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Journal articles on the topic 'Image registration'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Alam, Fakhre, Sami UR Rahman, Nasser Tairan, Habib Shah, Mohammed Saeed Abohashrh, and Sohail Abbas. "An Automatic Medical Image Registration Approach Based on Common Sub-regions of Interest." Journal of Medical Imaging and Health Informatics 9, no. 2 (February 1, 2019): 251–60. http://dx.doi.org/10.1166/jmihi.2019.2601.

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Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in two images (target image and source image) are automatically detected and locally registered. The final global registration is performed, using the transformation parameters obtained from the local registration. Registration using interested common sub-regions is always required in image guided surgery (IGS) and other medical procedures because it considers only the desired objects in medical images instead of the whole image contents. The proposed interested common sub-regions based registration is compared with the two states-of-the-art methods on MR images of human brain. In the experiments of rigid and deformable registrations, we show that our approach outperforms in terms of both the accuracy and time efficiency. The results reveal that interested common sub-region based registration can achieve good performance, regarding both the accuracy as well as the the time efficiency in monomodal brain image registration. In addition, the proposed approach also indicates the potential for multimodal images of different human organs.

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Govindarajulu, S. "Image Registration on Satellite Images." IOSR Journal of Electronics and Communication Engineering 3, no. 5 (2012): 10–17. http://dx.doi.org/10.9790/2834-0351017.

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Liang, Bo, Xi Chen, Lan Yu, Song Feng, Yangfan Guo, Wenda Cao, Wei Dai, Yunfei Yang, and Ding Yuan. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (July 20, 2022): 10. http://dx.doi.org/10.3847/1538-4365/ac7232.

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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.

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Himthani, Naveen, Malte Brunn, Jae-Youn Kim, Miriam Schulte, Andreas Mang, and George Biros. "CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications." Journal of Imaging 8, no. 9 (September 16, 2022): 251. http://dx.doi.org/10.3390/jimaging8090251.

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We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.

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Pluim, J. P. W., and J. M. Fitzpatrick. "Image registration." IEEE Transactions on Medical Imaging 22, no. 11 (November 2003): 1341–43. http://dx.doi.org/10.1109/tmi.2003.819272.

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Xu, Hong Kui, Ming Yan Jiang, and Ming Qiang Yang. "An Image Registration Method Combing Feature Constraint with Multilevel Strategy." Applied Mechanics and Materials 58-60 (June 2011): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.286.

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A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.

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Bingjian, Wang, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, and Lai Rui. "Image registration method for multimodal images." Applied Optics 50, no. 13 (April 25, 2011): 1861. http://dx.doi.org/10.1364/ao.50.001861.

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8

Watanabe, Yoichi, and Eunyoung Han. "Image registration accuracy of GammaPlan: a phantom study." Journal of Neurosurgery 109, Supplement (December 2008): 21–24. http://dx.doi.org/10.3171/jns/2008/109/12/s5.

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Object The authors evaluated the accuracy of the automatic image coregistration function implemented in the Leksell GammaPlan treatment planning software (Version 4C with MultiView Extension and Version 8.0). Methods The authors used a phantom with 9 landmarks (tips of thin cylindrical acrylic rods) evenly distributed in the treatment space. Two sets of images of the phantom were taken with both CT and MR imaging systems. The first image was obtained with the phantom aligned with the scanner's axis and the second scan was made by intentionally shifting and rotating the phantom relative to the scanner's axis. The authors attempted image registration of 2 CT image sets, CT and MR image sets, and 2 MR image sets. The accuracy of image registration was evaluated by measuring the x, y, and z coordinate values of the landmarks on each image set after 2 image sets were coregistered. The authors calculated the differences of the x, y, and z values and the distance, d, between corresponding landmarks in 2 image sets. To minimize interobserver dependence of coordinate measurements, 2 physicists did measurements independently. Results The distances, d, averaged over the 9 landmarks, were 2.63 ± 1.64 and 0.95 ± 0.25 mm for CT–CT and MR–MR image registrations, respectively. When the CT images of the air-filled phantom and MR images were coregistered, however, the algorithm performed poorly: d = 13.8 ± 1.23 mm. To remedy this, the authors undertook a 2-step process by first performing landmark-based registration of the 2 image sets and subsequently applying the automatic registration. With this approach, the mean distance drastically improved: d = 0.74 ± 0.31 mm. When the water-filled phantom was used for CT scans, the registration accuracy of CT and MR image sets was acceptable without the 2-step registration process: d = 1.18 ± 0.36 mm. Conclusions The accuracy of automatic registration of image sets from the same modality was within the voxel size of the scanned images. The accuracy of CT–MR image registration strongly depended on whether the phantom for CT scans was filled with air or water. This indicates the significant effect of the amount of common data available for a mutual information-based algorithm on the accuracy.

