Automatic multisensor image registration using wavelet transform

Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece.

Fine registration is a fundamental step for further application of remote sensing images. Focused on deficiencies in traditional manual registration, this paper presents a new method for automatic fine registration of multi-spectral images. To make the most of image information, the algorithm detects and matches feature points in the selected bands. Then pick up the common control points which contain more reliability relative to others after eliminating wrong matching points. The last registration model can be built based on common control points and the points selected by common ones. Experimental results with Landsat TM5 images demonstrate that the method is more accurate and suitable for automatic batch processing.

Abstract. For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

The geometric correction of images under the scope of remote sensing applications is still mostly a manual work. This is a time and effort consuming task associated with an intra- and inter-operator subjectivity. One of the main reasons may be the lack of a proper evaluation of the different available automatic image registration (AIR) methods, since some of them are only adequate for certain types of applications/data. In order to fulfill a gap in this context, a first reference dataset of pairs of images comprising some types of geometric distortions was created, different spatial and spectral resolutions, and divided according to the Level 1 of CORINE Land Cover nomenclature (European Environment Agency). This dataset will allow for gaining perception of the abilities and limitations of some AIR methods. Some AIR methods were evaluated in this work, including the traditional correlation-based method and the SIFT approach, for which a set of measures for an objective evaluation of the geometric correction process quality was computed for every combination of pair of images/AIR method. The reference dataset is available from an internet address, being expected that it becomes a channel of interaction among the remote sensing community interested in this field.

As the need for automating registration techniques is recognized, the authors feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. The authors present the first steps towards this quantitative evaluation: a few automatic image registration algorithms are described and first results of their evaluation are presented for three different datasets.

With the increasing importance of multiple multiplatform remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Firstly, an overview of development of automatic and parallel global image registration is given. And then, based on the analyses of existing three parallel methods of wavelet-based global registration, a new parallel strategy is proposed. Moreover, towards the quantitative evaluation, first results of the intercomparision of four parallel global registration algorithms are presented in theory and in experiments.

Automatic Registration between Real-Time Ultrasonography and Pre-Procedural Magnetic Resonance Images: A Prospective Comparison between Two Registration Methods by Liver Surface and Vessel and by Liver Surface Only

A method for automatic image registration which is characterized by its insensitivity to scaling, rotation, and intensity changes is described. The method is based on similarity assessment of the structures in the images and on a check of their spatial arrangement. Pairs of structures that correspond to each other provide sets of control points to geometric mapping functions. An application of the method to remote-sensing image alignment with a reference map is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

UAV remote sensing is low cost and convenient, but traditional methods for image registration and mosaic are low efficient, since it is hard to find ground control points for large quantity of images with small scale. In this study, adjacency transfer algorithm is used between every two adjacent images to automate the work flow: First SIFT(Scale-invariant feature transform) algorithm is employed to detect correspondence points, then polynomial algorithm is used for geometric correction, and finally all the digital orthophoto images are mosaiced into one image. Developed in VC program environment, this study builds its prototype system, which can produce digital orthophoto images with relative high efficiency. Through image overlay and visual interpretation, no image displacement was found. Adjacency transfer algorithm used in this study provides a new way for efficienct and automatic UAV image registration and mosaic.

Automatic image registration is important for many multiframe-based image analysis applications. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching.

A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy. The variational model considers local transformation, global smoothness and visual constraints. To account for intensity variations, we incorporate change of local contrast and brightness into our model. Firstly, the data entry of registration model is built according to the root-mean-square error of intensity; secondly, adaptive constraint using &lt;i&gt;H&lt;/i&gt;&lt;sup&gt;1&lt;/sup&gt; half norm is used to ensure the global smooth in the model; finally, in order to make sure that the spatial attributes of the image satisfy the visual requirements and without distortion, the linear features are used as priori constraints. During the solution of model parameters, the whole image is used to globally estimate the transformation parameters, and then local estimation of the parameters is taken in a small neighbor. The entire procedure is built upon a multi-level differential framework, and the transformation parameters are calculated iteratively, which can consider both global smoothness and local distortion. To assess the quality of the proposed method, ZY-3 satellite images were used. Visual and quantitative analysis proved that the proposed method can significantly improve the registration precision.

Radiotherapy is a major treatment for head and neck cancer. Currently, computed tomography (CT) is utilized to delineate the target area and make radiotherapy plans. Compared with CT images, magnetic resonance (MR) images have excellent soft tissue contrast, which can distinguish normal surrounding tissues. It is necessary to register MR and CT images since it helps physicists to improve the accuracy of radiotherapy plans. Most of the current registrations require manual intervention to select regions of interest, which increases the workload of doctors and the time of registration to a certain extent. In this paper, an automatic registration method is proposed to delineate the regions of interest. Herein, surface meshes are extracted from the CT and MR images and utilized to perform the surface matching, then the regions of interest are extracted automatically by calculating the overlapped regions between the surface mesh of the surface-aligned MR images and CT images. Furthermore, a multi-level resolution registration mechanism is utilized to improve the registration speed. Surface matching is performed using low-resolution images to obtain transformation parameters as initial parameters for rigid registration followed by deformable registration. The experiments demonstrate that our proposed method performs better in the registration speed and accuracy over the conventional methods of delineating regions of interest manually.

Radiation therapy of prostate cancer requires a high geometric accuracy of dose delivery. Unfortunately, this accuracy can be hardly obtained because of imperfect patients repositioning throughout the preparation and execution of the treatment. In order to correct for such errors we developed methods for online registration of portal images (PI) and digitally reconstructed radiographs (DRR). To improve the preconditions for registration of the Pis and DRRs image preprocessing, such as contrast-limited adaptive histogram equalization (CLAHE) of the Pis, noise and edge filtering of the Pis and the DRRs, has been performed. Then, similarity measures as mean square error, cross correlation and mutual information were investigated. A fully automatic bony-based registration method has been developed. As a reference method a semi-automatic stereometric fiducial-based registration has also been developed. This method uses gold seeds implanted into the prostate as internal fiducial markers for assessment of the similarity measures. Small lead spheres as well were adhered as external fiducial markers on the Alderson phantom. This method was also used for the verification of the automatic bony-based registration. The registration of repositioning was then performed fully automatically in less than 30 s with the Alderson phantom and with the patient data. At the present stage, there remains a registration error of less than 2 mm.

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues’ irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.

Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis