Modelos Deformáveis em Imagem Médica

Modelos deformáveis são actualmente bastante utilizados em imagem médica pois, através da utilização de princípios físicos, simulam de forma bastante satisfatória o comportamento dos objectos reais.Basicamente os modelos deformáveis são inicializados junto dos objectos a considerar, por processos automáticos ou semi-automáticos, e a aproximação para a posição final desejada é conseguida através de um processo de minimização de energia. Esta minimização de energia é verificada quando o modelo atinge o equilíbrio, entre as suas forças internas e as forças externas originadas pelos dados e por eventuais forças impostas pelo utilizador.Neste relatório são apresentados os fundamentos dos modelos deformáveis e indicados alguns exemplos de aplicação em imagem médica, nomeadamente na segmentação, no emparelhamento, no alinhamento e na reconstrução de dados 2D e 3D.Palavras-chave: Contornos activos, imagem médica, modelos deformáveis.Deformable models are currently very used in medical image since, through the use of physical principles, they simulate quite satisfactory the real objects behavior.Basically the deformable models are placed in the image near to the objects to be considered, by automatic or semi-automatic processes, and the approach to the desired final position is obtained through an energy minimization process. This energy minimization is verified when the model reaches the equilibrium, between its internal forces and the external forces originated by the data and eventual forces imposed by the user.In this report are presented the deformable models fundaments and indicated some application examples in medical imaging field, namely in segmentation, matching, alignment and in the reconstruction of 2D and 3D data.Keywords: Active contours, deformable models, medical image

Introdução à Análise de Movimento usando Visão Computacional

Pretende-se com este trabalho fazer uma introdução ao que tem vindo a ser realizado no domínio do seguimento e análise de movimento recorrendo a visão computacional.Assim no primeiro capítulo deste relatório faremos referência aos vários tipos de movimento e analisaremos as fases que compõem um sistema comum de captura e análise de movimento, descrevendo sucintamente alguns trabalhos realizados nesta área.Seguidamente, no segundo capítulo, faremos uma apresentação mais detalhada da área do seguimento e análise de movimento humano de corpo inteiro; nomeadamente, no reconhecimento da pose e do reconhecimento do andar e de gestos.Finalmente, no terceiro e último capítulo, daremos ênfase à análise de imagem médica e exemplificaremos, sumariamente, algumas das suas aplicações.With this work we intend to introduce what has been done in the domain of tracking and motion analysis by using computational vision.Therefore in the first chapter of this report we will refer the various types of motion, and analyse the steps that compose a general system of movement capture and analysis, by succinctly describing some works done in this field.Then, in the second chapter we will do a more detailed study about the area of human entire body tracking and motion analysis; namely, in pose recognition and in the recognition of gait and gestures.Finally, in the third and last chapter, emphasis will be given to the medical images analysis and we will summarily exemplify some of its applications

Physics-Based Modeling, Analysis and Animation

The idea of using physics-based models has received considerable interest in computer graphics and computer vision research the last ten years. The interest arises from the fact that simple geometric primitives cannot accurately represent natural objects. In computer graphics physics-based models are used to generate and visualize constrained shapes, motions of rigid and nonrigid objects and object interactions with the environment for the purposes of animation. On the other hand, in computer vision, the method applies to complex 3-D shape representation, shape reconstruction and motion estimation. In this paper we review two models that have been used in computer graphics and two models that apply to both areas. In the area of computer graphics, Miller [48] uses a mass-spring model to animate three forms of locomotion of snakes and worms. To overcome the problem of the multitude of degrees of freedom associated with the mass-spring lattices, Witkin and Welch [87] present a geometric method to model global deformations. To achieve the same result Pentland and Horowitz in [54] delineate the object motion into rigid and nonrigid deformation modes. To overcome problems of these two last approaches, Metaxas and Terzopoulos in [45] successfully combine local deformations with global ones. Modeling based on physical principles is a potent technique for computer graphics and computer vision. It is a rich and fruitful area for research in terms of both theory and applications. It is important, though, to develop concepts, methodologies, and techniques which will be widely applicable to many types of applications

