Improve Registration Accuracy Research Articles

Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multi-dimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

Using multiple images and contours for deformable 3D-2D registration of a preoperative CT in laparoscopic liver surgery.

Laparoscopic liver resection is a challenging procedure because of the difficulty to localise inner structures such as tumours and vessels. Augmented reality overcomes this problem by overlaying preoperative 3D models on the laparoscopic views. It requires deformable registration of the preoperative 3D models to the laparoscopic views, which is a challenging task due to the liver flexibility and partial visibility. We propose several multi-view registration methods exploiting information from multiple views simultaneously in order to improve registration accuracy. They are designed to work on two scenarios: on rigidly related views and on non-rigidly related views. These methods exploit the liver's anatomical landmarks and texture information available in all the views to constrain registration. We evaluated the registration accuracy of our methods quantitatively on synthetic and phantom data, and qualitatively on patient data. We measured 3D target registration errors in mm for the whole liver for the quantitative case, and 2D reprojection errors in pixels for the qualitative case. The proposed rigidly related multi-view methods improve registration accuracy compared to the baseline single-view method. They comply with the 1cm oncologic resection margin advised for hepatocellular carcinoma interventions, depending on the available registration constraints. The non-rigidly related multi-view method does not provide a noticeable improvement. This means that using multiple views with the rigidity assumption achieves the best overall registration error.

Impact of radiographic field-of-view volume on alignment accuracy during virtual implant planning: A noninterventional retrospective pilot study.

To evaluate the impact of reducing the radiographic field of view (FOV) on the trueness and precision of the alignment between cone beam computed tomography (CBCT) and intraoral scanning data for implant planning. Fifteen participants presenting with one of three clinical scenarios: single tooth loss (ST, n=5), multiple missing teeth (MT, n=5) and presence of radiographic artifacts (AR, n=5) were included. CBCT volumes covering the full arch (FA) were reduced to the quadrant (Q) or the adjacent tooth/teeth (A). Two operators, an expert (exp) in virtual implant planning and an inexperienced clinician, performed multiple superimpositions, with FA-exp serving as a reference. The deviations were calculated at the implant apex and shoulder levels. Thereafter, linear mixed models were adapted to investigate the influence of FOV on discrepancies. Evaluation of trueness compared to FA-exp resulted in the largest mean (AR-A: 0.10 ± 0.33 mm) and single maximum discrepancy (AR-Q: 1.44 mm) in the presence of artifacts. Furthermore, for the ST group, the largest mean error (-0.06 ± 0.2 mm, shoulder) was calculated with the FA-FOV, while for MT, with the intermediate volume (-0.07 ± 0.24 mm, Q). In terms of precision, the mean SD intervals were ≤0.25 mm (A-exp). Precision was influenced by FOV volume (FA < Q < A) but not by operator expertise. For single posterior missing teeth, an extended FOV does not improve registration accuracy. However, in the presence of artifacts or multiple missing posterior teeth, caution is recommended when reducing FOV.

Deep residual-SVD network for brain image registration

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.

Inter-Phase 4D Cardiac MRI Registration With a Motion Prior Derived From CTA.

Registration between phases in 4D cardiac MRI is essential for reconstructing high-quality images and appreciating the dynamics. Complex motion and limited image quality make it challenging to design regularization functionals. We propose to introduce a motion representation model (MRM) into a registration network to impose customized, site-specific, and spatially variant prior for cardiac motion. We propose a novel approach to regularize deep registration with a deformation vextor field (DVF) representation model using computed tomography angiography (CTA). In the form of a convolutional auto-encoder, the MRM was trained to capture the spatially variant pattern of feasible DVF Jacobian. The CTA-derived MRM was then incorporated into an unsupervised network to facilitate MRI registration. In the experiment, 10 CTAs were used to derive the MRM. The method was tested on 10 0.35 T scans in long-axis view with manual segmentation and 15 3 T scans in short-axis view with tagging-based landmarks. Introducing the MRM improved registration accuracy and achieved 2.23, 7.21, and 4.42 mm 80% Hausdorff distance on left ventricle, right ventricle, and pulmonary artery, respectively, and 2.23 mm landmark registration error. The results were comparable to carefully tuned SimpleElastix, but reduced the registration time from 40 s to 0.02 s. The MRM presented good robustness to different DVF sample generation methods. The model enjoys high accuracy as meticulously tuned optimization model and the efficiency of deep networks. The method enables model to go beyond the quality limitation of MRI. The robustness to training DVF generation scheme makes the method attractive to adapting to the available data and software resources in various clinics.

