Classical Registration Research Articles

Non-rigid image registration with anatomical structure constraint for assessing locoregional therapy of hepatocellular carcinoma

PurposeAssessing the treated region with locoregional therapy (LT) provides valuable information for predicting hepatocellular carcinoma (HCC) recurrence. The commonly used of assessment method is inefficient because it only compares two-dimensional CT images manually. In our previous work, we automatically aligned the two CT volumes to evaluate the therapeutic efficiency using registration algorithms. The non-rigid registration is applied to capture local deformation, however, it usually destroys internal structure. Taking these into consideration, this paper proposes a novel non-rigid registration approach for evaluating LT of HCC to maintain the image integrity. MethodIn our registration algorithm, a global affine transformation combined with localized cubic B-spline is used to estimate the significant non-rigid motions of two livers. The proposed method extends a classical non-rigid registration based on mutual information (MI) that uses an anatomical structure term to constrain the local deformation. The energy function can be defined based on the total one associated with the anatomical structure and deformation information. Optimal transformation is obtained by finding the equilibrium state in which the total energy is minimized, indicating that the anatomical landmarks have found their correspondences. Thus, we can use the same transformation to automatically transform the ablative region to the optimal position. ResultsRegistration accuracy is evaluated using the clinical data. Improved results are obtained with respect to all criteria in our proposed method (MI-LC) than those in the MI-based non-rigid registration. The landmark distance error (LDE) of MI-LC is decreased by an average of 3.93mm compared to the case of MI-based registration. Moreover, it could be found regardless of how many landmarks applied in our proposed method, a significant reduction in LDE values using registrations based on MI-LC compared with those based on MI is confirmed. ConclusionOur proposed approach can guarantee the continuity, the accuracy and the smoothness of structures by constraining the anatomical features. The results clearly indicate that our method can retain the local deformation of the image. In addition, it assures the anatomical structure stability.

Registration of a hybrid robot using the Degradation-Kronecker method and a purely nonlinear method

SUMMARYAlthough the registration of a robot is crucial in order to identify its pose with respect to a tracking system, there is no reported solution to address this issue for a hybrid robot. Different from classical registration, the registration of a hybrid robot requires the need to solve an equation with three unknowns where two of these unknowns are coupled together. This property makes it difficult to obtain a closed-form solution. This paper is a first attempt to solve the registration of a hybrid robot. The Degradation-Kronecker (D-K) method is proposed as an optimal closed-form solution for the registration of a hybrid robot in this paper. Since closed-form methods generally suffer from limited accuracy, a purely nonlinear (PN) method is proposed to complement the D-K method. With simulation and experiment results, it has been found that both methods are robust. The PN method is more accurate but slower as compared to the D-K method. The fast computation property of the D-K method makes it appropriate to be applied in real-time circumstances, while the PN method is suitable to be applied where good accuracy is preferred.

Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery.

Despite its widespread availability and success in open cranial neurosurgery, image-guidance technology remains more limited in use in open spinal procedures, in large part, because of patient registration challenges. In this study, we evaluated the feasibility of using intraoperative stereovision (iSV) for accurate, efficient, and robust patient registration in an open spinal fusion surgery. Geometrical surfaces of exposed vertebrae were first reconstructed from iSV. A classical multistart registration was then executed between point clouds generated from iSV and preoperative computed tomography images of the spine. With two pairs of feature points manually identified to facilitate the registration, an average registration accuracy of 1.43 mm in terms of surface-to-surface distance error was achieved in eight patient cases using a single iSV image pair sampling 2-3 vertebral segments. The iSV registration error was consistently smaller than the conventional landmark approach for every case (average of 2.02 mm with the same error metric). The large capture ranges (average of 23.8 mm in translation and 46.0° in rotation) found in the iSV patient registration suggest the technique may offer sufficient robustness for practical application in the operating room. Although some manual effort was still necessary, the manually-derived inputs for iSV registration only needed to be approximate as opposed to be precise and accurate for the manual efforts required in landmark registration. The total computational cost of the iSV registration was 1.5 min on average, significantly less than the typical ∼30 min required for the landmark approach. These findings support the clinical feasibility of iSV to offer accurate, efficient, and robust patient registration in open spinal surgery, and therefore, its potential to further increase the adoption of image guidance in this surgical specialty.

