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Search Results: 1 - 10 of 12291 matches for " Image Registration "
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Implementation of RANSAC Algorithm for Feature-Based Image Registration  [PDF]
Lan-Rong Dung, Chang-Min Huang, Yin-Yi Wu
Journal of Computer and Communications (JCC) , 2013, DOI: 10.4236/jcc.2013.16009
Abstract:

This paper describes the hardware implementation of the RANdom Sample Consensus (RANSAC) algorithm for featured-based image registration applications. The Multiple-Input Signature Register (MISR) and the index register are used to achieve the random sampling effect. The systolic array architecture is adopted to implement the forward elimination step in the Gaussian elimination. The computational complexity in the forward elimination is reduced by sharing the coefficient matrix. As a result, the area of the hardware cost is reduced by more than 50%. The proposed architecture is realized using Verilog and achieves real-time calculation on 30 fps 1024 * 1024 video stream on 100 MHz clock.

Medical Image Registration Using the Fourier Transform  [PDF]
Jason Luce, James Gray, Mark A. Hoggarth, Jeffery Lin, Elizabeth Loo, Maria I. Campana, John C. Roeske
International Journal of Medical Physics,Clinical Engineering and Radiation Oncology (IJMPCERO) , 2014, DOI: 10.4236/ijmpcero.2014.31008
Abstract:

A Fourier Transform (FT) based pattern-matching algorithm was adapted for use in medical image registration. This algorithm obtained the FT of two images, determined the normalized cross-power spectrum of the transformed images, and then applied an inverse FT. The result was a delta function with a maximum value at the location corresponding to the distance between the two images; a similar method was used to recover rotations. This algorithm was first tested using a simple two-dimensional image, with induced shifts of ±20 pixels and ±10 degrees. All translations were recovered with no error and all rotations were recovered within 0.18 degrees. Subsequently, this algorithm was tested on eight clinical kV images drawn from four different body sites. Twenty-five random shifts and rotations were applied to each image. The average mean error of the registration solution was -0.002 ± 0.077 mm in the x direction, 0.002 ± 0.075 mm in the y direction, and -0.012 ± 0.099 degrees. These initial results suggest that a FT algorithm has a high degree of accuracy when registering clinical kV images.