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Watcharawipha, Anirut, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields." Symmetry 11, no. 10 (September 28, 2019): 1210. http://dx.doi.org/10.3390/sym11101210.

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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.

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A. Yorke, Afua, Gary C. McDonald, David Solis, and Thomas Guerrero. "Quality Assurance of Image Registration Using Combinatorial Rigid Registration Optimization (CORRO)." Cancer Research and Cellular Therapeutics 5, no. 3 (July 26, 2021): 01–09. http://dx.doi.org/10.31579/2640-1053/076.

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Purpose: Expert selected landmark points on clinical image pairs to provide a basis for rigid registration validation. Using combinatorial rigid registration optimization (CORRO) provide a statistically characterized reference data set for image registration of the pelvis by estimating optimal registration. Materials ad Methods: Landmarks for each CT/CBCT image pair for 58 cases were identified. From the landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, forming k-set for k=4, 8, and 12. A rigid registration between the image pairs was computed for each k-combination set (2,000-8,000,000). The mean and standard deviation of the registration were used as final registration for each image pair. Joint entropy was used to validate the output results. Results: An average of 154 (range: 91-212) landmark pairs were selected for each CT/CBCT image pair. The mean standard deviation of the registration output decreased as the k-size increased for all cases. In general, the joint entropy evaluated was found to be lower than results from commercially available software. Of all 58 cases 58.3% of the k=4, 15% of k=8 and 18.3% of k=12 resulted in the better registration using CORRO as compared to 8.3% from a commercial registration software. The minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the CORRO algorithm. Conclusion: The results demonstrate that CORRO works even in the extreme case of the pelvic anatomy where the CBCT suffers from reduced quality due to increased noise levels. The estimated optimal registration using CORRO was found to be better than commercially available software for all k-sets tested. Additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.

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Soh, Youngsung, Mudasar Qadir, Aamer Mehmood, Yongsuk Hae, Hadi Ashraf, and Intaek Kim. "A Featured Area-Based Image Registration." International Journal of Computer Theory and Engineering 6, no. 5 (October 2014): 407–11. http://dx.doi.org/10.7763/ijcte.2014.v6.899.

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12

Tang, Jun. "Image Registration Using Clustering Algorithm." Advanced Materials Research 108-111 (May 2010): 63–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.63.

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This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.

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Ademaj, Adela, Lavdie Rada, Mazlinda Ibrahim, and Ke Chen. "A variational joint segmentation and registration framework for multimodal images." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262096669. http://dx.doi.org/10.1177/1748302620966691.

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Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.

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Nobnop, Wannapha, Imjai Chitapanarux, Somsak Wanwilairat, Ekkasit Tharavichitkul, Vicharn Lorvidhaya, and Patumrat Sripan. "Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882118. http://dx.doi.org/10.1177/1533033818821186.

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Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. Results: The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. Conclusions: The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.

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Sivaramakrishna, Radhika. "3D Breast Image Registration — A Review." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 39–48. http://dx.doi.org/10.1177/153303460500400106.

