O objetivo deste trabalho é apresentar um novo método para a remoção do ruído Poisson em imagens de mamografia digital adquiridas com baixa dosagem de radiação. Sabe-se que a mamografia por raios X é o exame mais eficiente para a detecção precoce do câncer de mama, aumentando consideravelmente as chances de cura da doença. No entanto, a radiação absorvida pela paciente durante o exame ainda é um problema a ser tratado. Estudos indicam que a exposição à radiação pode induzir a formação do câncer em algumas mulheres radiografadas. Apesar desse número ser significativamente baixo em relação ao número de mulheres que são salvas pelo exame, existe a necessidade do desenvolvimento de meios que viabilizem a diminuição da dose de radiação empregada. No entanto, uma redução na dose de radiação piora a qualidade da imagem pela diminuição da relação sinal-ruído, prejudicando o diagnóstico médico e a detecção precoce da doença. Nesse sentido, a proposta deste trabalho é apresentar um método para a filtragem do ruído Poisson que é adicionado às das imagens mamográficas quando adquiridas com baixa dosagem de radiação, fazendo com que ela apresente qualidade equivalente àquela adquirida com a dose padrão de radiação. O algoritmo proposto foi desenvolvido baseado em adaptações de algoritmos bem estabelecidos na literatura, como a filtragem no domínio Wavelet, aqui usando o Shrink-thresholding (WTST), e o Block-matching and 3D Filtering (BM3D). Os resultados obtidos com imagens mamográficas adquiridas com phantom e também imagens clínicas, mostraram que o método proposto é capaz de filtrar o ruído adicional incorporado nas imagens sem perda aparente de informação. / The aim of this work is to present a novel method for removing the Poisson noise in digital mammography images acquired with reduced radiation dose. It is known that the X-ray mammography is the most effective exam for early detection of breast cancer, greatly increasing the chances of healing the disease. However, the radiation absorbed by the patient during the exam is still a problem to be treated. Some studies showed that mammography can induce breast cancer in a few women. Although this number is significantly low compared to the number of women who are saved by the exam, it is important to develop methods to enable the reduction of the radiation dose used in the exam. However, dose reduction led to a decrease in image quality by means of the signal to noise ratio, impairing medical diagnosis and the early detection of the disease. In this sense, the purpose of this study is to propose a new method to reduce Poisson noise in mammographic images acquired with low radiation dose, in order to achive the same quality as those acquired with the standard dose. The method is based on well established algorithms in the literature as the filtering in Wavelet domain, here using Shrink-thresholding (WTST) and the Block-matching and 3D Filtering (BM3D). Results using phantom and clinical images showed that the proposed algorithm is capable of filtering the additional noise in images without apparent loss of information.