A Biomechanical Model of Human Lung Deformation Utilizing Patient-Specific Elastic Property  [PDF]
Behnaz Seyfi, Anand P. Santhanam, Olusegun J. Ilegbusi
Journal of Cancer Therapy (JCT) , 2016, DOI: 10.4236/jct.2016.76043
Abstract: A biomechanical model is developed and validated for breathing-induced deformation of human lung. Specifically, a subject-specific poro-elastic lung model is used to predict the displacement over the breathing cycle and compared with displacement derived from high resolution image registration. The lung geometry is derived from four-dimensional computed tomography (4DCT) scan dataset of two human subjects. The heterogeneous Young’s modulus is estimated using inverse analysis method. The numerical simulation uses fluid-structure interaction technique to solve the coupled airflow equations and structural dynamics of the lung tissue. The modelled displacement is validated by comparison with the 4DCT registration results.
An image analysis method for quantification of hepatic perfusion based on contrast-enhanced ultrasound imaging  [PDF]
Yi-xin Li, Fang Yang, Ning Gu
Journal of Biomedical Science and Engineering (JBiSE) , 2008, DOI: 10.4236/jbise.2008.12019
Abstract: Information about hepatic perfusion is used in clinical liver disease diagnosis. An image analy-sis system can help physicians make efficient and accurate diagnosis. The objective of this study is to propose an image analysis method for the quantification of the hepatic perfusion based on contrast-enhanced ultrasound imaging (CEUI). The proposed method contains frame selection, image registration, digital subtraction and grey-scale calculation. Then, by processing an image sequence, a time-intensity curve (TIC) for hepatic perfusion is derived. The kernel of this image analysis technology is digital subtrac-tion and its accuracy is improved by frame selec-tion and image registration. The advantage of this method is that it can obtain the perfusion information of the whole liver which is rarely ob-tained by traditional image analysis technology; therefore, it is a supplement of the traditional image analysis method. This method is applied on the quantification of a rabbit’s hepatic perfu-sion and the result shows the efficiency of it.
Regular Stereo Matching Improvement System Based on Kinect-supporting Mechanism  [PDF]
Din-Yuen Chan, Che-Han Hsu
Open Journal of Applied Sciences (OJAppS) , 2013, DOI: 10.4236/ojapps.2013.31B005
Abstract: In this paper, we built a stereoscopic video associated experimental model, which is referenced as Kinect-supporting improved stereo matching scheme. As the depth maps offered by the Kinect IR-projector are resolution-inadequate, noisy, distance-limited, unstable, and material-sensitive, the appropriated de-noising, stabilization and filtering are first performed for retrieving useful IR-projector depths. The disparities are linearly computed from the refined IR-projector depths to provide specifically referable disparity resources. By exploiting these resources with sufficiency, the proposed mechanism can lead to great enhancement on both speed and accuracy of stereo matching processing to offer better extra virtual view generation and the possibility of price-popularized IR-projector embedded stereoscopic camera.
A Hybrid Algorithm to Address Ambiguities in Deformable Image Registration for Radiation Therapy  [PDF]
Song Gao, Yongbin Zhang, Jinzhong Yang, Catherine H. Wang, Lifei Zhang, Laurence E. Court, Lei Dong
International Journal of Medical Physics,Clinical Engineering and Radiation Oncology (IJMPCERO) , 2012, DOI: 10.4236/ijmpcero.2012.12007
Abstract: We proposed the use of a hybrid deformable image registration approach that combines compact-support radial basis functions (CSRBF) spline registration with intensity-based image registration. The proposed method first uses the pre-viously developed image intensity-based method to achieve voxel-by-voxel correspondences over the entire image re-gion. Next, for those areas of inaccurate registration, a sparse set of landmark correspondences was defined for local deformable image registration using a multi-step CSRBF approach. This hybrid registration takes advantage of both intensity-based method for automatic processing of entire images and the CSRBF spline method for fine adjustment over specific regions. The goal of using this hybrid registration is to locally control the quality of registration results in specific regions of interest with minimal human intervention. The major applications of this approach in radiation ther-apy are for the corrections of registration failures caused by various imaging artifacts resulting in, low image contrast, and non-correspondence situations where an object may not be imaged in both target and source images. Both synthetic and real patient data have been used to evaluate this hybrid method. We used contours mapping to validate the accuracy of this method on real patient image. Our studies demonstrated that this hybrid method could improve overall registra-tion accuracy with moderate overhead. In addition, we have also shown that the multi-step CSRBF registration proved to be more effective in handling large deformations while maintaining the smoothness of the transformation than origi-nal CSRBF.
Accuracy Analysis on the Automatic Registration of Multi-Source Remote Sensing Images Based on the Software of ERDAS Imagine  [PDF]
Debao Yuan, Ximin Cui, Yahui Qiu, Xueyun Gu, Li Zhang
Advances in Remote Sensing (ARS) , 2013, DOI: 10.4236/ars.2013.22018
Abstract:

The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.

A new hybrid particle swarm optimization for multimodal brain image registration  [PDF]
Fatemeh Ayatollahi, Shahriar Baradaran Shokouhi, Ahmad Ayatollahi
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.54020
Abstract: Image registration is an important issue in medical analysis. In this process the spatial transformation that aligns the reference image and the floating image is estimated by optimizing a similarity metric. Mutual information (MI), a popular similarity metric, is a reliable criterion for medical image registration. In this paper, we present an improved method for multimodal image registration based on maximization of a new form of normalized MI incorporating particle swarm optimization, PSO, as a searching strategy. Also a new hybrid PSO algorithm is applied to approach more precise and robust results with better performance.
Robust Image Registration Based on Mutual Information Measure  [PDF]
Witold Kosiński, Pawe? Michalak, Piotr Gut
Journal of Signal and Information Processing (JSIP) , 2012, DOI: 10.4236/jsip.2012.32023
Abstract: A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.
Lookup Table Hough Transform for Real Time Range Image Segmentation and Featureless Co-Registration  [PDF]
Ben Gorte, George Sithole
Journal of Sensor Technology (JST) , 2012, DOI: 10.4236/jst.2012.23021
Abstract: The paper addresses range image segmentation, particularly of data recorded by range cameras, such as the Microsoft Kinect and the Mesa Swissranger SR4000. These devices record range images at video frame rates and allow for acquisition of 3-dimensional measurement sequences that can be used for 3D reconstruction of indoor environments from moving platforms. The role of segmentation is twofold. First the necessary image co-registration can be based on corresponding segments, instead of corresponding point features (which is common practice currently). Secondly, the segments can be used during subsequent object modelling. By realisising that planar regions in disparity images can be modelled as linear functions of the image coordinates, having integer values for both domain and range, the paper introduces a lookup table based implementation of local Hough transform, allowing to obtain good segmentation results at high speeds.
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