Edge-Based Multi-modal Registration and Application for Night Vision Devices

Multi-modal image sequence registration is a challenging problem that consists in aligning two image sequences of the same scene acquired with a different sensor, hence containing different characteristics. We focus in this paper on the registration of optical and infra-red image sequences acquired during the flight of a helicopter. Both cameras are located at different positions and they provide complementary informations. We propose a fast registration method based on the edge information: a new criterion is defined in order to take into account both the magnitude and the orientation of the edges of the images to register. We derive a robust technique based on a gradient ascent and combined with a reliability test in order to quickly determine the optimal transformation that matches the two image sequences. We show on real multi-modal data that our method outperforms classical registration methods, thanks to the shape information provided by the contours. Besides, results on synthetic images and real experimental conditions show that the proposed algorithm manages to find the optimal transformation in few iterations, achieving a rate of about 8 frames per second.

Optimized Method for Generating and Acquiring GPS Gold Codes

We propose a simpler and faster Gold codes generator, which can be efficiently initialized to any desired code, with a minimum delay. Its principle consists of generating only one sequence (code number 1) from which we can produce all the other different signal codes. This is realized by simply shifting this sequence by different delays that are judiciously determined by using the bicorrelation function characteristics. This is in contrast to the classical Linear Feedback Shift Register (LFSR) based Gold codes generator that requires, in addition to the shift process, a significant number of logic XOR gates and a phase selector to change the code. The presence of all these logic XOR gates in classical LFSR based Gold codes generator provokes the consumption of an additional time in the generation and acquisition processes. In addition to its simplicity and its rapidity, the proposed architecture, due to the total absence of XOR gates, has fewer resources than the conventional Gold generator and can thus be produced at lower cost. The Digital Signal Processing (DSP) implementations have shown that the proposed architecture presents a solution for acquiring Global Positioning System (GPS) satellites signals optimally and in a parallel way.

A Robust Class of Data Languages and an Application to Learning

We introduce session automata, an automata model to process data words, i.e., words over an infinite alphabet. Session automata support the notion of fresh data values, which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. Session automata have an expressiveness partly extending, partly reducing that of classical register automata. We show that, unlike register automata and their various extensions, session automata are robust: They (i) are closed under intersection, union, and (resource-sensitive) complementation, (ii) admit a symbolic regular representation, (iii) have a decidable inclusion problem (unlike register automata), and (iv) enjoy logical characterizations. Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries.

Non-rigid landmark-based large-scale image registration in 3-D reconstruction of mouse and rat kidney nephrons

BackgroundSerial histological sections are suffering from mechanical distortions that disturb the reconstruction of 3-D objects. We have corrected such artifacts with a non-rigid landmark-based method that respects the original geometry in the tissue block. The method is exemplified on a large scale in the registration of semi-thin serial sections of the mouse and rat kidneys, and has been tested on FFPE-sections. AimIn this study of mouse and rat kidneys, we have measured and characterized the deformations introduced in the preparation of 2.5-μm-thick Epon sections and then eliminated them by a landmark-based non-rigid transformation (NRT). MethodsWe obtained 2.5-μm-thick serial Epon sections from three mouse kidneys and three rat kidneys for 3-D reconstruction of the nephron tubules. First, the images from 3000 serial mouse and 13,000 serial rat sections underwent a classic rigid registration (CRR), and the distortions were measured and indexed. The section images underwent a further NRT in order to compensate for the deformations. The NRT used is a classic interactive landmark-based approach. The quality of the NRT was verified by comparing the geometry of the transformed images with corresponding block images. ResultsAfter CRR, the 2.5-μm-thick sections had a linear deformation of up to 2%, the tubular lengths were overestimated with up to 1.5×, and it was most difficult to trace the tubules from section to section. After the additional NRT, the geometry of the images reflected the original geometry in the block, the tubular lengths were no longer overestimated, and the NRT highly facilitated the tracing of the tubular system. ConclusionsNRT has facilitated the tracing of the tubular system in kidneys, a tracing, which would otherwise have been most difficult to perform. NRT has yielded substantial new knowledge to segmental and spatial nephron organization in the mouse and rat kidneys.