Automated machine learning (AutoML)-based surface registration methodology for image-guided surgical navigation system.

Although the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy. This research proposes automated machine learning (AutoML)-based surface registration to improve the accuracy of image-guided surgical navigation systems. The state-of-the-art surface registration concept is that first, using a neural network model, a new point-cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography. Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point-cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian optimization with expected improvement to yield improved registration accuracy. Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is 0.939±0.375mm, which shows 57.8% improvement compared to the average TRE of 2.227±0.193mm calculated by the conventional surface registration method (p<0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are 0.767±0.132 and 2.615±0.378mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML-based surface registration is also presented. Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury.

As one of the most common imaging screening techniques for spinal injuries, MRI is of great significance for the pretreatment examination of patients with spinal injuries. With rapid iterative update of imaging technology, imaging techniques such as diffusion weighted magnetic resonance imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy are frequently used in the clinical diagnosis of spinal injuries. Multimodal medical image fusion technology can obtain richer lesion information by combining medical images in multiple modalities. Aiming at the two modalities of DCE-MRI and DWI images under MRI images of spinal injuries, by fusing the image data under the two modalities, more abundant lesion information can be obtained to diagnose spinal injuries. The research content includes the following: (1) A registration study based on DCE-MRI and DWI image data. To improve registration accuracy, a registration method is used, and VGG-16 network structure is selected as the basic registration network structure. An iterative VGG-16 network framework is proposed to realize the registration of DWI and DCE-MRI images. The experimental results show that the iterative VGG-16 network structure is more suitable for the registration of DWI and DCE-MRI image data. (2) Based on the fusion research of DCE-MRI and DWI image data. For the registered DCE-MRI and DWI images, this paper uses a fusion method combining feature level and decision level to classify spine images. The simple classifier decision tree, SVM, and KNN were used to predict the damage diagnosis classification of DCE-MRI and DWI images, respectively. By comparing and analyzing the classification results of the experiments, the performance of multimodal image fusion in the auxiliary diagnosis of spinal injuries was evaluated.

Few-Shot Learning for Deformable Medical Image Registration With Perception-Correspondence Decoupling and Reverse Teaching.

Deformable medical image registration estimates corresponding deformation to align the regions of interest (ROIs) of two images to a same spatial coordinate system. However, recent unsupervised registration models only have correspondence ability without perception, making misalignment on blurred anatomies and distortion on task-unconcerned backgrounds. Label-constrained (LC) registration models embed the perception ability via labels, but the lack of texture constraints in labels and the expensive labeling costs causes distortion internal ROIs and overfitted perception. We propose the first few-shot deformable medical image registration framework, Perception-Correspondence Registration (PC-Reg), which embeds perception ability to registration models only with few labels, thus greatly improving registration accuracy and reducing distortion. 1) We propose the Perception-Correspondence Decoupling which decouples the perception and correspondence actions of registration to two CNNs. Therefore, independent optimizations and feature representations are available avoiding interference of the correspondence due to the lack of texture constraints. 2) For few-shot learning, we propose Reverse Teaching which aligns labeled and unlabeled images to each other to provide supervision information to the structure and style knowledge in unlabeled images, thus generating additional training data. Therefore, these data will reversely teach our perception CNN more style and structure knowledge, improving its generalization ability. Our experiments on three datasets with only five labels demonstrate that our PC-Reg has competitive registration accuracy and effective distortion-reducing ability. Compared with LC-VoxelMorph( λ = 1), we achieve the 12.5%, 6.3% and 1.0% Reg-DSC improvements on three datasets, revealing our framework with great potential in clinical application.

Markerless registration approach using dynamic touchable region model.

Markerless registration is required for image-guided surgery; it has limited accuracy due to the ambiguity of the correspondence point set. In this study, we proposed a registration framework to improve registration accuracy for markerless registration using a dynamic touchable region model (DTRM). The DTRM is defined using the geometric characteristics of the surface around the fiducial area using Intrinsic Shape Signature keypoints. The new registration procedure, which combines the DTRM with iterative closest point-based registration (ICDTP) was implemented and verified with phantom experiments for a single-user and multi-user study. The ICDTP registration framework provides improved performance over the paired-point registration for surgeries, where it is inappropriate to construct the corresponding positions with adhesive fiducial markers. The proposed method can be an alternative approach for image-guided surgery in operations for which itis not appropriate to set up markers for registration.

Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition

Groupwise image registration describes the problem of simultaneously aligning a series of more than two images through individual spatial deformations and it is a common task in the processing of medical image sequences. Variational methods with data fidelity terms based on robust PCA (RPCA) have proven successful in accounting for structural changes in image intensity stemming, e.g., from the uptake of a contrast agent in functional imaging. In this article, we investigate the drawbacks of the most commonly used RPCA data term and derive an improved replacement that employs explicit constraints instead of penalties. We further present a multilevel scheme with theoretically justified scaling to solve the underlying fully deformable registration model. Our numerical experiments on synthetic and real-life medical data confirm the advanced adaptability of RPCA-based data terms and showcase an improved registration accuracy of our algorithm when compared to related groupwise approaches.

Augmented reality visualization in brain lesions: a prospective randomized controlled evaluation of its potential and current limitations in navigated microneurosurgery

BackgroundAugmented reality (AR) has the potential to support complex neurosurgical interventions by including visual information seamlessly. This study examines intraoperative visualization parameters and clinical impact of AR in brain tumor surgery.MethodsFifty-five intracranial lesions, operated either with AR-navigated microscope (n = 39) or conventional neuronavigation (n = 16) after randomization, have been included prospectively. Surgical resection time, duration/type/mode of AR, displayed objects (n, type), pointer-based navigation checks (n), usability of control, quality indicators, and overall surgical usefulness of AR have been assessed.ResultsAR display has been used in 44.4% of resection time. Predominant AR type was navigation view (75.7%), followed by target volumes (20.1%). Predominant AR mode was picture-in-picture (PiP) (72.5%), followed by 23.3% overlay display. In 43.6% of cases, vision of important anatomical structures has been partially or entirely blocked by AR information. A total of 7.7% of cases used MRI navigation only, 30.8% used one, 23.1% used two, and 38.5% used three or more object segmentations in AR navigation. A total of 66.7% of surgeons found AR visualization helpful in the individual surgical case. AR depth information and accuracy have been rated acceptable (median 3.0 vs. median 5.0 in conventional neuronavigation). The mean utilization of the navigation pointer was 2.6 × /resection hour (AR) vs. 9.7 × /resection hour (neuronavigation); navigation effort was significantly reduced in AR (P < 0.001).ConclusionsThe main benefit of HUD-based AR visualization in brain tumor surgery is the integrated continuous display allowing for pointer-less navigation. Navigation view (PiP) provides the highest usability while blocking the operative field less frequently. Visualization quality will benefit from improvements in registration accuracy and depth impression.German clinical trials registration number.DRKS00016955.

Brain CT registration using hybrid supervised convolutional neural network

BackgroundImage registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.MethodHSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference–moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively.ResultsHSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s).ConclusionThe proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration.

Whole‐brain functional MRI registration based on a semi‐supervised deep learning model

Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not necessarily located relative to anatomical structures. In addition, registration methods based on functional information focus on gray matter (GM) information but ignore the importance of white matter (WM). To overcome the limitations of exiting techniques, in this paper, we aim to register resting-state fMRI (rs-fMRI) based directly on rs-fMRI data and make full use of GM and WM information to improve the registration performance. We provide a robust representation of WM functional connectivity features using tissue-specific patch-based functional correlation tensors (ts-PFCTs) as auxiliary information to assist registration. Furthermore, we propose a semi-supervised deep learning model that uses GM and WM information (GM ts-PFCTs and WM ts-PFCTs) during training as a fine tweak to improve registration accuracy when such information is not provided in new test image pairs. We implement our method on the 1000 Functional Connectomes Project dataset. To evaluate our method, a group-level analysis was implemented in resting-state brain functional networks after registration, resulting in t maps. Our method increases the peak t values of the t maps of default mode network, visual network, central executive network, and sensorimotor network to 21.4, 20.0, 18.4, and 19.0, respectively. Through comparison with traditional methods (FMRIB Software Library(FSL), Statistical Parametric Mapping _ Echo Planar Image(SPM_EPI), and SPM_T1), our method achieves an average improvement of 67.39%, 12.96%, and 25.14%. We propose a semi-supervised deep learning network by adding GM and WM information as auxiliary information for resting-state fMRI registration. GM and WM information is extracted and described as GM ts-PFCTs and WM ts-PFCTs. Experimental results show that our method achieves superior registration performance.

