Fingerprint registration by maximization of mutual information.

Fingerprint registration is a critical step in fingerprint matching. Although a variety of registration alignment algorithms have been proposed, accurate fingerprint registration remains an unresolved problem. We propose a new algorithm for fingerprint registration using orientation field. This algorithm finds the correct alignment by maximization of mutual information between features extracted from orientation fields of template and input fingerprint images. Orientation field, representing the flow of ridges, is a relatively stable global feature of fingerprint images. This method uses the statistics and distribution of global feature of fingerprint images so that it is robust to image quality and local changes in images. The primary characteristic of this method is that it uses this stable global feature to align fingerprints, and that its behavior may resemble the way humans compare fingerprints. Experimental results show that the occurrence of misalignment is dramatically reduced and that registration accuracy is greatly improved at the same time, leading to enhanced matching performance.     

Registration under topological change for CT colonography

Computed tomography (CT) colonography is a minimally invasive screening technique for colorectal polyps, in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions of a single patient. Registration of segmented colon images from both positions will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colons when both are well distended and there is a single connected lumen. However, due to inadequate bowel preparation or peristalsis, there may be collapsed segments in one or both of the colon images resulting in a topological change in the images. Such changes make deformable registration of the colon images difficult, and at present, there are no registration algorithms that can accommodate them. In this paper, we present an algorithm that can perform volume registration of prone/supine colon images in the presence of a topological change. For this purpose, 3-D volume images are embedded as a manifold in a 4-D space, and the manifold is evolved for nonrigid registration. Experiments using data from 24 patients show that the proposed method achieves good registration results in both the shape alignment of topologically different colon images from a single patient and the polyp location estimation between supine and prone colon images.     

Accelerating image registration with the Johnson-Lindenstrauss lemma: application to imaging 3-D neural ultrastructure with electron microscopy

We present a novel algorithm to accelerate feature based registration, and demonstrate the utility of the algorithm for the alignment of large transmission electron microscopy (TEM) images to create 3-D images of neural ultrastructure. In contrast to the most similar algorithms, which achieve small computation times by truncated search, our algorithm uses a novel randomized projection to accelerate feature comparison and to enable global search. Further, we demonstrate robust estimation of nonrigid transformations with a novel probabilistic correspondence framework, that enables large TEM images to be rapidly brought into alignment, removing characteristic distortions of the tissue fixation and imaging process. We analyze the impact of randomized projections upon correspondence detection, and upon transformation accuracy, and demonstrate that accuracy is maintained. We provide experimental results that demonstrate significant reduction in computation time and successful alignment of TEM images.     

A "twisting and bending" model-based nonrigid image registration technique for 3-D ultrasound carotid images

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a “twisting and bending” model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. We calculated the mean registration error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our “twisting and bending” model-based nonrigid registration algorithm. We achieved an average registration error of $0.80 pm 0.26$ mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.      

Nonrigid point set matching of white matter tracts for diffusion tensor image analysis

Patient studies based on diffusion tensor images (DTI) require spatial correspondence between subjects. We propose to obtain the correspondence from white matter tracts, by introducing a new method for nonrigid matching of white matter fiber tracts in DTI. The method boils down to point set registration that involves simultaneously clustering and matching of the data points. The tracts are implicitly warped to a common frame of reference to avoid the potential bias toward one of the datasets. The algorithm gradually refines from global to local registration, which is implemented through deterministic annealing. Special care was taken to incorporate the spatial relation between fiber points and the uncertainty in principal diffusion orientation. As a result, the computed clusters are oriented along the fiber tracts and discriminate between adjacent but distinct fiber tracts. This is validated on synthetic and clinical data. The root-mean-squared distance with respect to expert-annotated landmarks is low (3 mm). In contrast to a state-of-the-art nonrigid registration technique, the proposed method is more robust to residual misalignments in terms of measured fractional anisotropy values.     

A recursive robust filtering approach for 3D registration

This work presents a new recursive robust filtering approach for feature-based 3D registration. Unlike the common state-of-the-art alignment algorithms, the proposed method has four advantages that have not yet occurred altogether in any previous solution. For instance, it is able to deal with inherent noise contaminating sensory data; it is robust to uncertainties caused by noisy feature localisation; it also combines the advantages of both \(L_\infty \) and \(L_2\) norms for a higher performance and a more prospective prevention of local minima. The result is an accurate and stable rigid body transformation. The latter enables a thorough control over the convergence regarding the alignment as well as a correct assessment of the quality of registration. The mathematical rationale behind the proposed approach is explained, and the results are validated on physical and synthetic data.     