O objetivo deste trabalho é apresentar um novo método para a remoção do ruído Poisson em imagens de mamografia digital adquiridas com baixa dosagem de radiação. Sabe-se que a mamografia por raios X é o exame mais eficiente para a detecção precoce do câncer de mama, aumentando consideravelmente as chances de cura da doença. No entanto, a radiação absorvida pela paciente durante o exame ainda é um problema a ser tratado. Estudos indicam que a exposição à radiação pode induzir a formação do câncer em algumas mulheres radiografadas. Apesar desse número ser significativamente baixo em relação ao número de mulheres que são salvas pelo exame, existe a necessidade do desenvolvimento de meios que viabilizem a diminuição da dose de radiação empregada. No entanto, uma redução na dose de radiação piora a qualidade da imagem pela diminuição da relação sinal-ruído, prejudicando o diagnóstico médico e a detecção precoce da doença. Nesse sentido, a proposta deste trabalho é apresentar um método para a filtragem do ruído Poisson que é adicionado às das imagens mamográficas quando adquiridas com baixa dosagem de radiação, fazendo com que ela apresente qualidade equivalente àquela adquirida com a dose padrão de radiação. O algoritmo proposto foi desenvolvido baseado em adaptações de algoritmos bem estabelecidos na literatura, como a filtragem no domínio Wavelet, aqui usando o Shrink-thresholding (WTST), e o Block-matching and 3D Filtering (BM3D). Os resultados obtidos com imagens mamográficas adquiridas com phantom e também imagens clínicas, mostraram que o método proposto é capaz de filtrar o ruído adicional incorporado nas imagens sem perda aparente de informação. / The aim of this work is to present a novel method for removing the Poisson noise in digital mammography images acquired with reduced radiation dose. It is known that the X-ray mammography is the most effective exam for early detection of breast cancer, greatly increasing the chances of healing the disease. However, the radiation absorbed by the patient during the exam is still a problem to be treated. Some studies showed that mammography can induce breast cancer in a few women. Although this number is significantly low compared to the number of women who are saved by the exam, it is important to develop methods to enable the reduction of the radiation dose used in the exam. However, dose reduction led to a decrease in image quality by means of the signal to noise ratio, impairing medical diagnosis and the early detection of the disease. In this sense, the purpose of this study is to propose a new method to reduce Poisson noise in mammographic images acquired with low radiation dose, in order to achive the same quality as those acquired with the standard dose. The method is based on well established algorithms in the literature as the filtering in Wavelet domain, here using Shrink-thresholding (WTST) and the Block-matching and 3D Filtering (BM3D). Results using phantom and clinical images showed that the proposed algorithm is capable of filtering the additional noise in images without apparent loss of information.

Fast Block Matching (FBM) algorithms for video compression are well suited for acceleration using parallel data-path architectures on Field Programmable Gate Arrays (FPGAs). However, designing an efficient on-chip memory subsystem to provide the required throughput to this parallel data-path architecture is a complex problem. This thesis presents a memory architecture template that can be parameterized for a given FBM algorithm, number of parallel Processing Elements (PEs), and block size. The template can be parameterized with well known exploration techniques to design efficient on-chip memory subsystems. The memory subsystems are derived for two existing FBM algorithms and are implemented on a Xilinx Virtex 4 family of FPGAs. Results show that the derived memory subsystem in the best case supports up to 27 more parallel PEs than the three existing subsystems and processes integer pixels in a 1080p video sequence up to a rate of 73 frames per second. The speculative execution of an FBM algorithm for the same number of PEs increases the number of frames processed per second by 49%.

This research presents a procedure for interactive segmentation and automatic tracking of moving objects in a video sequence. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI. The refined mask is used to model a spline template of the object to be tracked. The tracking algorithm then employs a motion model to track the template through a sequence of frames and gathers the 3D affine motion parameters of the object from each frame. The extracted template is compared with a previously stored library of 3D shapes to determine the closest 3D object. If the extracted template is completely new, it is used to model a new 3D object which is added to the library. To recreate the motion, the motion parameters are applied to the 3D object in a virtual environment. The procedure described here can be applied to industrial problems such as traffic management and material flow congestion analysis.

This research presents a procedure for interactive segmentation and automatic tracking of moving objects in a video sequence. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI. The refined mask is used to model a spline template of the object to be tracked. The tracking algorithm then employs a motion model to track the template through a sequence of frames and gathers the 3D affine motion parameters of the object from each frame. The extracted template is compared with a previously stored library of 3D shapes to determine the closest 3D object. If the extracted template is completely new, it is used to model a new 3D object which is added to the library. To recreate the motion, the motion parameters are applied to the 3D object in a virtual environment. The procedure described here can be applied to industrial problems such as traffic management and material flow congestion analysis.

