Precision Of Image Registration Research Articles

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.

Image Registration and Mosaicking Based on the Criterion of Four Collinear Points

In order to avoid the influence caused by mismatching, improve the stitching accuracy, image registration based on refined feature points has been proposed. First, Harris corner detection is utilized to extract the feature points. Then the collinear points set have been selected from all of the feature points. Select the number of collinear points which are greater than or equal to 4. The collinear points from two images have been estimated by Cross-Ratio criterion. If the value of Cross-Ratio meets a certain threshold, these collinear points will be retained. 4 groups of points which are extracted from collinear points set will be used to calculate equation solution of projection transformation. Finally, fusion method combining optimal seam with feathering has been used to stitch images. An increase in the number of mismatching will cause a decline of image registration precision. There is a fall of 14.7% accuracy when one group of mismatching appears. There is a fall of 36% accuracy when three group of mismatching appears. However, the accuracy of our method has almost been unaffected. The collinear points extracted by Cross-Ratio rule are a good way to avoid the interference of mismatching. The precision can be guaranteed in the experimental process.

Video stitching based on iterative hashing and dynamic seam-line with local context

Video stitching fuses multi-camera videos with differing centers of projection into a single panoramic video. Image registration and video fusion is the key of video stitching. In this paper, a robust and real-time video stitching method based on iterative hashing and best dynamic seam-line with local context model is proposed, which can eliminate ghosting and ensure real-time in video stitching. Firstly, the iterative hashing algorithm is proposed to improve the speed and precision of image registration. Image feature points are matched by constructing a multi-table, extracting candidates and refining candidates with iteration, which enhances the local sensitivity of hashing and speeds up the process of feature points matching. Secondly, the method of finding the best seam-line dynamically and blending with local context is proposed to improve the quality of video fusion. The proposed video fusion method is able to eliminate ghosting and illumination variation during video stitching. In addition, the speed of video stitching can be enhanced. Experimental results on several scenes show the efficiency and effectiveness of the proposed video stitching method.

Influence of polarization filtering on image registration precision in underwater conditions

Underwater images often suffer from poor visibility due to photon scattering. However, in some cases, optical polarization filtering techniques can decrease the contribution of the scattered light and improve the visual image quality. In this Letter, the influence of these techniques for underwater image registration is analyzed, particularly when backscattered light is the main perturbation induced by the submarine environment. This analysis is performed using the Cramer-Rao bound and relies on a standard image formation model, taking into account various kinds of noises.

SU-E-J-92: CERR: New Tools to Analyze Image Registration Precision.

To present new tools in CERR (The Computational Environment for Radiotherapy Research) to analyze image registration and other software updates/additions. CERR continues to be a key environment (cited more than 129 times to date) for numerous RT-research studies involving outcomes modeling, prototyping algorithms for segmentation, and registration, experiments with phantom dosimetry, IMRT research, etc. Image registration is one of the key technologies required in many research studies. CERR has been interfaced with popular image registration frameworks like Plastimatch and ITK. Once the images have been autoregistered, CERR provides tools to analyze the accuracy of registration using the following innovative approaches (1)Distance Discordance Histograms (DDH), described in detail in a separate paper and (2)'MirrorScope', explained as follows: for any view plane the 2-d image is broken up into a 2d grid of medium-sized squares. Each square contains a right-half, which is the reference image, and a left-half, which is the mirror flipped version of the overlay image. The user can increase or decrease the size of this grid to control the resolution of the analysis. Other updates to CERR include tools to extract image and dosimetric features programmatically and storage in a central database and tools to interface with Statistical analysis software like SPSS and Matlab Statistics toolbox. MirrorScope was compared on various examples, including 'perfect' registration examples and 'artificially translated' registrations. for 'perfect' registration, the patterns obtained within each circles are symmetric, and are easily, visually recognized as aligned. For registrations that are off, the patterns obtained in the circles located in the regions of imperfections show unsymmetrical patterns that are easily recognized. The new updates to CERR further increase its utility for RT-research. Mirrorscope is a visually intuitive method of monitoring the accuracy of image registration that improves on the visual confusion of standard methods.

Automatic Prospective Registration of High-Resolution Trabecular Bone Images of the Tibia

Magnetic Resonance Imaging (MRI) longitudinal studies conducted to assess changes in tibia bone quality impose strict requirements on the reproducibility of the prescribed region acquired. Registration, the process of aligning two images, is commonly performed on the images after acquisition. However, techniques to improve image registration precision by adjusting scanning parameters prospectively, prior to image acquisition, would be preferred. We have adapted an automatic prospective mutual information based registration algorithm to a MRI longitudinal study of trabecular bone of the tibia and compared it to a post-scan manual registration. Qualitatively, image alignment due to the prospective registration is shown in 2D subtraction images and 3D surface renderings. Quantitatively, the registration performance is demonstrated by calculating the sum of the squares of the subtraction images. Results show that the sum of the squares is lower for the follow up images with prospective registration by an average of 19.37% +/- 0.07 compared to follow up images with post-scan manual registration. Our study found no significant difference between the trabecular bone structure parameters calculated from the post-scan manual registration and the prospective registration images (p > 0.05). All coefficient of variation values for all trabecular bone structure parameters were within a 2-4.5% range which are within values previously reported in the literature. Results suggest that this algorithm is robust enough to be used in different musculoskeletal imaging applications including the hip as well as the tibia.

Analysis of image registration noise due to rotationally dependent aliasing

This paper investigates factors that degrade the precision of image registration based on phase correlation. The major sources of error are interpolation error and rotationally dependent aliasing. The latter error stems from the fact that the discrete-Fourier transform does not commute with the rotation of sampled-images, whereas in the continuous domain the corresponding operations do commute. We show through a series of examples how much the various sources of error contribute to phase-correlation registration, and we demonstrate constructive techniques for improving precision and signal to noise ratio in the registration process. Since rotationally dependent aliasing is exacerbated by the presence of high frequencies, the examples demonstrate that the use of a Blackman window removes spurious high frequencies in the spectral leakage created by the image boundary and greatly reduces aliasing effects. Since remaining aliasing effects are strongest in the low frequencies of the Fourier transform, their effects can be reduced to a negligible amount by removing frequencies within a radius of N/4 of the Fourier domain origin. A third technique is to perform phase correlation over half the Fourier plane rather than over the full plane, which more than doubles the signal-to-noise ratio of phase correlation. For an example image, the combination of techniques improved the phase-correlation signal-to-noise ratio from 8.5 to 172 and raised the peak from 0.348 to 0.885, which are substantially higher values than previously reported.

Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates

Estimates of forest area were obtained for the states of Indiana, Iowa, Minnesota, and Missouri in the United States using stratified analyses and observations from forest inventory plots measured in federal fiscal year 1999. Strata were created by aggregating the land cover classes of the National Land Cover Data (NLCD), and strata weights were calculated as proportions of strata pixel counts. The analyses focused on improving the precision of unbiased forest area estimates and included evaluation of the correspondence between forest/nonforest aggregations of the NLCD classes and observed attributes of forest inventory plots, evaluation of the utility of the NLCD as a stratification tool, and estimation of the effects on precision of image registration and plot location errors. The results indicate that the combination of NLCD-based stratification of inventory plots and stratified analyses increases the precision of forest area estimates and that the estimates are only slightly adversely affected by image registration and plot location errors.