Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy

Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework

BACKGROUND AND OBJECTIVE

Electroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls.

METHODS

This study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data.

RESULTS

We employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients.

CONCLUSIONS

This emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.

Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals

Heart rate variability (HRV) is used as an index reflecting the adaptability of the autonomic nervous system to external stimuli and can be used to detect various heart diseases. Since HRVs are the time series signal with nonlinear property, entropy has been an attractive analysis method. Among the various entropy methods, dispersion entropy (DE) has been preferred due to its ability to quantify the time series' underlying complexity with low computational cost. However, the order between patterns is not considered in the probability distribution of dispersion patterns for computing the DE value. Here, a multiscale cumulative residual dispersion entropy (MCRDE), which employs a cumulative residual entropy and DE estimation in multiple temporal scales, is presented. Thus, a generalized and fast estimation of complexity in temporal structures is inherited in the proposed MCRDE. To verify the performance of the proposed MCRDE, the complexity of inter-beat interval obtained from ECG signals of congestive heart failure (CHF), atrial fibrillation (AF), and the healthy group was compared. The experimental results show that MCRDE is more capable of quantifying physiological conditions than preceding multiscale entropy methods in that MCRDE achieves more statistically significant cases in terms of p-value from the Mann-Whitney test.

Image to English translation and comprehension: INT2-VQA method based on inter-modality and intra-modality collaborations

Existing visual question answering methods typically concentrate only on visual targets in images, ignoring the key textual content in the images, thereby limiting the depth and accuracy of image content comprehension. Inspired by this, we pay attention to the task of text-based visual question answering, address the performance bottleneck issue caused by over-fitting risk in existing self-attention-based models, and propose a scenario text visual question answering method called INT2-VQA that fuses knowledge manifestation based on inter-modality and intra-modality collaborations. Specifically, we model the complementary priori knowledge of locational collaboration between visual targets and textual targets across modalities and the contextual semantical collaboration among textual word targets within a modality. Based on this, a universal knowledge-reinforced attention module is designed to achieve a unified encoding manifestation of both relations. Extensive ablation experiments, contrast experiments, and visual analyses demonstrate the effectiveness of the proposed method and prove its superiority over the other state-of-the-art methods.

Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration

In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.

Cumulative Residual Tsallis Entropy-Based Test of Uniformity and Some New Findings

The Tsallis entropy is an extension of the Shannon entropy and is used extensively in physics. The cumulative residual Tsallis entropy, which is a generalization of the Tsallis entropy, plays an important role in the measurement uncertainty of random variables and has simple relationships with other important information and reliability measures. In this paper, some novel properties of the cumulative residual Tsallis entropy are disclosed. Moreover, this entropy measure is applied to testing the uniformity, where the limit distribution and an approximation of the distribution of the test statistic are derived. In addition, the property of stability is discussed. Furthermore, the percentage points and power against seven alternative distributions of this test statistic are presented. Finally, to compare the power of the suggested test with that of other tests of uniformity, a simulation study is conducted.

Entropy analysis of human death uncertainty

Uncertainty about the time of death is part of one's life, and plays an important role in demographic and actuarial sciences. Entropy is a measure useful for characterizing complex systems. This paper analyses death uncertainty through the concept of entropy. For that purpose, the Shannon and the cumulative residual entropies are adopted. The first may be interpreted as an average information. The second was proposed more recently and is related to reliability measures such as the mean residual lifetime. Data collected from the Human Mortality Database and describing the evolution of 40 countries during several decades are studied using entropy measures. The emerging country and inter-country entropy patterns are used to characterize the dynamics of mortality. The locus of the two entropies gives a deeper insight into the dynamical evolution of the human mortality data series.

A Robust Algorithm Based on Phase Congruency for Optical and SAR Image Registration in Suburban Areas

Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L₁-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

Uncertainty of financial time series based on discrete fractional cumulative residual entropy

Cumulative residual entropy (CRE) is a measure of uncertainty and departs from other entropy in that it is established on cumulative residual distribution function instead of density function. In this paper, we prove some important properties of discrete CRE and propose fractional multiscale cumulative residual entropy (FMCRE) as a function of fractional order α, which combines CRE with fractional calculus, probability of permutation ordinal patterns, and multiscale to overcome the limitation of CRE. After adding amplitude information through weighted permutation ordinal patterns, we get fractional weighted multiscale cumulative residual entropy (FWMCRE). FMCRE and FWMCRE extend CRE into a continuous family and can be used in more situations with a suitable parameter. Moreover, they can capture long-range phenomena more clearly and have higher sensitivity to the signal evolution. Results from simulated data verify that FMCRE and FWMCRE can identify time series accurately and have immunity to noise. We confirm that the length of time series has little effect on the accuracy of distinguishing data, and even short series can get results exactly. Finally, we apply FMCRE and FWMCRE on stock data and confirm that they can be used as metrics to measure uncertainty of the system as well as distinguishing signals. FWMCRE can also track changes in stock markets and whether adding amplitude information must be decided by the characteristics of data.

