained. / Syftet med denna studie är att undersöka hur dimensionalitetsreduktion av neuroradiologisk data före träning av stödvektormaskiner (SVMs) påverkar klassificeringsnoggrannhet av bipolär sjukdom. Studien använder principalkomponentanalys (PCA) för dimensionalitetsreduktion. En datauppsättning av 19 bipolära och 31 friska magnetisk resonanstomografi(MRT) bilder användes, vilka tillhör den öppna datakällan från studien UCLA Consortium for Neuropsychiatric Phenomics LA5c som finansierades av NIH Roadmap Initiative i syfte att främja genombrott i utvecklingen av nya behandlingar för neuropsykiatriska funktionsnedsättningar. Bilderna genomgick oskärpa, särdragsextrahering och PCA innan de användes som indata för att träna SVMs. Med 3-delad korsvalidering inställdes ett antal parametrar för linjära, radiala och polynomiska kärnor. Experiment gjordes för att utforska prestationen av SVM-modeller tränade med 1 till 29 principalkomponenter (PCs). Flera PC uppsättningar uppnådde 100% noggrannhet i den slutliga utvärderingen, där den minsta uppsättningen var de två första PCs. Den ackumulativa variansen över antalet PCs som användes hade inte någon korrelation med prestationen på modellen. Valet av kärna och hyperparametrar är betydande eftersom prestationen kan variera mycket. Resultatet stödjer tidigare studier att SVM kan vara användbar som stöd för diagnostisering av bipolär sjukdom och användningen av PCA som en dimensionalitetsreduktionsmetod i kombination med SVM kan vara lämplig för klassificering av neuroradiologisk data för bipolär och andra sjukdomar. På grund av begränsningen med få dataprover, kräver resultaten framtida forskning med en större datauppsättning för att validera de erhållna noggrannheten.