Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes

In this paper, we introduce a novel parametric method for segmentation of three-dimensional images. We consider a piecewise constant version of the Mumford-Shah and the Chan-Vese functionals and perform a region-based segmentation of 3D image data. An evolution law is derived from energy minimization problems which push the surfaces to the boundaries of 3D objects in the image. We propose a parametric scheme which describes the evolution of parametric surfaces. An efficient finite element scheme is proposed for a numerical approximation of the evolution equations. Since standard parametric methods cannot handle topology changes automatically, an efficient method is presented to detect, identify and perform changes in the topology of the surfaces. One main focus of this paper are the algorithmic details to handle topology changes like splitting and merging of surfaces and change of the genus of a surface. Different artificial images are studied to demonstrate the ability to detect the different types of topology changes. Finally, the parametric method is applied to segmentation of medical 3D images

Automatic Mesh-Based Segmentation of Multiple Organs in MR Images

La segmentation de structures anatomiques multiples dans des images de résonance magnétique (RM) est souvent requise dans des applications de génie biomédical telles que la simulation numérique, la chirurgie guidée par l’image, la planification de traitements, etc. De plus, il y a un besoin croissant pour une segmentation automatique d’organes multiples et de structures complexes à partir de cette modalité d’imagerie. Il existe plusieurs techniques de segmentation multi-objets qui ont été appliquées avec succès sur des images de tomographie axiale à rayons-X (CT). Cependant, dans le cas des images RM cette tâche est plus difficile en raison de l’inhomogénéité des intensités dans ces images et de la variabilité dans l’apparence des structures anatomiques. Par conséquent, l’état de l’art sur la segmentation multi-objets sur des images RM est beaucoup plus faible que celui sur les images CT. Parmi les travaux qui portent sur la segmentation d’images RM, les approches basées sur la segmentation de régions sont sensibles au bruit et la non uniformité de l’intensité dans les images. Les approches basées sur les contours ont de la difficulté à regrouper les informations sur les contours de sorte à produire un contour fermé cohérent. Les techniques basées sur les atlas peuvent avoir des problèmes en présence de structures complexes avec une grande variabilité anatomique. Les modèles déformables représentent une des méthodes les plus populaire pour la détection automatique de différents organes dans les images RM. Cependant, ces modèles souffrent encore d’une limitation importante qui est leur sensibilité à la position initiale et la forme du modèle. Une initialisation inappropriée peut conduire à un échec dans l’extraction des frontières des objets. D’un autre côté, le but ultime d’une segmentation automatique multi-objets dans les images RM est de produire un modèle qui peut aider à extraire les caractéristiques structurelles d’organes distincts dans les images. Les méthodes d’initialisation automatique actuelles qui utilisent différents descripteurs ne réussissent pas complètement l’extraction d’objets multiples dans les images RM. Nous avons besoin d’exploiter une information plus riche qui se trouve dans les contours des organes. Dans ce contexte les maillages adaptatifs anisotropiques semblent être une solution potentielle au problème soulevé. Les maillages adaptatifs anisotropiques construits à partir des images RM contiennent de l’information à un plus haut niveau d’abstraction représentant les éléments, d’une orientation et d’une forme donnée, qui constituent les différents organes dans l’image. Les méthodes existantes pour la construction de maillages adaptatifs sont basées sur les intensités dans l’image et possèdent une limitation pratique qui est l’alignement inadéquat des éléments du maillage en présence de contours inclinés dans l’image. Par conséquent, nous avons aussi besoin d’améliorer le processus d’adaptation de maillage pour produire une meilleure représentation de l’image basée sur un maillage.----------ABSTRACT: Segmentation of multiple anatomical structures in MR images is often required for biomedical engineering applications such as clinical simulation, image-guided surgery, treatment planning, etc. Moreover, there is a growing need for automatic segmentation of multiple organs and complex structures from this medical imaging modality. Many successful multi-object segmentation attempts were introduced for CT images. However in the case of MR images it is a more challenging task due to intensity inhomogeneity and variability of anatomy appearance. Therefore, state-of-the-art in multi-object MR segmentation is very inferior to that of CT images. In literature dealing with MR image segmentation, the region-based approaches are sensitive to noise and non-uniformity in the input image. The edge-based approaches are challenging to group the edge information into a coherent closed contour. The atlas-based techniques can be problematic for complicated structures with anatomical variability. Deformable models are among the most popular methods for automatic detection of different organs in MR images. However they still have an important limitation which is that they are sensitive to initial position and shape of the model. An unsuitable initialization may provide failure to capture the true boundaries of the objects. On the other hand, a useful aim for an automatic multi-object MR segmentation is to provide a model which promotes understanding of the structural features of the distinct objects within the MR images. The current automatic initialization methods which have used different descriptors are not completely successful in extracting multiple objects from MR images and we need to find richer information that is available from edges. In this regard, anisotropic adaptive meshes seem to be a potential solution to the aforesaid limitation. Anisotropic adaptive meshes constructed from MR images contain higher level, abstract information about the anatomical structures of the organs within the image retained as the elements shape and orientation. Existing methods for constructing adaptive meshes based on image features have a practical limitation where manifest itself in inadequate mesh elements alignment to inclined edges in the image. Therefore, we also have to enhance mesh adaptation process to provide a better mesh-based representation. In this Ph.D. project, considering the highlighted limitations we are going to present a novel method for automatic segmentation of multiple organs in MR images by incorporating mesh adaptation techniques. In our progress, first, we improve an anisotropic adaptation process for the meshes that are constructed from MR images where the mesh elements align adequately to the image content and improve mesh anisotropy along edges in all directions. Then the resulting adaptive meshes are used for initialization of multiple active models which leads to extract initial object boundaries close to the true boundaries of multiple objects simultaneously. Finally, the Vector Field Convolution method is utilized to guide curve evolution towards the object boundaries to obtain the final segmentation results and present a better performance in terms of speed and accuracy

Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration

This thesis establishes a biomedical image analysis framework for the advanced visualization of biological structures. It consists of two important parts: 1) the segmentation of some structures of interest in 3D medical scans, and 2) the registration of patient-specific 3D models with 2D interventional images. Segmenting biological structures results in 3D computational models that are simple to visualize and that can be analyzed quantitatively. Registering a 3D model with interventional images permits to position the 3D model within the physical world. By combining the information from a 3D model and 2D interventional images, the proposed framework can improve the guidance of surgical intervention by reducing the ambiguities inherent to the interpretation of 2D images. Two specific segmentation problems are considered: 1) the segmentation of large structures with low frequency intensity nonuniformity, and 2) the detection of fine curvilinear structures. First, we directed our attention toward the segmentation of relatively large structures with low frequency intensity nonuniformity. Such structures are important in medical imaging since they are commonly encountered in MRI. Also, the nonuniform diffusion of the contrast agent in some other modalities, such as CTA, leads to structures of nonuniform appearance. A level-set method that uses a local-linear region model is defined, and applied to the challenging problem of segmenting brain tissues in MRI. The unique characteristics of the proposed method permit to account for important image nonuniformity implicitly. To the best of our knowledge, this is the first time a region-based level-set model has been used to perform the segmentation of real world MRI brain scans with convincing results. The second segmentation problem considered is the detection of fine curvilinear structures in 3D medical images. Detecting those structures is crucial since they can represent veins, arteries, bronchi or other important tissues. Unfortunately, most currently available curvilinear structure detection filters incur significant signal lost at bifurcations of two structures. This peculiarity limits the performance of all subsequent processes, whether it be understanding an angiography acquisition, computing an accurate tractography, or automatically classifying the image voxels. This thesis presents a new curvilinear structure detection filter that is robust to the presence of X- and Y-junctions. At the same time, it is conceptually simple and deterministic, and allows for an intuitive representation of the structure’s principal directions. Once a 3D computational model is available, it can be used to enhance surgical guidance. A 2D/3D non-rigid method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of the interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, which reduces the uncertainty inherent in 2D interventional images. A fully non-rigid registration model is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process. With a typical run time of less than 3 seconds, the algorithms are fast enough for interactive applications