Accounting for signal loss due to dephasing in the correction of distortions in gradient-echo EPI via nonrigid registration

Gradient-echo (GE) echo planar imaging (EPI) is susceptible to both geometric distortions and signal loss. This paper presents a retrospective correction approach based on nonrigid image registration. A new physics-based intensity correction factor derived to compensate for intravoxel dephasing in GE EPI images is incorporated into a previously reported nonrigid registration algorithm. Intravoxel dephasing causes signal loss and thus intensity attenuation in the images. The new rephasing factor we introduce, which changes the intensity of a voxel in images during the registration, is used to improve the accuracy of the intensity-based nonrigid registration method and mitigate the intensity attenuation effect. Simulation-based experiments are first used to evaluate the method. A magnetic resonance (MR) simulator and a real field map are used to generate a realistic GE EPI image. The geometric distortion computed from the field map is used as the ground truth to which the estimated nonrigid deformation is compared. We then apply the algorithm to a set of real human brain images. The results show that, after registration, alignment between EPI and multi-shot, spin-echo images, which have relatively long acquisition times but negligible distortion, is improved and that signal loss caused by dephasing can be recovered.     

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.     

Nonrigid registration using free-form deformations: application tobreast MR images

In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.     

Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model

Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. We make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. We also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a imagnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.     

Temporal subtraction of thorax CR images using a statistical deformation model

We propose a voxel-based nonrigid registration algorithm for temporal subtraction of two-dimensional thorax X-ray computed radiography images of the same subject. The deformation field is represented by a B-spline with a limited number of degrees of freedom, that allows global rib alignment to minimize subtraction artifacts within the lung field without obliterating interval changes of clinically relevant soft-tissue abnormalities. The spline parameters are constrained by a statistical deformation model that is learned from a training set of manually aligned image pairs using principal component analysis. Optimization proceeds along the transformation components rather then along the individual spline coefficients, using pattern intensity of the subtraction image within the automatically segmented lung field region as the criterion to be minimized and applying a simulated annealing strategy for global optimization in the presence of multiple local optima. The impact of different transformation models with varying number of deformation modes is evaluated on a training set of 26 images using a leave-one-out strategy and compared to the manual registration result in terms of criterion value and deformation error. Registration quality is assessed on a second set of validation images by a human expert rating each subtraction image on screen. In 85% of the cases, the registration is subjectively rated to be adequate for clinical use.     

Automatic quantification of changes in bone in serial MR images of joints

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.     

Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician.     

Accurate localization of optic radiation during neurosurgery in an interventional MRI suite

Accurate localization of the optic radiation is key to improving the surgical outcome for patients undergoing anterior temporal lobe resection for the treatment of refractory focal epilepsy. Current commercial interventional magnetic resonance imaging (MRI) scanners are capable of performing anatomical and diffusion weighted imaging and are used for guidance during various neurosurgical procedures. We present an interventional imaging workflow that can accurately localize the optic radiation during surgery. The workflow is driven by a near real-time multichannel nonrigid image registration algorithm that uses both anatomical and fractional anisotropy pre- and intra-operative images. The proposed workflow is implemented on graphical processing units and we perform a warping of the pre-operatively parcellated optic radiation to the intra-operative space in under 3 min making the proposed algorithm suitable for use under the stringent time constraints of neurosurgical procedures. The method was validated using both a numerical phantom and clinical data using pre- and post-operative images from patients who had undergone surgery for treatment of refractory focal epilepsy and shows strong correlation between the observed post-operative visual field deficit and the predicted damage to the optic radiation. We also validate the algorithm using interventional MRI datasets from a small cohort of patients. This work could be of significant utility in image guided interventions and facilitate effective surgical treatments.     

2D/3D registration and motion tracking for surgical interventions

In order to use pre-operative images during an intervention for navigation, they must be registered to the patient's co-ordinate system in the operating theatre or to an intra-operative image. For the registration to be valid in the case of patient movements, the registration must be updated or the patient movement must be tracked. One problem in this area is the registration of intra-operatively acquired X-ray fluoroscopies with 3D CT images obtained before the intervention as well as motion tracking for this setup. The result can be used to support the placement of pedicle screws in spine surgery or aortic endoprostheses in transfemoral endovascular aneurysm management (TEAM). The different approaches to 2D/3D registration are discussed and a novel voxel-based method is presented: using a small part of the CT image covering only the vertebra of interest, pseudo-projections are computed and the resulting vertebra template is compared to the X-ray projection using a new similarity measure which is called pattern intensity. Application, performance and registration accuracy are discussed and demonstrated by application to images of a TEAM procedure and of a spine phantom.     