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation. To take the spatial information between voxels into account, the distance of gradient distributions is constructed. The goal of nonrigid alignment is to find the optimal solution of a cost function including a dissimilarity measure, a regularization term, and a distance term between the gradient distributions of two images to be registered, which would achieve a minimum value when two misaligned images are perfectly registered using limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization scheme. To evaluate the test results of our presented algorithm in non-rigid medical image registration, experiments on simulated three-dimension (3D) brain magnetic resonance imaging (MR) images, real 3D thoracic computed tomography (CT) volumes and 3D cardiac CT volumes were carried out on elastix package. Comparison studies including mutual information (MI) and the approach without considering spatial information were conducted. These results demonstrate a slight improvement in accuracy of non-rigid registration.

Efficient and Robust Direct Image Registration Based on Joint Geometric and Photometric Lie Algebra

This paper considers the joint geometric and photometric image registration problem. The inverse compositional (IC) algorithm and the efficient second-order minimization (ESM) algorithm are two typical efficient methods applied to the geometric registration problem. Their efficiency stems from the utilization of the group structure of geometric transformations. To allow for photometric variations, the dual IC algorithm (DIC) proposed by Bartoli performs joint geometric and photometric image registration by extending the IC algorithm. The group structures of both geometric and photometric transformations are exploited. Despite the robustness to large photometric variations, DIC is vulnerable to large geometric deformations. The ESM algorithm is extended by Silveira et al. to address photometric variations. In their approach, the photometric transformations are modeled in Euclidean space. Their approach is robust to relatively large geometric and photometric transformations; however, it is not efficient for large photometric variations. We propose a new efficient and robust image registration method by exploiting the non-Euclidean Lie group structure of joint geometric and photometric transformations for both grayscale and color images. The image registration is formulated as a nonlinear least squares problem. In our method, the geometric and photometric transformations are jointly parameterized by their corresponding Lie algebras. Based on this parameterization approach, the second-order approximation strategy of ESM is employed to optimize the joint geometric and photometric parameters. The error function in the nonlinear least squares problem is approximated by a second-order Taylor expansion with respect to joint geometric and photometric parameters without computing the Hessian matrix. For further efficiency, independent convergence criteria for geometric and photometric parameters are used in the iterative optimization process. The superiority of our proposed method over the previous methods, in terms of efficiency, accuracy, and robustness, is demonstrated through extensive experiments on synthetic and real data.

An atlas-based multimodal registration method for 2D images with discrepancy structures

An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Graphical Abstract An Atlas-based multimodal registration method schematic diagram.

Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information

The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulates an MRF model while grayscale and gradient statistical information of a multimodal image is employed for the evaluation of similarity while the spatial weighting function is optimized simultaneously. Besides, the value space is discretized to improve the convergence speed. The developed automatic approach was validated both qualitatively and quantitatively, demonstrating its potential for a variety of multimodal remote sensing datasets and scenes. As for the registration accuracy, the average target registration error of the proposed framework is less than 1 pixel, while the maximum displacement error is less than 1 pixel. Compared with the polynomial model registration based on manual selection, the registration accuracy has been significantly improved. In the meantime, the proposed approach had the partial applicability for the multimodal image registration of large deformation scenes. It is also proved that the proposed registration framework using grayscale and gradient information outperforms the MRF-based registration using only grayscale information and only gradient information while the proposed registration framework using Gaussian function as spatial weighting function is superior to that using distance inverse weight method.

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.

Inter-scanner Variation Independent Descriptors for Constrained Diffeomorphic Demons Registration of Retina OCT

PURPOSE

OCT offers high in-plane micrometer resolution, enabling studies of neurodegenerative and ocular-disease mechanisms via imaging of the retina at low cost. An important component to such studies is inter-scanner deformable image registration. Image quality of OCT, however, is suboptimal with poor signal-to-noise ratio and through-plane resolution. Geometry of OCT is additionally improperly defined. We developed a diffeomorphic deformable registration method incorporating constraints accommodating the improper geometry and a decentralized-modality-insensitive-neighborhood-descriptors (D-MIND) robust against degradation of OCT image quality and inter-scanner variability.

METHOD

The method, called D-MIND Demons, estimates diffeomorphisms using D-MINDs under constraints on the direction of velocity fields in a MIND-Demons framework. Descriptiveness of D-MINDs with/without denoising was ranked against four other shape/texture-based descriptors. Performance of D-MIND Demons and its variants incorporating other descriptors was compared for cross-scanner, intra- and inter-subject deformable registration using clinical retina OCT data.