Human attention-inspired volume reconstruction method on serial section electron microscopy images.

The alignment of a 2D microscopic image stack to create a 3D image volume is an indispensable aspect of serial section electron microscopy (EM) technology, which could restore the original 3D integrity of biological tissues destroyed by chemical fixation and physical dissection. However, due to the similar texture intrasection and complex variations intersections of neural images, previous registration methods usually failed to yield reliable correspondences. And this also led to misalignment and impeded restoring the z-axis anatomical continuity of the neuron volume. In this article, inspired by human behaviors in finding correspondences, which use the topological relationship of image contents, we developed a spatial attention-based registration method for serial EM images to improve registration accuracy. Our approach combined the U-Net framework with spatial transformer networks (STN) to regress corresponding transformation maps in an unsupervised training fashion. The spatial attention (SA) module was incorporated into the U-Net architecture to increase the distinctiveness of image features by modeling its topological relationship. Experiments are conducted on both simulated and real data sets (MAS and RegCremi). Quantitative and qualitative comparisons demonstrate that our approach results in state of art accuracy (using the evaluation index of NCC, SSIM, Dice, Landmark error) and providing smooth and reliable transformation with less texture blur and unclear boundary than existing techniques. Our method is able to restore image stacks for visualization and quantitative analysis of EM image sequences.

Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix.

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.

A coarse-to-fine registration method for three-dimensional MR images.

Three-dimensional (3D) multimodal magnetic resonance (MR) image registration aims to align similar things in different MR images spatially. Such a technology is useful in auxiliary disease diagnosis and surgical treatment. However, inconsistent intensity correspondence and large initial displacement contribute to the difficulty in registering multimodal MR volumes. A coarse-to-fine method is proposed in this study for pairwise 3D MR image rigid registration. Firstly, the proposed method extracts image feature points to form unregistered point sets and performs coarse registration based on point set registration to reduce the initial displacements of offset images effectively. Then, this method calculates a grey histogram based on voxels in the adaptive region of interest and further improves registration accuracy by maximizing mutual information of coarse-registered images. Some representative registration methods are compared on the basis of three MR image datasets to evaluate the performance of the proposed method. Experimental results show that the proposed method improved more in registration success rate and accuracy compared with conventional registration methods, especially when initial displacements are large.

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.

We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to the other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment-incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.

Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.

Retinal image registration as a tool for supporting clinical applications

Background and Objective: The study of small vessels allows for the analysis and diagnosis of diseases with strong vasculopathy. This type of vessels can be observed non-invasively in the retina via fundoscopy. The analysis of these vessels can be facilitated by applications built upon Retinal Image Registration (RIR), such as mosaicing, Super Resolution (SR) or eye shape estimation. RIR is challenging due to possible changes in the retina across time, the utilization of diverse acquisition devices with varying properties, or the curved shape of the retina.Methods: We employ the Retinal Image Registration through Eye Modelling and Pose Estimation (REMPE) framework, which simultaneously estimates the cameras’ relative poses, as well as eye shape and orientation to develop RIR applications and to study their effectiveness.Results: We assess quantitatively the suitability of the REMPE framework towards achieving SR and eye shape estimation. Additionally, we provide indicative results demonstrating qualitatively its usefulness in the context of longitudinal studies, mosaicing, and multiple image registration. Besides the improvement over registration accuracy, demonstrated via registration applications, the most important novelty presented in this work is the eye shape estimation and the generation of 3D point meshes. This has the potential for allowing clinicians to perform measurements on 3D representations of the eye, instead of doing so in 2D images that contain distortions induced because of the projection on the image space.Conclusions: RIR is very effective in supporting applications such as SR, eye shape estimation, longitudinal studies, mosaicing and multiple image registration. Its improved registration accuracy compared to the state of the art translates directly in improved performance when supporting the aforementioned applications.

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images.

A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.