Abstract This thesis presents several novel improvements to video coding algorithms, including block-based motion estimation, quantization selection, and video filtering. Most of the presented improvements are fully compatible with the standards in general use, including MPEG-1, MPEG-2, MPEG-4, H.261, H.263, and H.264. For quantization selection, new methods are developed based on the rate-distortion theory. The first method obtains locally optimal frame-level quantization parameter considering frame-wise dependencies. The method is applicable to generic optimization problems, including also motion estimation. The second method, aimed at real-time performance, heuristically modulates the quantization parameter in sequential frames improving significantly the rate-distortion performance. It also utilizes multiple reference frames when available, as in H.264. Finally, coding efficiency is improved by introducing a new matching criterion for motion estimation which can estimate the bit rate after transform coding more accurately, leading to better motion vectors. For fast motion estimation, several improvements on prior methods are proposed. First, fast matching, based on filtering and subsampling, is combined with a state-of-the-art search strategy to create a very quick and high-quality motion estimation method. The successive elimination algorithm (SEA) is also applied to the method and its performance is improved by deriving a new tighter lower bound and increasing it with a small constant, which eliminates a larger part of the candidate motion vectors, degrading quality only insignificantly. As an alternative, the multilevel SEA (MSEA) is applied to the H.264-compatible motion estimation utilizing efficiently the various available block sizes in the standard. Then, a new method is developed for refining the motion vector obtained from any fast and suboptimal motion estimation method. The resulting algorithm can be easily adjusted to have the desired tradeoff between computational complexity and rate-distortion performance. For refining integer motion vectors into half-pixel resolution, a new very quick but accurate method is developed based on the mathematical properties of bilinear interpolation. Finally, novel number theoretic transforms are developed which are best suited for two-dimensional image filtering, including image restoration and enhancement, but methods are developed with a view to the use of the transforms also for very reliable motion estimation.

Le début des années 2000 a vu la cartographie du génome humain se réaliser après 13 ans de recherche. Le défi du prochain siècle réside dans la construction du connectome humain, qui consiste à cartographier les connexions du cerveau en utilisant l’imagerie par résonance magnétique (IRM) de diffusion. Cette technique permet en effet d’étudier la matière blanche du cerveau de façon complètement non invasive. Bien que le défi soit monumental, la résolution d’une image d’IRM se situe à l’échelle macroscopique et est environ 1000 fois inférieure à la taille des axones qu’il faut cartographier. Pour aider à pallier à ce problème, ce mémoire propose une nouvelle technique de débruitage spécialement conçue pour l’imagerie de diffusion. L’algorithme Non Local Spatial and Angular Matching (NLSAM) se base sur les principes du block matching et du dictionary learning pour exploiter la redondance des données d’IRM de diffusion. Un seuillage sur les voisins angulaire est aussi réalisé à l’aide du sparse coding, où l’erreur de reconstruction en norme l2 est bornée par la variance locale du bruit. L’algorithme est aussi conçu pour gérer le biais du bruit Ricien et Chi non centré puisque les images d’IRM contiennent du bruit non Gaussien. Ceci permet ainsi d’acquérir des données d’IRM de diffusion à une plus grande résolution spatiale que présentement disponible en milieu clinique. Ce travail ouvre donc la voie à un meilleur type d’acquisition, ce qui pourrait contribuer à révéler de nouveaux détails anatomiques non discernables à la résolution spatiale présentement utilisée par la communauté d’IRM de diffusion. Ceci pourrait aussi éventuellement contribuer à identifier de nouveaux biomarqueurs permettant de comprendre les maladies dégénératives telles que la sclérose en plaques, la maladie d’Alzheimer et la maladie de Parkinson.

This document elaborates on the subject of video interlacing and its removal. It describes the interlacing of video, its history and the reasons that led to its use. The document also explains why it is necessary to remove interlacing and the basic methods that are used for it. It describes the proposed deinterlacing algorithm and its implementation, including description of inpainting and block matching. Included are also test results of both quality and speed of my deinterlacing algorithm. The final chapter describes the implementation as a console application and a DLL library.

Thesis deals with ultrasonographic contrast examinations, that are performed to assess tissue perfusion and non-invasive ultrasound method speckle tracking, overcoming the weaknesses of Doppler techniques used to scanning the movement of the tissue.

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%.