Automatic Image Registration for Optical Techniques in Aerodynamic Test Facilities

For remote sensing applications, automatic image registration is an essential part for further image processing such as image fusion, change detection and so on. In this paper, we propose an effective and fast automatic image registration based on both global and local method. In the first stage, we extract SIFT features and make the affine transformation with RANSAC, a robust outlier removal method. After the image globally registered, uniform spacing control points are selected in the common area. Through template matching, control points are refined and more local to the image. At last thin-plate spline, a nonrigid image registration transformation function is applied to achieve local matching. The experiment results indicate that this method not only distinctive and robust in overall registration but also significantly improve matching performance in local area1.

Computed tomography (CT) has become the main contributor of the cumulative radiation exposure in radiology. Information on cumulative exposure history of the patient should be available for efficient management of radiation exposures and for radiological justification. To develop and evaluate automatic image registration for organ dose calculation in CT. Planning radiograph (scout) image data describing CT scan ranges from 15 thoracic CT examinations (9 men and 6 women) and 10 abdominal CT examinations (6 men and 4 women) were co-registered with the reference trunk CT scout image. 2-D affine transformation and normalized correlation metric was used for image registration. Longitudinal (z-axis) scan range coordinates on the reference scout image were converted into slice locations on the CT-Expo anthropomorphic male and female models, following organ and effective dose calculations. The average deviation of z-location of studied patient images from the corresponding location in the reference scout image was 6.2 mm. The ranges of organ and effective doses with constant exposure parameters were from 0 to 28.0 mGy and from 7.3 to 14.5 mSv, respectively. The mean deviation of the doses for fully irradiated organs (inside the scan range), partially irradiated organs and non-irradiated organs (outside the scan range) was 1%, 5%, and 22%, respectively, due to image registration. The automated image processing method to registrate individual chest and abdominal CT scout radiograph with the reference scout radiograph is feasible. It can be used to determine the individual scan range coordinates in z-direction to calculate the organ dose values. The presented method could be utilized in automatic organ dose calculation in CT for radiation exposure tracking of the patients.

Positron Emission Tomography (PET) is an essential tool for in vivo molecular imaging, due to its high sensitivity. Combined with the unparalleled soft tissue contrast of magnetic resonance imaging (MRI), PET-MRI allows for accurate quantification required for cell tracking studies. The purpose of this study is to develop a landmark based image registration program capable of fiducial registration, for application in a stem cell tracking study. PET and MR Images acquired with fiducial markers located in the field of view around the specimen were registered using our landmark based program, written in Matlab. The fiducial markers were also used to test the accuracy of our Matlab program, along with other automated image registration software including: automatic image registration (AIR), FMRIB's Linear Image Registration Tool (FLIRT), and Medical Image Processing, Analysis, and Visualization (MIPAV), all available online free for download. All of the programs were able to successfully register the images; however, our Matlab program provided the best registration accuracy, with a Fiducial Registration Error (FRE) of 1.36 mm. Our Matlab developed program is available free for download at www.sourceforge.net/p/matlabaffinereg.

Image registration of satellite imagery is challenging task as the image pair may have been captured by different sensors, view angles or at different times. Conventional satellite image registration tasks are achieved by feature based techniques which are to be identified and matched before estimating transforms and resample the moving or slave image to a fixed or master image. However, automatic image registration is very important requirement for voluminous data sets. Even if feature selection made automatic, even distribution and the density of feature points is always not ensured. A good alternative is to switch over to intensity based image registration techniques which are somewhat image content independent. We bring out details on an intensity based multi temporal satellite image registration task with metric, transform and optimization components based method. An optimization technique employed to find optimal transform parameters by maximizing the chosen similarity measure criteria, provides a robust image registration framework. Adaptive Stochastic Gradient Descent optimization (ASGD) for satellite image registration is a promising new technique, details of which are reported here.