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Image registration is an important problem in breast imaging. It is used in a wide variety of applications that include better visualization of lesions on pre- and post-contrast breast MRI images, speckle tracking and image compounding in breast ultrasound images, alignment of positron emission, and standard mammography images on hybrid machines et cetera. It is a prerequisite to align images taken at different times to isolate small interval lesions. Image registration also has useful applications in monitoring cancer therapy. The field of breast image registration has gained considerable interest in recent years. While the primary focus of interest continues to be the registration of pre- and post-contrast breast MRI images, other areas like breast ultrasound registration have gained more attention in recent years. The focus of registration algorithms has also shifted from control point based semiautomated techniques, to more sophisticated voxel based automated techniques that use mutual information as a similarity measure. This paper visits the problem of breast image registration and provides an overview of the current state-of-the-art in this area.

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Zhang, Xuming, Yao Zhou, Peng Qiao, Xiaoning Lv, Jimin Li, Tianyu Du, and Yiming Cai. "Image Registration Algorithm for Remote Sensing Images Based on Pixel Location Information." Remote Sensing 15, no. 2 (January 11, 2023): 436. http://dx.doi.org/10.3390/rs15020436.

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Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images.

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Christensen, G. E., and H. J. Johnson. "Consistent image registration." IEEE Transactions on Medical Imaging 20, no. 7 (July 2001): 568–82. http://dx.doi.org/10.1109/42.932742.

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Bondzulic, Boban. "Image registration introduction." Vojnotehnicki glasnik 57, no. 3 (2009): 88–110. http://dx.doi.org/10.5937/vojtehg0903088b.

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Pernuš, Franjo, H. Siegfried Srtiehl, and Max A. Viergever. "Biomedical Image Registration." Image and Vision Computing 19, no. 1-2 (January 2001): 1–2. http://dx.doi.org/10.1016/s0262-8856(00)00071-8.

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Hill, Derek L. G., Philipp G. Batchelor, Mark Holden, and David J. Hawkes. "Medical image registration." Physics in Medicine and Biology 46, no. 3 (February 15, 2001): R1—R45. http://dx.doi.org/10.1088/0031-9155/46/3/201.

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Chen, Chin-Tu. "Radiologic Image Registration." Academic Radiology 10, no. 3 (March 2003): 239–41. http://dx.doi.org/10.1016/s1076-6332(03)80096-x.

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22

Weber, D. A., and M. Ivanovic. "Correlative image registration." Seminars in Nuclear Medicine 24, no. 4 (October 1994): 311–23. http://dx.doi.org/10.1016/s0001-2998(05)80021-2.

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Rogelj, Peter, and Stanislav Kovačič. "Symmetric image registration." Medical Image Analysis 10, no. 3 (June 2006): 484–93. http://dx.doi.org/10.1016/j.media.2005.03.003.

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Kneöaurek, Karin, Marija Ivanovic, Josef Machac, and David A. Weber. "Medical image registration." Europhysics News 31, no. 4 (July 2000): 5–8. http://dx.doi.org/10.1051/epn:2000401.

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McCool, D., K. L. Adamson, J. R. Buscombe, and A. J. W. Hilson. "14. Image registration." Nuclear Medicine Communications 18, no. 4 (April 1997): 326. http://dx.doi.org/10.1097/00006231-199704000-00128.

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Chen, Bowei, Li Chen, Umara Khalid, and Shuai Zhang. "IFSrNet: Multi-Scale IFS Feature-Guided Registration Network Using Multispectral Image-to-Image Translation." Electronics 13, no. 12 (June 7, 2024): 2240. http://dx.doi.org/10.3390/electronics13122240.

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Multispectral image registration is the process of aligning the spatial regions of two images with different distributions. One of the main challenges it faces is to resolve the severe inconsistencies between the reference and target images. This paper presents a novel multispectral image registration network, Multi-scale Intuitionistic Fuzzy Set Feature-guided Registration Network (IFSrNet), to address multispectral image registration. IFSrNet generates pseudo-infrared images from visible images using Cycle Generative Adversarial Network (CycleGAN), which is equipped with a multi-head attention module. An end-to-end registration network encodes the input multispectral images with intuitionistic fuzzification, which employs an improved feature descriptor—Intuitionistic Fuzzy Set–Scale-Invariant Feature Transform (IFS-SIFT)—to guide its operation. The results of the image registration will be presented in a direct output. For this task we have also designed specialised loss functions. The results of the experiment demonstrate that IFSrNet outperforms existing registration methods in the Visible–IR dataset. IFSrNet has the potential to be employed as a novel image-to-image translation paradigm.

