Multimodality and Nonrigid Image Registration with Application to Diffusion Tensor Imaging

The great challenge in image registration is to devise computationally efficient algorithms for aligning images so that their details overlap accurately. The first problem addressed in this thesis is multimodality medical image registration, which we formulate as an optimization problem in the information-theoretic setting. We introduce a viable and practical image registration method by maximizing a generalized entropic dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The feasibility of the proposed image registration approach is demonstrated through extensive experiments. The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein, we extend our nonrigid registration approach to align diffusion tensor images for multiple components by enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image registration indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information, not only in terms of registration accuracy in the presence of geometric distortions but also in terms of robustness in the presence of Rician noise

Robust Estimation of Trifocal Tensors Using Natural Features for Augmented Reality Systems

Augmented reality deals with the problem of dynamically augmenting or enhancing the real world with computer generated virtual scenes. Registration is one of the most pivotal problems currently limiting AR applications. In this paper, a novel registration method using natural features based on online estimation of trifocal tensors is proposed. This method consists of two stages: offline initialization and online registration. Initialization involves specifying four points in two reference images respectively to build the world coordinate system on which a virtual object will be augmented. In online registration, the natural feature correspondences detected from the reference views are tracked in the current frame to build the feature triples. Then these triples are used to estimate the corresponding trifocal tensors in the image sequence by which the four specified points are transferred to compute the registration matrix for augmentation. The estimated registration matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. This paper also proposes a robust method for estimating the trifocal tensors, where a modified RANSAC algorithm is used to remove outliers. Compared with standard RANSAC, our method can significantly reduce computation complexity, while overcoming the disturbance of mismatches. Some experiments have been carried out to demonstrate the validity of the proposed approach

Parameter Optimization of the Modified Phase Correlation Method for Sub-pixel Image Registration

Práce je zaměřena na určení parametrů váhové funkce, která je pak použita pro určení posunu mezi dvěma obrazy se sub-pixelovou přesností. Jsou použity standartní techniky pro registraci obrazů jako je Fourierova transformace, fázová korelace, bilineární interpolace aj. V práci je zahrnuta potřebná teorie, hledané parametry, postup optimalizace a je přiložen i program, který sloužil jako optimalizační prostředek.The goal of this thesis is to find parameters of weight function, thanks to them we will gain searched shift vector with sub-pixel precision. We are applying Fourier transform, inverse Fourier transform, phase correlation, bilinear interpolation etc. This thesis includes theory, wanted parameters, process of optimization and there is also enclosed program, which helped us with optimization.

Robust Algorithms for Registration of 3D Images of Human Brain

This thesis is concerned with the process of automatically aligning 3D medical images of human brain. It concentrates on rigid-body matching of Positron Emission Tomography images (PET) and Magnetic Resonance images (MR) within one patient and on non-linear matching of PET images of different patients. In recent years, mutual information has proved to be an excellent criterion for automatic registration of intra-individual images from different modalities. We propose and evaluate a method that combines a multi-resolution optimization of mutual information with an efficient segmentation of background voxels and a modified principal axes algorithm. We show that an acceleration factor of 6-7 can be achieved without loss of accuracy and that the method significantly reduces the rate of unsuccessful registrations. Emphasis was also laid on creation of an automatic registration system that could be used routinely in clinical environment. Non-linear registration tries to reduce the inter-individual variability of shape and structure between two brain images by deforming one image so that homologous regions in both images get aligned. It is an important step of many procedures in medical image processing and analysis. We present a novel algorithm for an automatic non-linear registration of PET images based on hierarchical volume subdivisions and local affine optimizations. It produces a C2-continuous deformation function and guarantees that the deformation is one-to-one. Performance of the algorithm was evaluated on more than 600 clinical PET images

Velocity estimation via registration-guided least-squares inversion

This paper introduces an iterative scheme for acoustic model inversion where the notion of proximity of two traces is not the usual least-squares distance, but instead involves registration as in image processing. Observed data are matched to predicted waveforms via piecewise-polynomial warpings, obtained by solving a nonconvex optimization problem in a multiscale fashion from low to high frequencies. This multiscale process requires defining low-frequency augmented signals in order to seed the frequency sweep at zero frequency. Custom adjoint sources are then defined from the warped waveforms. The proposed velocity updates are obtained as the migration of these adjoint sources, and cannot be interpreted as the negative gradient of any given objective function. The new method, referred to as RGLS, is successfully applied to a few scenarios of model velocity estimation in the transmission setting. We show that the new method can converge to the correct model in situations where conventional least-squares inversion suffers from cycle-skipping and converges to a spurious model.Comment: 20 pages, 13 figures, 1 tabl

303. Image registration – precise quality assessment of radiotherapy without necessity of showing corresponding points in simulation and portal images

PurposeEnabling the quality assessment of radiotherapy to be made in daily practice, using the new software tool to analyze the simulation and portal images.MethodIn the registration of the anatomical structures as well as the irradiation fields, the features used as landmarks are the edges. The significant edge fragments must be chosen manually, but without showing any specific corresponding points. Field edges marked with wires in the simulation image are found fully automatically with the original combination of a dedicated line edge detector and a version of hierarchical, combined Hough transform. The registration is guided by the robust accuracy criterion using the modified Hausdorff distance measure. The only parameter of the measure – quantile rank, or share of data used in comparison – is not fixed, but evolves from 1 to 0 during the optimization of the accuracy. This has two advantages. 1: The user can choose the result found for the share corresponding to the actual share of erroneous data in the images, which can be seen only after the results for all the possible ranks are known. 2: The algorithm can avoid the local minima. The registration takes few seconds on a typical PC. The method has been implemented in a software tool which supports the complete process of measurement, and has been tested in clinical triais with positive result.ConclusionsThe modified Hausdorff distance measure with evolving rank is a good and efficient registration accuracy measure for quality assessment of radiotherapy based on the comparison of portal and simulation images