173 research outputs found

    A dynamic tree-based registration could handle possible large deformations among MR brain images

    Get PDF
    Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multi-atlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. The dynamic tree capturing the structural relationships between images is then employed to further reduce misalignment errors. Evaluation based on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy

    Advances in Groupwise Image Registration

    Get PDF

    Advances in Groupwise Image Registration

    Get PDF

    Registration of brain MR images in large-scale populations

    Get PDF
    Non-rigid image registration is fundamentally important in analyzing large-scale population of medical images, e.g., T1-weighted brain MRI data. Conventional pairwise registration methods involve only two images, as the moving subject image is deformed towards the space of the template for the maximization of their in-between similarity. The population information, however, is mostly ignored, with individual images in the population registered independently with the arbitrarily selected template. By contrast, this dissertation investigates the contributions of the entire population to image registration. First, the population can provide guidance to the pairwise registration between a certain subject and the template. If the subject and an intermediate image in the same population are similar in appearances, the subject shares a similar deformation field with the intermediate image. Thus, the guidance from the intermediate image can be beneficial to the subject, in that the pre-estimated deformation field of the intermediate image initiates the estimation of the subject deformation field when the two images are registered with the identical template. Second, all images in the population can be registered towards the common space of the population using the groupwise technique. Groupwise registration differs from the traditional design of pairwise registration in that no template is pre-determined. Instead, all images agglomerate to the common space of the population simultaneously. Moreover, the common space is revealed spontaneously during image registration, without introducing any bias towards the subsequent analyses and applications. This dissertation shows that population information can contribute to both pairwise registration and groupwise registration. In particular, by utilizing the guidance from the intermediate images in the population, the pairwise registration is more robust and accurate compared to the direct pairwise registration between the subject and the template. Also, for groupwise registration, all images in the population can be aligned more accurately in the common space, although the complexity of groupwise registration increases substantially.Doctor of Philosoph

    Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

    Get PDF
    International audienceThis paper presents a new framework for capturing large and complex deformations in image registration and atlas construction. This challenging and recurrent problem in computer vision and medical imaging currently relies on iterative and local approaches, which are prone to local minima and, therefore, limit present methods to relatively small deformations. Our general framework introduces to this effect a new direct feature matching technique that finds global correspondences between images via simple nearest-neighbor searches. More specifically, very large image deformations are captured in Spectral Forces, which are derived from an improved graph spectral representation. We illustrate the benefits of our framework through a new enhanced version of the popular Log-Demons algorithm, named the Spectral Log-Demons, as well as through a groupwise extension, named the Groupwise Spectral Log-Demons, which is relevant for atlas construction. The evaluations of these extended versions demonstrate substantial improvements in accuracy and robustness to large deformations over the conventional Demons approaches

    The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation

    Get PDF
    Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of surgeries. Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly. This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation. The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images

    Intermediate templates guided groupwise registration of diffusion tensor images

    Get PDF
    Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image

    Surface Registration for Pharyngeal Radiation Treatment Planning

    Get PDF
    Endoscopy is an in-body examination procedure that enables direct visualization of tumor spread on tissue surfaces. In the context of radiation treatment planning for throat cancer, there have been attempts to fuse this endoscopic information into the planning CT space for better tumor localization. One way to achieve this CT/Endoscope fusion is to first reconstruct a full 3D surface model from the endoscopic video and then register that surface into the CT space. These two steps both require an algorithm that can accurately register two or more surfaces. In this dissertation, I present a surface registration method I have developed, called Thin Shell Demons (TSD), for achieving the two goals mentioned above. There are two key aspects in TSD: geometry and mechanics. First, I develop a novel surface geometric feature descriptor based on multi-scale curvatures that can accurately capture local shape information. I show that the descriptor can be effectively used in TSD and other surface registration frameworks, such as spectral graph matching. Second, I adopt a physical thin shell model in TSD to produce realistic surface deformation in the registration process. I also extend this physical model for orthotropic thin shells and propose a probabilistic framework to learn orthotropic stiffness parameters from a group of known deformations. The anisotropic stiffness learning opens up a new perspective to shape analysis and allows more accurate surface deformation and registration in the TSD framework. Finally, I show that TSD can also be extended into a novel groupwise registration framework. The advantages of Thin Shell Demons allow us to build a complete 3D model of the throat, called an endoscopogram, from a group of single-frame-based reconstructions. It also allows us to register an endoscopogram to a CT segmentation surface, thereby allowing information transfer for treatment planning.Doctor of Philosoph

    Coronary motion modelling for CTA to X-ray angiography registration

    Get PDF
    • …
    corecore