A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.      

3-D quantitative vascular shape analysis for arterial bifurcations via dynamic tube fitting

Reliable and reproducible estimation of vessel centerlines and reference surfaces is an important step for the assessment of luminal lesions. Conventional methods are commonly developed for quantitative analysis of the "straight" vessel segments and have limitations in defining the precise location of the centerline and the reference lumen surface for both the main vessel and the side branches in the vicinity of bifurcations. To address this, we propose the estimation of the centerline and the reference surface through the registration of an elliptical cross-sectional tube to the desired constituent vessel in each major bifurcation of the arterial tree. The proposed method works directly on the mesh domain, thus alleviating the need for image upsampling, usually required in conventional volume domain approaches. We demonstrate the efficiency and accuracy of the method on both synthetic images and coronary CT angiograms. Experimental results show that the new method is capable of estimating vessel centerlines and reference surfaces with a high degree of agreement to those obtained through manual delineation. The centerline errors are reduced by an average of 62.3% in the regions of the bifurcations, when compared to the results of the initial solution obtained through the use of mesh contraction method.     

Dynamic 2D Ultrasound and 3D CT Image Registration of the Beating Heart

Two-dimensional ultrasound (US) is widely used in minimally invasive cardiac procedures due to its convenience of use and noninvasive nature. However, the low quality of US images often limits their utility as a means for guiding procedures, since it is often difficult to relate the images to their anatomical context. To improve the interpretability of the US images while maintaining US as a flexible anatomical and functional real-time imaging modality, we describe a multimodality image navigation system that integrates 2D US images with their 3D context by registering them to high quality preoperative models based on magnetic resonance imaging (MRI) or computed tomography (CT) images. The mapping from such a model to the patient is completed using spatial and temporal registrations. Spatial registration is performed by a two-step rapid registration method that first approximately aligns the two images as a starting point to an automatic registration procedure. Temporal alignment is performed with the aid of electrocardiograph (ECG) signals and a latency compensation method. Registration accuracy is measured by calculating the TRE. Results show that the error between the US and preoperative images of a beating heart phantom is $1.7 pm 0.4$ mm, with a similar performance being observed in in vivo animal experiments.      

Validation of nonrigid image registration using finite-element methods: application to breast MR images

This paper presents a novel method for validation of nonrigid medical image registration. This method is based on the simulation of physically plausible, biomechanical tissue deformations using finite-element methods. Applying a range of displacements to finite-element models of different patient anatomies generates model solutions which simulate gold standard deformations. From these solutions, deformed images are generated with a range of deformations typical of those likely to occur in vivo. The registration accuracy with respect to the finite-element simulations is quantified by co-registering the deformed images with the original images and comparing the recovered voxel displacements with the biomechanically simulated ones. The functionality of the validation method is demonstrated for a previously described nonrigid image registration technique based on free-form deformations using B-splines and normalized mutual information as a voxel similarity measure, with an application to contrast-enhanced magnetic resonance mammography image pairs. The exemplar nonrigid registration technique is shown to be of subvoxel accuracy on average for this particular application. The validation method presented here is an important step toward more generic simulations of biomechanically plausible tissue deformations and quantification of tissue motion recovery using nonrigid image registration. It will provide a basis for improving and comparing different nonrigid registration techniques for a diversity of medical applications, such as intrasubject tissue deformation or motion correction in the brain, liver or heart.     

Medical image registration using mutual information

Analysis of multispectral or multitemporal images requires proper geometric alignment of the images to compare corresponding regions in each image volume. Retrospective three-dimensional alignment or registration of multimodal medical images based on features intrinsic to the image data itself is complicated by their different photometric properties, by the complexity of the anatomical objects in the scene and by the large variety of clinical applications in which registration is involved. While the accuracy of registration approaches based on matching of anatomical landmarks or object surfaces suffers from segmentation errors, voxel-based approaches consider all voxels in the image without the need for segmentation. The recent introduction of the criterion of maximization of mutual information, a basic concept from information theory, has proven to be a breakthrough in the field. While solutions for intrapatient affine registration based on this concept are already commercially available, current research in the field focuses on interpatient nonrigid matching.