Curve smoothing and matching

We present a new approach to the problem of matching 3D curves . The approach has an algorithmic complexity sublinear with the number of models, and can operate in the presence of noise and partial occlusions . Our method buids upon the seminal work of [27, 28], where curves are first smoothed using B-splines, with matching based on hashing using curvature and torsion measures . However, we introduce two enhancements * Ce travail a été en partie financé par Digital Equipment Corporation .We present a new approach to the problem of matching 3D curves . The approach has an algorithmic complexity sublinear with the number of models, and can operate in the presence of noise and partial occlusions . Our method buids upon the seminal work of [27, 28], where curves are first smoothed using B-splines, with matching based on hashing using curvature and torsion measures . However, we introduce two enhancements * Ce travail a été en partie financé par Digital Equipment Corporation . We present a new approach to the problem of matching 3D curves . The approach has an algorithmic complexity sublinear with the number of models, and can operate in the presence of noise and partial occlusions . Our method buids upon the seminal work of [27, 28], where curves are first smoothed using B-splines, with matching based on hashing using curvature and torsion measures . However, we introduce two enhancements * Ce travail a été en partie financé par Digital Equipment Corporation . we make use of non-uniform B-spline approximations, which permits us to better retain information at high curvature locations . The spline approximations are controlled (i.e ., regularized) by making use of normal vectors to the surface in 3-D on which the curves lie, and by an explicit minimization of a bending energy . These measures allow a more accurate estimation of position, curvatue, torsion and Frénet frames along the curve ; • the computational complexity of the recognition process is considerably decreased with explicit use of the Frénet frame for hypotheses generation . As opposed to previous approaches, the method better copes with partial occlusion . Moreover, following a statistical study of the curvature and torsion covariances, we optimize the hash table discretization and discover improved invariants for recognition, différent than the torsion measure. Finally, knowledge of invariant uncertainties is used to compute an optimal global transformation using an extended Kalman filter . We present experimental results using synthetic data and also using characteristic curves extracted front 3D medical images .Nous présentons une solution originale au problème de la reconnaissance et du recalage d'une courbe gauche discrète. La spécificité du problème est la nécessité de conserver une faible complexité algorithmique en présence d'un très grand nombre de modèles, d'être robuste au bruit et aux occultations partielles. Notre approche est une continuation logique des travaux de [27, 28] fondés sur un lissage des points par une courbe régulière puis par une reconnaissance à l'aide d'une table d'indexation mais présente deux innovations importantes: . pour une détermination plus fiable du modèle et de ses dérivées, les points discrets sont lissés par des splines en utilisant un critère d'erreur mixte et une distribution non uniforme de nœuds fondée sur la courbure locale et une régularisation exploitant la connaissance de la normale à la surface sur laquelle la courbe est inscrite et minimisant explicitement la variation de la courbure..

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value

Análise de movimento de corpos deformáveis usando visão computacional

O tema desta tese está inserido no domínio da visão por computador e na área da análise de movimento de corpos deformáveis. O seu interesse tem vindo a aumentar consideravelmente nos últimos tempos devido, sobretudo, ao fracasso das tentativas de utilizar as metodologias normalmente associadas aos corpos rígidos para a análise do movimento não rígido, e também ao elevado número de aplicações que existem para tal análise. O enorme potencial de aplicação existente na área da imagem médica, nomeadamente na segmentação, no emparelhamento e na análise e seguimento do movimento de estruturas, é responsável por grande parte do trabalho realizado neste âmbito. Outras aplicações que podem ser referidas são o seguimento de sistemas articulados, a análise do escoamento de fluidos, do movimento de nuvens para a previsão meteorológica, do comportamento de materiais sob a acção de forças, a análise e reconhecimento de faces, de veículos e de caracteres, etc.Ao contrário do que sucede com os objectos rígidos, a representação da forma de um objecto deformável está fortemente relacionada com a análise e seguimento do seu movimento e, para se desenvolverem técnicas para resolver tais problemas, é necessário utilizar determinadas restrições sobre o movimento/forma o que, consequentemente, individualiza as abordagens desenvolvidas e as torna específicas para determinadas classes de problemas.The theme of this thesis is in the computer vision domain and more specifically in the area of motion analysis of deformable bodies. The interest in this field has risen significantly in the last few years due to the failure of adapting existing rigid-body methods and to the very wide range of potential applications. A strong impulse originated in the area of medical imaging for segmenting, matching and tracking body structures, but other application domains have also contributed, namely the tracking of articulate systems, the analysis of fluids flow, the movement of clouds for weather forecasting, the structural analysis of materials, the recognition of faces, vehicles and characters, etc.Unlike rigid objects, the shape representation of deformable objects is strongly related with the analysis and tracking of its motion and thus, in order to develop suitable approaches and techniques for analysis, certain restrictions and constraints on the shape/motion must be specific to the type of task under consideration