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Jiao, Jichao, Wenyi Li, Zhongliang Deng, and Qasim Ali Arain. "A structural similarity-inspired performance assessment model for multisensor image registration algorithms." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141771705. http://dx.doi.org/10.1177/1729881417717059.

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In order to assess the performance of multisensor image registration algorithms that are used in the multirobot information fusion, we propose a model based on structural similarity whose name is vision registration assessment model. First of all, this article introduces a new image concept named superimposed image for testing subjective and objective assessment methods. Therefore, we assess the superimposed image but not the registered image, which is different from previous image registration assessment methods that usually use reference and sensed images. Then, we calculate eight assessment indicators from different aspects for superimposed images. After that, vision registration assessment model fuses the eight indicators using canonical correlation analysis, which is used for evaluating the quality of an image registration results in different aspects. Finally, three kinds of images which include optical images, infrared images, and SAR images are used to test vision registration assessment model. After evaluating three state-of-the-art image registration methods, experiments indict that the proposed structural similarity-motivated model achieved almost same evaluation results with that of the human object with the consistency rate of 98.3%, which shows that vision registration assessment model is efficient and robust for evaluating multisensor image registration algorithms. Moreover, vision registration assessment model is independent of the emotional factors and outside environment, which is different from the human.

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Pan, Meisen, Jianjun Jiang, Fen Zhang, and Qiusheng Rong. "MEDICAL IMAGE REGISTRATION BASED ON IMPROVED FUZZY C-MEANS CLUSTERING." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (August 2015): 1550032. http://dx.doi.org/10.4015/s1016237215500325.

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The mutual information (MI) technology and the iterative closest point (ICP) algorithm, as intensity-based and feature-based image registration methods respectively, are commonly put into use in medical image registration. But some naturally existing things which restrict the further development need to be faced and be solved. On one hand, they remain heavy calculation costs and low registration efficiencies. On the other hand, since they seriously depend on whether the initial rotation and translation registration parameters can be exactly selected, they often trap in the local optima and even fail to register images. In this paper, we compute the centroids of the reference and floating images by using the image moments to obtain the initial translation values, and use improved fuzzy C-means clustering (IFCM) to classify the image coordinates. Before clustering, this proposed method first centralizes the medical image coordinates, creates the two-row coordinate matrix to construct the two-dimensional (2D) sample set partitioned into two classes, and computes the slope of a straight line fitted to the two classes, finally derives the rotation angle from solving the arc tangent of the slope and obtains the initial rotation values. The experimental results show that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. Also, it can efficiently avoid trapping in the local optima and meets both mono-modality and multi-modality image registrations.

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Zhu, Ning, Mohammad Najafi, Bin Han, Steven Hancock, and Dimitre Hristov. "Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882196. http://dx.doi.org/10.1177/1533033818821964.

