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Deformable registration of digital images
Deformable Registration of Digital Images

Guan Weiguang,Xie Lin,Ma Songde,
Guan Weiguang
,Xie Lin,Ma Songde

计算机科学技术学报 , 1998,
Abstract: This paper proposes a novel elastic model and presents a deformable registration method based on the model. The method registers images without the need to extract features from the images, and therefore works directly on grey-level images. A new similarity metric is given on which the formation of external forces is based. The registration method, taking the coarse-to-fine strategy constructs external forces in larger scales for the first few iterations to rely more on global evidence, and then in smaller scales for later iterations to allow local relinements. The stiffness of the elastic body decreases as the process proceeds.To make it widely applicable, the method is not restricted to any trpe of transformation. The variations between images are thought as general free-form deformations.Because the elastic model designed is linearized, it can be solved very efficiently with high accuracy.The method has been successfully tested on MRI images. It will certainly find other uses such as matching time-varying sequences of pictures for moion analysis,fitting templates into images for non-rigid object recognition, maching stereo images for shape recovery etc.
Efficient Variational Approaches for Deformable Registration of Images
Mehmet Ali Akinlar,Muhammet Kurulay,Aydin Secer,Mustafa Bayram
Abstract and Applied Analysis , 2012, DOI: 10.1155/2012/704567
Abstract: Dirichlet, anisotropic, and Huber regularization terms are presented for efficient registration of deformable images. Image registration, an ill-posed optimization problem, is solved using a gradient-descent-based method and some fundamental theorems in calculus of variations. Euler-Lagrange equations with homogeneous Neumann boundary conditions are obtained. These equations are discretized by multigrid and finite difference numerical techniques. The method is applied to the registration of brain MR images of size 65×65. Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.
RANCOR: Non-Linear Image Registration with Total Variation Regularization  [PDF]
Martin Rajchl,John S. H. Baxter,Wu Qiu,Ali R. Khan,Aaron Fenster,Terry M. Peters,Jing Yuan
Computer Science , 2014,
Abstract: Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms. These regularization mechanisms are designed to mitigate the effects of trivial solutions to ill-posed registration problems and to otherwise ensure the resulting deformation fields are well-behaved. This paper introduces a novel deformable registration algorithm, RANCOR, which uses iterative convexification to address deformable registration problems under total-variation regularization. Initial comparative results against four state-of-the-art registration algorithms are presented using the Internet Brain Segmentation Repository (IBSR) database.
A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration  [PDF]
Wei-Yen Hsu
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0066656
Abstract: Background Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects. Methods and Findings An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic. Conclusions We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.
CT to Cone-beam CT Deformable Registration With Simultaneous Intensity Correction  [PDF]
Xin Zhen,Xuejun Gu,Hao Yan,Linghong Zhou,Xun Jia,Steve B. Jiang
Physics , 2012,
Abstract: Computed tomography (CT) to cone-beam computed tomography (CBCT) deformable image registration (DIR) is a crucial step in adaptive radiation therapy. Current intensity-based registration algorithms, such as demons, may fail in the context of CT-CBCT DIR because of inconsistent intensities between the two modalities. In this paper, we propose a variant of demons, called Deformation with Intensity Simultaneously Corrected (DISC), to deal with CT-CBCT DIR. DISC distinguishes itself from the original demons algorithm by performing an adaptive intensity correction step on the CBCT image at every iteration step of the demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the voxel intensities inside a patch around the voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities. DISC is implemented on computer graphics processing units (GPUs) in compute unified device architecture (CUDA) programming environment. The performance of DISC is evaluated on a simulated patient case and six clinical head-and-neck cancer patient data. It is found that DISC is robust against the CBCT artifacts and intensity inconsistency and significantly improves the registration accuracy when compared with the original demons.
A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy  [PDF]
Xuejun Gu,Bin Dong,Jing Wang,John Yordy,Loren Mell,Xun Jia,Steve B. Jiang
Physics , 2013, DOI: 10.1088/0031-9155/58/6/1889
Abstract: In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto propagated contours on the treatment CT may contain relatively large errors, especially in low contrast regions. A clinician inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The purpose of this work is to develop a contour-guided deformable image registration (CG-DIR) algorithm to improve the accuracy and consistency of the DVF for adaptive radiotherapy. Incorporation of the edited contours into the registration algorithm is realized by regularizing the objective function of the original demons algorithm with a term of intensity matching between the delineated structures set pairs. The CG-DIR algorithm is implemented on computer graphics processing units (GPUs) by following the original GPU-based demons algorithm computation framework [Gu et al, Phys Med Biol. 55(1): 207-219, 2010]. The performance of CG-DIR is evaluated on five clinical head-and-neck and one pelvic cancer patient data. It is found that compared with the original demons, CG-DIR improves the accuracy and consistency of the DVF, while retaining similar high computational efficiency.
Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture  [PDF]
Dat Tien Ngo,Sanghuyk Park,Anne Jorstad,Alberto Crivellaro,Chang Yoo,Pascal Fua
Computer Science , 2015,
Abstract: Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces.
Implementation and evaluation of various demons deformable image registration algorithms on GPU  [PDF]
Xuejun Gu,Hubert Pan,Yun Liang,Richard Castillo,Deshan Yang,Dongju Choi,Edward Castillo,Amitava Majumdar,Thomas Guerrero,Steve B. Jiang
Physics , 2009, DOI: 10.1088/0031-9155/55/1/012
Abstract: Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A grey-scale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4DCT images with an average size of 256x256x100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation.
Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models  [PDF]
Jianhua Xuan,Yue Wang,Matthew T. Freedman,Tulay Adali,Peter Shields
International Journal of Biomedical Imaging , 2006, DOI: 10.1155/ijbi/2006/73430
Abstract: Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery.
Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration  [PDF]
Lotta M. Ellingsen,Jerry L. Prince
International Journal of Biomedical Imaging , 2009, DOI: 10.1155/2009/281615
Abstract: Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.
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