Бесконтактные электроды

This paper reviews current approaches to the registration of the electrocardiogram for a long period. A review of existing solutions based on contact adhesive and fabric electrodes and non-contact ones is carried out. The last type of electrodes is offered as an optimal solution. The fundamental principle of operation of the new electrodes is fixing the capacitance parameters, in contrast to the classical voltage registration. The ability to operate through a tissue, thereby providing a suitable level of comfort for long-term utilizing of these electrodes is advantage in comparison with the contact adhesive and the contact dry textiles ones for electrocardiogram monitoring. The main problem is that all types of the electrodes are affected with noise spoiling useful signal when the person moves. To minimize the influence of artifacts, some developers of registration systems for measuring electrical activity of heart and brain have chosen the way of manufacturing high-cost microchips, what can be an obstacle for non-contact sensors penetrating of the public market. The authors propose an approach to solving the difficulties in signal recordings in indirect way without using of specialized microchip. The results of their own research are presented.

A Survey of Recent and Classical Image Registration Methods

Image Registration is the process of aligning two or more images of the same scene taken from different viewpoints, at different times, from different sensors. Image registration aligns two images, i.e., base image and reference image, geometrically. There are different approaches of image registration and these approaches are categorized according to their nature that is areas based and feature based. These approaches are also categorized according to the four simple steps of image registration procedure: feature detection, feature matching, function mapping and transformation and re-sampling. Advantages and disadvantages of different methods are discussed in the paper. The main aim of this paper is to provide the knowledge of different image registrations methods used in different application area.

Hierarchical model-based interferometric synthetic aperture radar image registration

With the rapid development of spaceborne interferometric synthetic aperture radar technology, classical image registration methods are incompetent for high-efficiency and high-accuracy masses of real data processing. Based on this fact, we propose a new method. This method consists of two steps: coarse registration that is realized by cross-correlation algorithm and fine registration that is realized by hierarchical model-based algorithm. Hierarchical model-based algorithm is a high-efficiency optimization algorithm. The key features of this algorithm are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-to-fine refinement strategy. Experimental results from different kinds of simulated and real data have confirmed that the proposed method is very fast and has high accuracy. Comparing with a conventional cross-correlation method, the proposed method provides markedly improved performance.

A novel quantum representation for log-polar images

The power of quantum mechanics has been extensively exploited to meet the high computational requirement of classical image processing. However, existing quantum image models can only represent the images sampled in Cartesian coordinates. In this paper, quantum log-polar image (QUALPI), a novel quantum image representation is proposed for the storage and processing of images sampled in log-polar coordinates. In QUALPI, all the pixels of a QUALPI are stored in a normalized superposition and can be operated on simultaneously. A QUALPI can be constructed from a classical image via a preparation whose complexity is approximately linear in the image size. Some common geometric transformations, such as symmetry transformation, rotation, etc., can be performed conveniently with QUALPI. Based on these geometric transformations, a fast rotation-invariant quantum image registration algorithm is designed for log-polar images. Performance comparison with classical brute-force image registration method reveals that our quantum algorithm can achieve a quartic speedup.

AUTOMATIC FEATURE-BASED POINT CLOUD REGISTRATION FOR A MOVING SENSOR PLATFORM

Abstract. The automatic and accurate alignment of multiple point clouds is a basic requirement for an adequate digitization, reconstruction and interpretation of large 3D environments. Due to the recent technological advancements, modern devices are available which allow for simultaneously capturing intensity and range images with high update rates. Hence, such devices can even be used for dynamic scene analysis and for rapid mapping which is particularly required for environmental applications and disaster management, but unfortunately, they also reveal severe restrictions. Facing challenges with respect to noisy range measurements, a limited non-ambiguous range, a limited field of view and the occurrence of scene dynamics, the adequate alignment of captured point clouds has to satisfy additional constraints compared to the classical registration of terrestrial laser scanning (TLS) point clouds for describing static scenes. In this paper, we propose a new methodology for point cloud registration which considers such constraints while maintaining the fundamental properties of high accuracy and low computational effort without relying on a good initial alignment or human interaction. Exploiting 2D image features and 2D/2D correspondences, sparse point clouds of physically almost identical 3D points are derived. Subsequently, these point clouds are aligned with a fast procedure directly taking into account the reliability of the detected correspondences with respect to geometric and radiometric information. The proposed methodology is evaluated and its performance is demonstrated for data captured with a moving sensor platform which has been designed for monitoring from low altitudes. Due to the provided reliability and a fast processing scheme, the proposed methodology offers a high potential for dynamic scene capture and analysis.