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Purpose: Registration of 3-dimensional ultrasound images poses a challenge for ultrasound-guided radiation therapy of the prostate since ultrasound image content changes significantly with anatomic motion and ultrasound probe position. The purpose of this work is to investigate the feasibility of using a pretrained deep convolutional neural network for similarity measurement in image registration of 3-dimensional transperineal ultrasound prostate images. Methods: We propose convolutional neural network-based registration that maximizes a similarity score between 2 identical in size 3-dimensional regions of interest: one encompassing the prostate within a simulation (reference) 3-dimensional ultrasound image and another that sweeps different spatial locations around the expected prostate position within a pretreatment 3-dimensional ultrasound image. The similarity score is calculated by (1) extracting pairs of corresponding 2-dimensional slices (patches) from the regions of interest, (2) providing these pairs as an input to a pretrained convolutional neural network which assigns a similarity score to each pair, and (3) calculating an overall similarity by summing all pairwise scores. The convolutional neural network method was evaluated against ground truth registrations determined by matching implanted fiducial markers visualized in a pretreatment orthogonal pair of x-ray images. The convolutional neural network method was further compared to manual registration and a standard commonly used intensity-based automatic registration approach based on advanced normalized correlation. Results: For 83 image pairs from 5 patients, convolutional neural network registration errors were smaller than 5 mm in 81% of the cases. In comparison, manual registration errors were smaller than 5 mm in 61% of the cases and advanced normalized correlation registration errors were smaller than 5 mm only in 25% of the cases. Conclusion: Convolutional neural network evaluation against manual registration and an advanced normalized correlation -based registration demonstrated better accuracy and reliability of the convolutional neural network. This suggests that with training on a large data set of transperineal ultrasound prostate images, the convolutional neural network method has potential for robust ultrasound-to-ultrasound registration.

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Rodgers, John, Rosie Hales, Lee Whiteside, Jacqui Parker, Louise McHugh, Anthea Cree, Marcel van Herk, et al. "Comparison of radiographer interobserver image registration variability using cone beam CT and MR for cervix radiotherapy." British Journal of Radiology 93, no. 1112 (August 2020): 20200169. http://dx.doi.org/10.1259/bjr.20200169.

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Objectives: The aim of this study was to assess the consistency of therapy radiographers performing image registration using cone beam computed tomography (CBCT)-CT, magnetic resonance (MR)-CT, and MR-MR image guidance for cervix cancer radiotherapy and to assess that MR-based image guidance is not inferior to CBCT standard practice. Methods: 10 patients receiving cervix radiation therapy underwent daily CBCT guidance and magnetic resonance (MR) imaging weekly during treatment. Offline registration of each MR image, and corresponding CBCT, to planning CT was performed by five radiographers. MR images were also registered to the earliest MR interobserver variation was assessed using modified Bland–Altman analysis with clinically acceptable 95% limits of agreement (LoA) defined as ±5.0 mm. Results: 30 CBCT-CT, 30 MR-CT and 20 MR–MR registrations were performed by each observer. Registration variations between CBCT-CT and MR-CT were minor and both strategies resulted in 95% LoA over the clinical threshold in the anteroposterior direction (CBCT-CT ±5.8 mm, MR-CT ±5.4 mm). MR–MR registrations achieved a significantly improved 95% LoA in the anteroposterior direction (±4.3 mm). All strategies demonstrated similar results in lateral and longitudinal directions. Conclusion: The magnitude of interobserver variations between CBCT-CT and MR-CT were similar, confirming that MR-CT radiotherapy workflows are comparable to CBCT-CT image-guided radiotherapy. Our results suggest MR–MR radiotherapy workflows may be a superior registration strategy. Advances in knowledge: This is the first publication quantifying interobserver registration of multimodality image registration strategies for cervix radical radiotherapy patients.

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Yang, Lu Jing, Wei Hao, and Chong Lun Li. "A Modified Phase Correlation Method for Image Registration." Applied Mechanics and Materials 48-49 (February 2011): 48–51. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.48.

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Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.

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Huang, Min, Guanyu Ren, Shizheng Zhang, Qian Zheng, and Huiyang Niu. "An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration." Computational and Mathematical Methods in Medicine 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/9246378.

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In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. This network was an unsupervised end-to-end convolutional neural network. After training, the dense displacement vector field can be predicted almost in real-time for the unseen input image pairs, which saves a lot of time compared with the traditional algorithms of independent iterative optimization for each pair of images. On the basis of the encoder-decoder structure, this network introduced the improved Inception module for multiscale feature extraction and expanding the receptive field and the hierarchical forecast structure to promote the update of the parameters of the middle layers, which achieved the best performance on the augmented public dataset compared with the existing four excellent registration methods.

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Zhou, Wu, and Yaoqin Xie. "Interactive Multigrid Refinement for Deformable Image Registration." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/532936.

