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Images collected by intravital microscopy suffer from motion artifacts which necessitates the use of image registration techniques to reconstruct three-dimensional or time series data. In this paper, we describe a multi-resolution/multi-level approach to non-rigid image registration using B-splines with a coarse-to-fine strategy. A control lattice with a coarse grid spacing is used first to account...
A procedure to fuse the information of short-axis cine and late enhanced magnetic resonance images is presented. First a coherent 3D reconstruction of the images is obtained by object-based interpolation of the information of contiguous slices in stacked short-axis cine acquisitions and by the correction of slice misalignments with the aid of a set of reference long-axis slices. Then, late enhanced...
We propose a novel framework for longitudinal registration which can handle large intra-subject anatomical variations. The framework exploits freely available spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. The spatio-temporal atlas is used to develop...
Image registration is in principle a symmetric problem. Nonetheless, most intensity-based non-rigid algorithms are asymmetric. In this paper, we propose a novel symmetric deformable registration algorithm formulated in a Markov Random Fields framework where both images are let to deform towards a common domain that lies halfway between two image domains. A grid-based deformation model is employed...
Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established non-rigid registration methods, but those which are able to preserve discontinuities in the displacement field are rather rare. This paper deals with a nonrigid registration method, that can handle discontinuities in the motion field that appear in...
Toxicity prediction is crucial in prostate cancer radiotherapy. There is thus a need to model voxel-wise relationships between dose distribution and toxicity events. However, building such a model is challenging due to difficulties in the mapping of the organs and of the dose distribution. We designed an organ-driven registration method merging the deformation fields computed for registrations first...
The aim of this work is to register serial in-vivo confocal microscopy images of zebrafish to enable accurate cell tracking on corresponding fluorescence images. The following problem arises during acquisition; the zebrafish tail may undergoe a series of movement and non-linear deformations, which if not corrected, adds to the motion of leukocytes being tracked. This makes it difficult to accurately...
Thin-plate splines are probably the most often used technique for landmark-based registration with radial basis functions. However, a disadvantage is that the locality of the transformation cannot be controlled. We introduce an extension of thin-plate splines which enables to control the spatial influence of acting forces and thus the locality of the transformation. Our approach is based on a new...
Problem of non-rigid registration has become very important in the area of biomedical imaging. A non-rigid registration problem is modeled as an optimization problem and is solved using graph cuts and MRFs in recent years. In this paper, we have improved the graph cuts-based solution to non-rigid registration with a novel data term. The proposed data term has several advantages. Firstly, displacement...
We present a diffeomorphic diffusion tensor image (DTI) registration technique with multi-contrast images extracted from DTI and conventional structural MRI data. DTI provides microstructure information in white matter. However due to the acquisition protocols used in many clinical studies, DTI has lower SNR and spatial resolution compared to structural MRI. Complementary information can be used to...
Accurate inter-subject registration of magnetic resonance (MR) images of the human brain is required to allow meaningful comparisons across groups of subjects. Some anatomical structures can be very difficult to match and this can result in intensity based registration approaches inferring complex and implausible mappings in some regions. In this work, we propose a generic probabilistic framework...
Progress in our understanding of brain functions relies on our capability to explore the human cortical surface at a fine scale (typically 1.5 mm isotropic at 3T). For this purpose, high accuracy is required for all processing steps from image acquisition to data analysis. For group studies, the high intersubject variability of the human cortices hampers their precise registration. Based on the hypothesis...
Piecewise rigid registration is a fundamental problem in medical imaging involving intra-patient pose differences in multiple medical images, mostly due to articulated motion. In this paper, we propose a method to extract multiple rigid transformations in 2D medical images in the presence of outliers. First, points of interest in the images are extracted and matched with the SIFT algorithm. Secondly,...
More and more researchers are beginning to use multiple dissimilarity metrics or image features for medical image registration. In most of these approaches, however, weights for ranking the relative importance between the selected metrics are empirically tuned and fixed for the entire image domain. Different parts of a medical image, however, may contain significantly different appearance properties...
In the present work we propose a novel, efficient strategy for modelling tumour induced brain deformation as a prior for non-rigid image registration in non-diffeomorphic registration problems seen in serial or cross-population brain tumour imaging studies. Here, the presence of pathology dramatically alters the morphological and textural appearance of the anatomical structures under consideration...
For medical image segmentation, multi-atlas based segmentation methods have attracted great attention recently. Within the multi-atlas segmentation framework, labels of all atlases are propagated to the target image by means of image registration and then fused to achieve segmentation of the target image. While most multi-atlas based segmentation methods focus on developing effective label fusion...
This paper is concerned with assessing localization errors emanating from the image registration of two monochromatic fluorescence microscopy images. Assuming an affine transform exists between images, registration in this setting typically involves using control points to solve a multivariate linear regression problem; however with measurement errors existing in both sets of variables the use of...
We introduce a novel boosting algorithm to boost — i.e. improve on — existing methods for deformable image registration. The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well-known in the field of machine learning. DIRBoost involves a classifier for landmark-based Registration Error Detection (RED). Based on these RED predictions a Voronoi tessellation is generated...
We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration...
In this paper, we present a new algorithm to register 3D multimodal images with sliding conditions in a diffeomorphic framework. Our driving motivation is to define one-to-one mappings between CT/MR pulmonary volumes acquired from patients with empyema. The main problem to overcome is that the pulmonary motion, which can be large, presents sliding conditions at the thoracic cage boundary. Our algorithm...
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