Shape registration for remote-sensing images with background variation

Image registration is a basic and key processing art for multi-sensor and/or multi-temporal remote-sensing images. Generally speaking, multi-temporal images covering the same region may contain different textures and/or changed backgrounds where the classical autonomous registration methods usually fail, especially those before and after disasters. The main reason is that a single pixel with surrounding region becomes unstable and cannot be applied for descriptor generation. However, the topology (or shape) between points should be viewed as steady description. In this article, we introduce a level line and consequent shape recognition algorithm (level line descriptor (LLD)) from close-range applications and propose an improved LLD for remote sensing. We present our analysis based on multi-temporal images with moderately changed backgrounds. The numerical experiments illustrate the operator's potential to become a relatively robust alternative for shape-based remote-sensing registration.

Toward physiologically motivated registration of diagnostic CT and PET/CT of lung volumes

Current clinical practice for lung cancer diagnosis and staging requires the acquisition of a diagnostic computed tomography (CT) as well as positron emission tomography (PET)/CT volumes from a hybrid scanner, where the CT is used for attenuation correction (AC-CT). The PET and AC-CT images are implicitly aligned, however, image registration between the diagnostic CT and PET volumes is needed to relate the anatomical correspondences. This is an important but difficult task due to the absence of a direct or functional relationship between the intensities. Alternatively, here we propose the diagnostic CT can be aligned with the PET image through an indirect registration process that uses the AC-CT. The resultant deformation field can then be used to align the PET image to the diagnostic CT. The registration of the diagnostic CT to AC-CT registration still presents two major challenges: (a) it is a multimodal registration problem since the diagnostic CT is acquired after the injection of a contrast agent, and (b) the type and amplitude of the deformations require a registration process that includes physically motivated properties to achieve an accurate and physiologically plausible alignment. The authors propose a new framework based on fluid registration including three physiologically motivated properties: (i) sliding motion of the lungs against the pleura; (ii) preservation of rigid structures; (iii) preservation of topology. The sliding motion is modeled using direction dependent regularization that decouples the tangential and the normal components of the external force term. The rigid shape of the bones is preserved using a spatially varying filter for the deformations. Finally, the topology is maintained using the concept of log-unbiased deformations. To solve the multimodal problem, the authors use local cross correlation (LCC) as the similarity measure. The proposed framework is first evaluated on CT lung image pairs representing several phases of the respiratory cycle. The authors show that their proposed framework has a superior performance compared to the classic fluid registration, both in quantitative and qualitative terms. The authors then evaluate the framework using ten real patient scans, where the authors also demonstrate how their physiologically motivated registration framework can be successfully applied to the task of fusing diagnostic CT with the PET/CT image volumes. The proposed registration framework has better results for the fusion of diagnostic CT with PET images in comparison to the classic fluid registration framework.

TH‐A‐BRA‐05: Linear Regression Analysis of 2D Projection Image Data of 6 Degrees‐ Of‐Freedom Transformed 3D Image Sets for Stereotactic Radiotherapy

Purpose: To improve accuracy and speed of identifying the 6 degrees‐of‐ freedom (DoF) transformation during patient positioning alignment for stereotactic radiotherapy by determining the linear relationship between two real‐time orthogonal 2D projection images and two digitally reconstructed radiographs (DRRs) generated from a 3D volume image. Methods: Each 2D image representing an independent transformation was characterized and defined by a shape function, a function parameterized by distance and pixel value. A principal component analysis was developed to find the linear relationship between the 2D projection images and the two DRRs generated from the 3D volume image for small‐scale transformations by matching the 2D images to a set of base functions established from singular transformations. The patient‐positioning algorithm calculates the 6 DoF transformation of the patient based upon two orthogonal real‐time 2D images and from the linear relationship established. The algorithm has been validated against classical image registration methods on patient data for runtime and positioning accuracy. Results: The algorithm has accuracy to at least 1 pixel (equivalent to 0.5 mm accuracy given this image resolution). The shape function characterizes each 2D image on an average of 1.78 seconds. The algorithm interprets the evaluated transformation as a linear combination of separate 6 DoF transformations. The principal component analysis allows for rapid and accurate position matching of the images, and provides the transformation needed to align the orthogonal images to the volume on an average of 3.32 seconds. Conclusions: The linear relationship developed from the shape function algorithm affords speed and accuracy of patient positioning during stereotactic radiotherapy treatment. The algorithm is fast and accurate for small‐order 6 DoF transformations. This algorithm indicates the potential for high precision of patient positioning from the interpolation and extrapolation of the linear relationships. This project is supported by the NCI Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center.