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Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable registration methods. This paper focuses on how to revise registration results interactively for deformable image registration. We can manually revise the transformed image locally in a hierarchical multigrid manner to make the transformed image register well with the reference image. The proposed method is based on multilevel B-spline to interactively revise the deformable transformation in the overlapping region between the reference image and the transformed image. The resulting deformation controls the shape of the transformed image and produces a nice registration or improves the registration results of other registration methods. Experimental results in clinical medical images for adaptive radiotherapy demonstrated the effectiveness of the proposed method.

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Patel, Laukikkumar K., and Manish I. Patel. "Feature Based Image Registration Using ORB and CNN for Remote Sensing Images." Indian Journal Of Science And Technology 16, no. 42 (November 13, 2023): 3803–13. http://dx.doi.org/10.17485/ijst/v16i42.1782.

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WANG Yun, 王运, and 颜昌翔 YAN Chang-xiang. "Sub-pixel image registration of spectrometer images." Optics and Precision Engineering 20, no. 3 (2012): 661–67. http://dx.doi.org/10.3788/ope.20122003.0661.

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Kim, Kee-Baek, Jong-Su Kim, Sangkeun Lee, and Jong-Soo Choi. "Fast Image Registration Using Pyramid Edge Images." International Journal of Intelligent Engineering and Systems 2, no. 1 (March 31, 2009): 1–8. http://dx.doi.org/10.22266/ijies2009.0331.01.

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Lee, Won-Hee, Su-Hong Yu, and Joon Heo. "Image Registration of Cloudy Pushbroom Scanner Images." Korean Journal of Remote Sensing 27, no. 1 (February 28, 2011): 9–15. http://dx.doi.org/10.7780/kjrs.2011.27.1.009.

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Mezura-Montes, Efrén, Héctor-Gabriel Acosta-Mesa, Darío-del-Sinaí Ramírez-Garcés, Nicandro Cruz-Ramírez, and Rodolfo Hernández-Jiménez. "An Image Registration Method for Colposcopic Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/285962.

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A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

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Gul, Sabeen, Sheeraz Memon, and Bushra Naz. "Image Registration Model For Remote Sensing Images." EAI Endorsed Transactions on Internet of Things 4, no. 16 (October 31, 2018): 159333. http://dx.doi.org/10.4108/eai.21-12-2018.159333.

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WANG, XIUYING, and DAVID DAGAN FENG. "AUTOMATIC ELASTIC MEDICAL IMAGE REGISTRATION BASED ON IMAGE INTENSITY." International Journal of Image and Graphics 05, no. 02 (April 2005): 351–69. http://dx.doi.org/10.1142/s0219467805001793.

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An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.

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Wei, Chun Rong, Chu He, and Hong Sun. "SAR Image Registration Using Ratio Mutual Information." Applied Mechanics and Materials 241-244 (December 2012): 2630–37. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2630.

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In order to reduce the noise sensitivity of the SAR (synthetic aperture radar) image registration, a image registration algorithm which basing on the ratio mutual information (RatioMI) is proposed in this paper. Firstly, the ratio images of the reference image and the floating image are gotten by using the ratio operator, and then take the two ratio images as a similar characteristic quantity to construct the similarity measure function which was used in the optimization process of the image registration experiment. The experimental results of the SAR image registration show that the new registration algorithm which based on the RatioMI is effectively in avoiding the local maxima point problems causing by speckle noise.

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Yang, Aolin, Tiejun Yang, Xiang Zhao, Xin Zhang, Yanghui Yan, and Chunxia Jiao. "DTR-GAN: An Unsupervised Bidirectional Translation Generative Adversarial Network for MRI-CT Registration." Applied Sciences 14, no. 1 (December 21, 2023): 95. http://dx.doi.org/10.3390/app14010095.