<div class="htmlview paragraph">Recently, there has been a strong emphasis on aerodynamic and aeroacoustic wind tunnel testing of automobiles. While significant level resources have been spent on investigating aerodynamics, the methodology has not changed appreciably since the beginning of aerodynamics as a science. Over the past decade, a number of global flow diagnostic techniques have been developed that drastically increase the quality and quantity of data from wind tunnel testing. One of these technologies is the use of pressure sensitive luminescent coatings, known as pressure-sensitive paint, a method which has matured considerably since its inception and is now used extensively in aerospace applications with good results. The goal of this research is to implement this technology in the full scale testing of high performance automotive vehicles. This paper discusses the details of a preliminary test, such as technique, paint formulation, camera and lighting hardware, and data reduction and analysis. The test article was an actual high performance vehicle, a “stock car”, and the test facility was the Langley Full Scale Tunnel.</div>

Background: Structural and functional characterization of skeletal muscles is often assessed by histochemical techniques, which enable the classification into different fiber types by combining the reactions in serial transverse muscle cross-sections. A drawback is that a knowledgeable operator is required to combine and evaluate the reactions, which is a time-consuming, tedious, and subjective task. To enhance the speed and reproducibility of muscle fiber typing, the registration of serial transverse sections of muscle fibers images has been proposed as a preprocessing step. Methods: Three different registration methods were considered: first, a semi-automatic elastic point-based registration; second, an automatic (rigid, affine, and projective) whole image content-based registration; and, third, an automatic hierarchical elastic registration obtained by integration of the first two methods. The performances of the methods were tested on a database of 50 image stacks each containing three images of histochemically differently stained serial human muscle cross-sections. Results: The amounts of successful globally and locally registered stacks were approximately 20% for automatic rigid registration, 60% for automatic affine and projective registrations, and 80% for semi-automatic and automatic elastic registration. Conclusions: By using robust elastic registration methods, the automatic registration of serial transverse muscle fiber images seems feasible and might allow automatic muscle fiber typing, and consequently, improve the characterization of skeletal muscles. Cytometry 37:93–106, 1999. © 1999 Wiley-Liss, Inc.

An automatic 3D non-rigid body registration system based upon the genetic algorithm (GA) process is presented. The system has been successfully applied to 2D and 3D situations using both rigid-body and affine transformations. Conventional optimization techniques and gradient search strategies generally require a good initial start location. The GA approach avoids the local minima/maxima traps of conventional optimization techniques. Based on the principles of Darwinian natural selection (survival of the fittest), the genetic algorithm has two basic steps: 1. Randomly generate an initial population. 2. Repeated application of the natural selection operation until a termination measure is satisfied. The natural selection process selects individuals based on their fitness to participate in the genetic operations; and it creates new individuals by inheritance from both parents, genetic recombination (crossover) and mutation. Once the termination criteria are satisfied, the optimum is selected from the population. The algorithm was applied on 2D and 3D magnetic resonance images (MRI). It does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. To evaluate the performance of the GA registration, the results were compared with results of the Automatic Image Registration technique (AIR) and manual registration which was used as the gold standard. Results showed that our GA implementation was a robust algorithm and gives very close results to the gold standard. A pre-cropping strategy was also discussed as an efficient preprocessing step to enhance the registration accuracy.

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

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.

In this paper we present a pre-processing stage for an automated volumetric CT stroke image diagnosis system. It concerns the automatic intensity-based 3D image registration of non-contrast CT (NCCT) to CT angiography (CTA) images. The NCCT were acquired with a gantry tilt and hence they are first tilt corrected. After the tilt correction process, the NCCT images are then registered to the CTA image. The accuracy of image registration is measured by the overlap of the segmented skull from both images. The limitation of the current implementation is computing time which should be improved for a clinical application of the method.

Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively slow using the conventional ray-casting algorithm. Image-guided radiation therapy systems using DTS to verify target location require a large number DRRs to be precomputed since there is insufficient time within the automatic image registration procedure to generate DRRs and search for an optimal pose. DRRs were rendered from octree-compressed CT data. Previous work showed that octree-compressed volumes rendered by conventional ray casting deliver a registration with acceptable clinical accuracy, but efficiently rendering the irregular grid of an octree data structure is a challenge for conventional ray casting. We address this by using vertex and fragment shaders of modern graphics processing units (GPUs) to directly project internal spaces of the octree, represented by textured particle sprites, onto the view plane. The texture is procedurally generated and depends on the CT pose. The performance of this new algorithm was found to be 4 times faster than that of a ray-casting algorithm implemented using NVIDIA™Compute Unified Device Architecture (CUDA™) on an equivalent GPU (~95% octree compression). Rendering artifacts are apparent (consistent with other splatting algorithm), but image quality tends to improve with compression and fewer particles are needed. A peak signal-to-noise ratio analysis confirmed that the images rendered from compressed volumes were of marginally better quality to those rendered using Gaussian footprints. Using octree-encoded DRRs within a 2D/3D registration framework indicated the approach may be useful in accelerating automatic image registration.

The Transient Pressure Test Article (TPTA) test program is being conducted at a new test facility located in the east test area at the National Aeronautics and Space Administration's (NASA's) Marshall Space Flight Center (MSFC) in Huntsville, Alabama. This facility, along with the special test equipment (STE) required for facility support, was constructed specifically to test and verify the sealing capability of the redesigned solid rocket motor's (RSRM) field, igniter, and nozzle joints. The test article consists of full scale RSRM hardware loaded with inert propellant and assembled in a short stack configuration. The TPTA is pressurized by igniting a propellant cartridge capable of inducing a pressure rise rate that simulates the ignition transient that occurs during launch. Dynamic loads are applied during the pressure cycle to simulate external tank attach (ETA) strut loads present on the ETA ring. Sealing ability of the redesigned joints is evaluated under joint movement conditions produced by these combined loads since joint sealing ability depends on seal resilience velocity being greater than gap opening velocity. Also, maximum flight dynamic loads are applied to the test article which is either pressurized to 600 psia using gaseous nitrogen (GN2) or applied to the test article as the pressure decays inside the test article on the down cycle after the ignition transient cycle. This paper will present the uniqueness of this new test facility with respect to its capabilities. It will also touch on both the topic of test effectiveness versus space vehicle flight performance and new aerospace test techniques, as well as presenting a comparison between the old SRM design and the RSRM.

Innovative studies on morphing wing structures with the highest potential to improve aerodynamic performance on large aircraft are running under the CleanSky2 platform to validate the morphing architectures on true-scale demonstrator through ground and wind tunnel tests. Research activities have been conducted to develop a revolutionary multi-modal camber morphing flap to enhance the aerodynamic behaviour of a new generation of regional aircraft within this challenging framework. The design and validation of the intelligent architecture capable of different morphing modes required for low-speed (take-off/landing) and high-speed (cruise)conditions. To enhance the significance and applicability of the wind tunnel test campaign, a significant scale factor of 1:3 was selected for the test article. In this study, general layout of the mechanical model and FE analysis performed both inner and outer flap will be presented. A comprehensive structural analysis of the flap test article was conducted to ensure the safety and effectiveness of the conceived mechanical solutions. Linear static analyses were performed using the finite element (FE) method within the Ansys Workbench® environment. These analyses aimed to assess the adequacy of the mechanical solutions and validate the test article’s structural integrity.

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

Multi-temporal remote sensing image registration is the key step of change detection, and because of the remarkable difference and the probably unknown of sensor parameters, the automatic registration of different temporal remote sensing images is very difficult. Image registration based on Fourier-Mellin transform(FMT) is a global and phase correlation method, which is based on Fourier and Log-polar transform. This method finds the transformation parameters for registration of the images while working in the frequency-domain and it is resilient to noise, occlusions and so on. In this paper, an improved approach based on Fourier-Mellin algorithm is proposed for the registration. Spectrum aliasing and resampling interpolation will bring errors during Fourier-Mellin transform. To get a better registration result, we have improved it by adding window function and filtering to reduce spectrum aliasing and increase the robustness.