Quantitative evaluation of image registration techniques in the case of retinal images

In human retina observation (with non mydriatic optical microscopes), an image registration process is often employed to enlarge the field of view. Analyzing all the images takes a lot of time. Numerous techniques have been proposed to perform the registration process. Its good evaluation is a difficult question that is then raising. This article presents the use of two quantitative criterions to evaluate and compare some classical feature-based image registration techniques. The images are first segmented and the resulting binary images are then registered. The good quality of the registration process is evaluated with a normalized criterion based on the ϵ dissimilarity criterion, and the figure of merit criterion (fom), for 25 pairs of images with a manual selection of control points. These criterions are normalized by the results of the affine method (considered as the most simple method). Then, for each pair, the influence of the number of points used to perform the registration is evaluated.

Nonlinear Shape Registration without Correspondences

In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known shape and its distorted observation. Classical registration methods first establish correspondences between the shapes and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation without established correspondences. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.

Registering randomly oriented scenes through moving object attributes

We present a procedure to register motion imagery scenes captured from different viewpoints and under various orientations. Spatiotemporal (ST) trajectories of moving or changing objects and phenomena comprising a scene provide a series of interrelation attributes which are used to define the scene topology. Spatial proximity, temporal algebra, arbitrary directional measures, and shape complexity descriptors construct an abstract orientation-invariant space where conjugate pairs of trajectories between scenes are matched. This topology-centric matching procedure compensates for varying scale, occlusion, random error, and data uncertainty. Scene registration is performed through least square projective transformation. The procedure is quite precise, since the matched ST trajectories consist of populated sets of points following a common geometry compared with classic single point registration prone to errors or misinterpretation. Thus, while the matching procedure is abstract in nature, registration itself uses rigid geometry to provide accurate scene correspondence. Experimentation is quite encouraging even under extreme orientation, error, and occlusion conditions.

Variational Bayesian Super Resolution

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.

WE‐D‐BRB‐09: Quality Assurance (QA) for Deformable Registration: Measures for Evaluating Displacement Fields in Clinical Settings

Purpose: Deformable registration is an essential tool in adaptive radiotherapy, as it accounts for anatomical changes during treatment. In recent years, research has focused on proposing different deformable registration algorithms and inter‐comparing their results in academic settings. We contend that finding an efficient method for quality assurance of deformable registration in clinical settings is crucial for a global acceptance of adaptive radiotherapy. This study proposes measures derived from computational fluid dynamics as a simple and efficient tool to quantify a displacement field. Method: Our aim was to develop quantitative metrics of registration quality designed for routine use that are algorithm‐independent, labor‐efficient, and accurately identify errors in a given displacement field. The quality assurance (QA) framework identifies unrealistic anatomical motion through vortexes in the displacement field as detected using the CURL operator and presented as a vortex map overlaid on the original anatomy for a quick identification of problematic regions. Regions of compression/expansion are identified through the determinant of the Jacobian matrix. The warp energy measure is proposed as a global measure of displacement field smoothness. Results: The new evaluation approach was tested on numerous inter and intra patient cases using both single and multi‐modality registration algorithms. The CURL operator quantitatively detected errors in the displacement field and identified problematic regions that were invisible to classical voxel‐based evaluation methods. Warping emerges above 1 indicated unrealistic displacement fields. Conclusions: The proposed QA framework for deformable image registration provides increased usability and accuracy in detecting unrealistic warping over classical registration assessment methods. It is computationally efficient and provides a valuable platform for the clinical acceptance of adaptive therapy in the future.