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Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named DTR-GAN. Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the complementary information of different modalities. Then, to enhance the quality of the transformed images in the translation network, we design a multiscale encoder–decoder network that effectively captures both local and global features in images. Finally, we propose a mixed similarity loss to encourage the warped image to be closer to the target image in deep features. We extensively evaluate methods for MRI-CT image registration tasks of the abdominal cavity with advanced unsupervised multimodal image registration approaches. The results indicate that DTR-GAN obtains a competitive performance compared to other methods in MRI-CT registration. Compared with DFR, DTR-GAN has not only obtained performance improvements of 2.35% and 2.08% in the dice similarity coefficient (DSC) of MRI-CT registration and CT-MRI registration on the Learn2Reg dataset but has also decreased the average symmetric surface distance (ASD) by 0.33 mm and 0.12 mm on the Learn2Reg dataset.

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Siu, A. M. K., and R. W. H. Lau. "Image registration for image-based rendering." IEEE Transactions on Image Processing 14, no. 2 (February 2005): 241–52. http://dx.doi.org/10.1109/tip.2004.840690.

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Tomaževič, Dejan, Boštjan Likar, and Franjo Pernuš. "MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION." Image Analysis & Stereology 31, no. 1 (March 15, 2012): 43. http://dx.doi.org/10.5566/ias.v31.p43-53.

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Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

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Yang, Huan, Pengjiang Qian, and Chao Fan. "An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis." Computational and Mathematical Methods in Medicine 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/2684851.

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Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.

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Rathore, Gurpreet, and Vijay Dhir. "A comparative approach to image registration methods." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 2 (October 23, 2013): 757–62. http://dx.doi.org/10.24297/ijmit.v6i2.3821.

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Image processing methods are possibly able to visualize objects inside the human body. Efficient image processing methods are useful in medical diagnosis, treatment planning and medical research. Medical images are used for medical diagnosis. These images should be geometrically aligned for better observation. Registration is necessary technique to integrate data taken from different measurements. Image Registration is a process of overlaying two or more images that can taken at different times, using different devices, different viewpoints and from different angles in order to have 2D or 3D perspectives. The purpose of this paper is to present comparative approach to various image registration methods. This will be a useful document for researchers to implement alternative image registration methods for specific purpose.

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Ruchti, Alexander, Alexander Neuwirth, Allison K. Lowman, Savannah R. Duenweg, Peter S. LaViolette, and John D. Bukowy. "Homologous point transformer for multi-modality prostate image registration." PeerJ Computer Science 8 (December 1, 2022): e1155. http://dx.doi.org/10.7717/peerj-cs.1155.

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Registration is the process of transforming images so they are aligned in the same coordinate space. In the medical field, image registration is often used to align multi-modal or multi-parametric images of the same organ. A uniquely challenging subset of medical image registration is cross-modality registration—the task of aligning images captured with different scanning methodologies. In this study, we present a transformer-based deep learning pipeline for performing cross-modality, radiology-pathology image registration for human prostate samples. While existing solutions for multi-modality prostate image registration focus on the prediction of transform parameters, our pipeline predicts a set of homologous points on the two image modalities. The homologous point registration pipeline achieves better average control point deviation than the current state-of-the-art automatic registration pipeline. It reaches this accuracy without requiring masked MR images which may enable this approach to achieve similar results in other organ systems and for partial tissue samples.

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YE, Peng, Zhiyong ZHAO, and Fang LIU. "Rectified Registration Consistency for Image Registration Evaluation." IEICE Transactions on Information and Systems E97.D, no. 9 (2014): 2549–51. http://dx.doi.org/10.1587/transinf.2013edl8313.

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Sudhakar, K., R. Priyanka, V. Mani Kumar, A. V. Ramani, N. Aparna, and M. Manju Sarma. "Automatic Image Registration Through-Principal Axes Registration." International Journal of Computer Applications 185, no. 30 (August 31, 2023): 30–35. http://dx.doi.org/10.5120/ijca2023923059.

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Adeleke, Adebayo. "Image Information Measures for Predicting Image Registration Performance on iThemba LABS Image Registration System." Journal of Scientific Research and Reports 12, no. 1 (January 10, 2016): 1–20. http://dx.doi.org/10.9734/jsrr/2016/28108.

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