Denis P. Shamonin, Esther E. Bron, Boudewijn P. Lelieveldt, Marion Smits, Stefan Klein, Marius Staring
Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e. for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: […]
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Zhou Haifang, Xu Rulin, Jiang Jingfei
Image registration is a crucial step of many remote sensing related applications. As the scale of data and complexity of algorithm keep growing, image registration faces great challenges of its processing speed. In recent years, the computing capacity of GPU improves greatly. Taking the benefits of using GPU to solve general propose problem, we research […]
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Ying-Chih Lin, Chien-Liang Huang, Chin-Sheng Chen, Wen-Chung Chang, Yu-Jen Chen, Chia-Yuan Liu
Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance, it needs more complex image processing, and it will spend expensive computation cost. For the real time issue, this paper […]
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Jian Cao, Jie Liang, Xiao-fang Xie, Xun-qiang Hu
Wide baseline matching is the state of the art for object recognition and image registration problems in computer vision. Robust feature descriptors can give vast improvements in the quality and speed of subsequent steps, but intensive computation is still required. With the release of general purpose parallel computing interfaces, opportunities for increases in performance arise. […]
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Lin Shi, Wen Liu, Heye Zhang, Yongming Xie, Defeng Wang
Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and […]
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Xuejun Gu, Bin Dong, Jing Wang, John Yordy, Loren Mell, Xun Jia, Steve B. Jiang
In adaptive radiotherapy, a deformable image registration is often conducted between the planning CT and the 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. Clinician’s inspection and editing […]
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Vincent Stanley Dayes
Concern about the threats posed by natural proliferation of animal-borne human diseases like BSE ("mad cow disease") and by the possible use of animals as disease vectors in bioterrorism, have spurred heightened interest in the development of methods for rapid automated identification of individual animals of various societally and commercially important mammalian species. Just as […]
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Sachitsing Dwarkan
Medical image registration is a computational task involving the spatial realignment of multiple sets of images of the same or different modalities. A novel method of using the Open Computing Language (OpenCL) framework to accelerate affine image registration across multiple processing architectures is presented. The use of this method on graphics processors results in a […]
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Xin Zhen, Xuejun Gu, Hao Yan, Linghong Zhou, Xun Jia, Steve B. Jiang
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 […]
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Joep Schreurs
Next generation DNA sequencing technologies generate terabytes of image data in a typical run over several days. Compute power to process the increasing amount of image data is becoming a problem in next generation sequencing. We propose to use the compute power of Graphical Processing Units (GPUs) to address this problem. GPUs have an efficient […]
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Linh Ha, Marcel Prastawa, Guido Gerig, John H. Gilmore, Claudio T. Silva, Sarang Joshi1
Deformable image registration in the presence of considerable contrast differences and large size and shape changes presents significant research challenges. First, it requires a robust registration framework that does not depend on intensity measurements and can handle large nonlinear shape variations. Second, it involves the expensive computation of nonlinear deformations with high degrees of freedom. […]
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Paul Ross
The alignment or registration of two images or volumetric datasets is frequently a requirement in modern image-processing applications, particularly within the context of medical imaging. Modern graphics-processing units (GPUs) are designed to perform simple 3D graphics-pipeline tasks on a massively parallel scale; this processing power can be harnessed for general computation via libraries such as […]
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