RESULT

D-MINDs outperformed other descriptors with the difference in mutual descriptiveness between high-contrast and homogenous regions > 0.2. Among Demons variants, D-MIND-Demons was computationally efficient, demonstrating robustness against OCT image degradation (noise, speckle, intensity-non-uniformity, and poor through-plane resolution) and consistent registration accuracy [(4±4 μm) and (4±6 μm) in cross-scanner intra- and inter-subject registration] regardless of denoising.

CONCLUSIONS

A promising method for cross-scanner, intra- and inter-subject OCT image registration has been developed for ophthalmological and neurological studies of retinal structures. The approach could assist image segmentation, evaluation of longitudinal disease progression, and patient population analysis, which in turn, facilitate diagnosis and patient-specific treatment.

3D nonrigid medical image registration using a new information theoretic measure

This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy.

Why Does Mutual-Information Work for Image Registration? A Deterministic Explanation

This paper proposes a deterministic explanation for mutual-information-based image registration (MI registration). The explanation is that MI registration works because it aligns certain image partitions. This notion of aligning partitions is new, and is shown to be related to Schur- and quasi-convexity. The partition-alignment theory of this paper goes beyond explaining mutual- information. It suggests other objective functions for registering images. Some of these newer objective functions are not entropy-based. Simulations with noisy images show that the newer objective functions work well for registration, lending support to the theory. The theory proposed in this paper opens a number of directions for further research in image registration. These directions are also discussed.

Multimodal registration via mutual information incorporating geometric and spatial context

Multimodal image registration is a class of algorithms to find correspondence from different modalities. Since different modalities do not exhibit the same characteristics, finding accurate correspondence still remains a challenge. To deal with this, mutual information (MI)-based registration has been a preferred choice as MI is based on the statistical relationship between both volumes to be registered. However, MI has some limitations. First, MI-based registration often fails when there are local intensity variations in the volumes. Second, MI only considers the statistical intensity relationships between both volumes and ignores the spatial and geometric information about the voxel. In this work, we propose to address these limitations by incorporating spatial and geometric information via a 3D Harris operator. In particular, we focus on the registration between a high-resolution image and a low-resolution image. The MI cost function is computed in the regions where there are large spatial variations such as corner or edge. In addition, the MI cost function is augmented with geometric information derived from the 3D Harris operator applied to the high-resolution image. The robustness and accuracy of the proposed method were demonstrated using experiments on synthetic and clinical data including the brain and the tongue. The proposed method provided accurate registration and yielded better performance over standard registration methods.

Repeat validation of a method to measure in vivo three dimensional hip kinematics using computed tomography and fluoroscopy

Total hip arthroplasty or THA is a surgical procedure for the relief of significant disabling pain caused by osteoarthritis or hip fracture. Knowledge of the 3D kinematics of the hip during specific functional activities is important for THA component design. In this paper we compare kinematic measurements obtained by a new 2D-3D registration algorithm with measurements provided by the gold standard roentgen stereo analysis (RSA). The study validates a promising method for investigating the kinematics of some pathologies, which involves fitting three dimensional patient specific 3D CT scans to dynamic fluoroscopic images of the hip during functional activities. This is the first study in which single plane fluoroscopy has been used for kinematic measurements of natural hip bones. The main focus of the study is on the out-of-plane translation and rotation movements which are difficult to measure precisely using a single plane approach. From our experimental results we found that the precision of our proposed approach compares favourably with that of the most recent dual plane fluoroscopy approach.

Two phase non-rigid multi-modal image registration using Weber local descriptor-based similarity metrics and normalized mutual information

Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Existing image registration methods based on similarity metrics such as mutual information (MI) and sum of squared differences (SSD) cannot achieve either high registration accuracy or high registration efficiency. To address this problem, we propose a novel two phase non-rigid multi-modal image registration method by combining Weber local descriptor (WLD) based similarity metrics with the normalized mutual information (NMI) using the diffeomorphic free-form deformation (FFD) model. The first phase aims at recovering the large deformation component using the WLD based non-local SSD (wldNSSD) or weighted structural similarity (wldWSSIM). Based on the output of the former phase, the second phase is focused on getting accurate transformation parameters related to the small deformation using the NMI. Extensive experiments on T1, T2 and PD weighted MR images demonstrate that the proposed wldNSSD-NMI or wldWSSIM-NMI method outperforms the registration methods based on the NMI, the conditional mutual information (CMI), the SSD on entropy images (ESSD) and the ESSD-NMI in terms of registration accuracy and computation efficiency.

SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine

This paper proposes a scheme consisting of two novel components to recognize multiple hand motions from surface electromyography (SEMG). First, we use the cumulative residual entropy (CREn), a measure of uncertainty in a random variable, as the feature. Second, we employ the extreme learning machine (ELM), a fast and effective classifier using single-hidden layer feedforward neural network with additive neurons, to distinguish different motions. To evaluate performance of the proposed system, we compare CREn with fuzzy entropy, sample entropy, and approximate entropy, and a state-of-the-art time-domain feature; and ELM with linear discriminant analysis and support vector machine. They are tested on four channel SEMG signals acquired from ten normal subjects. Experimental results indicate that the classification accuracies of CREn are not only better than those of other entropies with all the classifiers, but also comparable to the time-domain feature for all the segment lengths of 200, 250 and 1,000 ms with all classifiers that are evaluated. Furthermore, the computational complexity of CREn is lower than those of other features, and ELM performs significantly faster than other classifiers without sacrificing any performance. It suggests that the proposed CREn-ELM scheme has the potential to be applied to real-time control of SEMG-based multifunctional prosthesis.

New registration algorithm for determining 3D knee kinematics using CT and single-plane fluoroscopy with improved out-of-plane translation accuracy

To understand the kinematic effects of surgery, arthroplasty or conservative treatments, a noninvasive system to capture accurate 3D imaging of functional activities in prospective, controlled studies is required. To provide such a technique, a new algorithm was developed to register 3D CT data of normal bones to the same bones in a 2D fluoroscopy frame. The algorithm produces a digitally reconstructed radiograph (DRR) from the CT data and then filters this to produce an edge-enhanced image. The resulting image is then registered with an edge-enhanced version of the fluoroscopy frame using a new similarity measure called Cross-Correlation Residual Entropy (CCRE). The system was evaluated by implanting tantalum beads into three cadaveric knees to act as fiducial markers. The knees were flexed between 0 degrees and 70 degrees , and single-plane fluoroscopy data of the knees were acquired. CT data of the femur and tibia were then individually registered to the fluoroscopy images. No significant measurement bias was observed, and the standard deviation of the error in bead positions was 0.38 mm for in-plane translation and 0.42 degrees for rotation. To determine the accuracy of the registration algorithm for out-of-plane translations, fluoroscopy frames were scaled in size by fixed increments; the average standard deviation of the errors for out-of-plane translation was 0.65 mm. The ability to obtain such accurate 3D motion data from a noninvasive technique will enable prospective, longitudinal, and controlled studies of reconstruction surgery, and conservative management of joint pathologies.

A computationally efficient approach for 2D-3D image registration

2D-3D image registration has become an important tool in many clinical applications such as image-guided surgery and the kinematic analysis of bones in knee and ankle joints. A limitation of this approach is the need to recalculate the voxel values in the 3D volume for every iteration of the registration procedure. In this paper we propose a new 2D-3D image registration algorithm which uses the projected 2D data from the original 3D CT volume. For the majority of the iterations of the algorithm, only this 2D data is updated rather than the 3D volume. Experimental results show that similar registration accuracy to the approach which employs 3-D updates at every iteration can be achieved with our method if we employ 3-D updates only in the last few iterations. As a result of reducing the number of 3-D updates, the proposed approach reduces the time required to perform the registration by approximately a factor of five.

A comparison of the 3D kinematic measurements obtained by single-plane 2D-3D image registration and RSA

3D computed tomography (CT) to single-plane 2D fluoroscopy registration is an emerging technology for many clinical applications such as kinematic analysis of human joints and image-guided surgery. However, previous registration approaches have suffered from the inaccuracy of determining precise motion parameters for out-of-plane movements. In this paper we compare kinematic measurements obtained by a new 2D-3D registration algorithm with measurements provided by the gold standard Roentgen Stereo Analysis (RSA). In particular, we are interested in the out-of-plane translation and rotations which are difficult to measure precisely using a single plane approach. Our experimental results show that the standard deviation of the error for out-of-plane translation is 0.42 mm which compares favourably to RSA. It is also evident that our approach produces very similar flexion/extension, abduction/adduction and external knee rotation angles when compared to RSA.

A new multi-modal similarity measure for fast gradient-based 2D-3D image registration

2D-3D image registration has been adopted in many clinical applications such as image-guided surgery and the kinematic analysis of bones in knee and ankle joints. In this paper we propose a new single-plane 2D-3D registration algorithm which requires far less iteration than previous techniques. The new algorithm includes a new multi-modal similarity measure and a novel technique for the analytic calculation of the required gradients. Our experimental results show that, when compared to existing gradient and non-gradient based techniques, the proposed algorithm has a wider range of initial poses for which registration can be achieved and requires significantly fewer iterations to converge to the true 3D position of the anatomical structure.