The Experts below are selected from a list of 111 Experts worldwide ranked by ideXlab platform
Anand P Santhanam - One of the best experts on this subject based on the ideXlab platform.
-
tu h campus jep1 01 characterization of a Parameterized image similarity cost function for automated patient and site specific dir accuracy optimizations
Medical Physics, 2016Co-Authors: J Neylon, Yugang Min, Katelyn Hasse, D Low, Anand P SanthanamAbstract:Purpose: Clinical deformable image Registration (DIR) accuracy is difficult to quantify, making validation and verification manual and time consuming. It has also been seen that DIR accuracy can be improved by patient and site specific optimization. We previously investigated a cost function to Parameterize image similarity metrics (ISMs) and provide a quantifiable comparison of DIR performance. In this abstract, we characterize and optimize the cost function response (CFR) and correlation to target Registration error (TRE) in a dense Registration Parameter space. Methods: A validated head-and-neck biomechanical model was used to induce clinical deformations in patient kVCT data (source), outputting known ground truth deformation vector fields (DVFs) and simulated CTs of deformed anatomy (target). The source and target were registered repeatedly using an in-house optical flow DIR, systematically sampling the Registration Parameter space, and producing TREs and data volumes deformed according to the DIR DVF (warp). Normalized mutual information (NMI) was chosen as the test ISM. The cost function variables (CFV) were also systematically sampled to characterize their effect on the CFR. Sub-volume analysis was performed on the data, defined by structures of interest in the head-and-neck region. Results: Strong inverse correlation was observed between the TRE and CFR when the data was subdivided around specific structures like the PTV, parotids, and mandible, reaching values of −0.95. The CFR slope changed by an order of magnitude by manipulating the CFV. Response and correlation were improved by adjusting on a structure specific basis. The CFR achieved a predominantly convex response, which should facilitate its use as an energy term in an optimization scheme. Conclusions: Replacing the time consuming, manually placed landmark assessment methodology is necessary for patient and site specific Registration optimization to be clinically feasible. The proposed cost function is a promising alternative for fast, fully automated DIR performance quantification.
-
fast simulated annealing and adaptive monte carlo sampling based Parameter optimization for dense optical flow deformable image Registration of 4dct lung anatomy
Proceedings of SPIE, 2016Co-Authors: T Dou, Yugang Min, John Neylon, David Thomas, Patrick A Kupelian, Anand P SanthanamAbstract:Deformable image Registration (DIR) is an important step in radiotherapy treatment planning. An optimal input Registration Parameter set is critical to achieve the best Registration performance with the specific algorithm. Methods In this paper, we investigated a Parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex Parameter optimization problem. The metric for Registration error for a given Parameter set was computed using landmark-based mean target Registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the Parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes. Numerical analyses on the Parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets. Results showed that the proposed method efficiently estimated the optimum Parameters for optical-flow and closely matched the best Registration Parameters obtained using an exhaustive Parameter search method.
-
Medical Imaging: Image-Guided Procedures - Fast simulated annealing and adaptive Monte Carlo sampling based Parameter optimization for dense optical-flow deformable image Registration of 4DCT lung anatomy
Medical Imaging 2016: Image-Guided Procedures Robotic Interventions and Modeling, 2016Co-Authors: T Dou, Yugang Min, John Neylon, David Thomas, Patrick A Kupelian, Anand P SanthanamAbstract:Deformable image Registration (DIR) is an important step in radiotherapy treatment planning. An optimal input Registration Parameter set is critical to achieve the best Registration performance with the specific algorithm. Methods In this paper, we investigated a Parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex Parameter optimization problem. The metric for Registration error for a given Parameter set was computed using landmark-based mean target Registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the Parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes. Numerical analyses on the Parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets. Results showed that the proposed method efficiently estimated the optimum Parameters for optical-flow and closely matched the best Registration Parameters obtained using an exhaustive Parameter search method.
Youwen Zhuang - One of the best experts on this subject based on the ideXlab platform.
-
Infrared and visual image Registration based on mutual information with a combined particle swarm optimization – Powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Gong XuemeiAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
infrared and visual image Registration based on mutual information with a combined particle swarm optimization powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Xuemei GongAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
ir and visual image Registration based on mutual information and pso powell algorithm
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 2014Co-Authors: Youwen Zhuang, Kun Gao, Xianghu MiuAbstract:Infrared and visual image Registration has a wide application in the fields of remote sensing and military. Mutual information (MI) has proved effective and successful in infrared and visual image Registration process. To find the most appropriate Registration Parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning, while the weakness is low search performance in late search stage. In image Registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image Registration, it is often obstructed by local maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a Registration Parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision Registration Parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a good solution to register infrared difference of two images and has a greater performance on time and precision than traditional and visible images.
Kun Gao - One of the best experts on this subject based on the ideXlab platform.
-
A mosaic method for multichannel sequence starry images via multiscale edge-preserving spatio-temporal context filtering
2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging Spectroscopy and Signal Processing Technology, 2020Co-Authors: Zhijia Yang, Xiaozheng Liu, Yuxuan Mao, Tinghua Zhang, Kun GaoAbstract:Astronomical observation and spatial target surveillance applications often require mosaic processing of starry images acquired by multiple image sensors to expand the Fields of View (FOV) or improve the resolutions. Due to the low SNR (Signal-to-Noise Ratio), lack of star point texture information and vulnerability of atmospheric turbulence of the starry image properties, traditional mosaic methods are prone to failures during feature point extraction. In this paper, Spatio-Temporal Context (STC) filtering is introduced as the preprocessing procedure to suppress the background interferences. We have improved the classical STC filtering and expands it into multi-scale space combining with Rolling-Guidance Filtering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatial variant interference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, are suppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we produced the feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometric invariant cost function for the input image pairs to be stitched. After Random Sample Consensus (RANSAC) processing, the mismatched feature point pairs in the star-point sets are excluded. The subsequent procedures of the Registration Parameter calculation, image fusion and parallax correction processing are adopted to complete the mosaic processing. The results of digital simulation and practical processing show that the proposed method for the multichannel sequence starry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustly comparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.
-
infrared and visual image Registration based on mutual information with a combined particle swarm optimization powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Xuemei GongAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
Infrared and visual image Registration based on mutual information with a combined particle swarm optimization – Powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Gong XuemeiAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
ir and visual image Registration based on mutual information and pso powell algorithm
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 2014Co-Authors: Youwen Zhuang, Kun Gao, Xianghu MiuAbstract:Infrared and visual image Registration has a wide application in the fields of remote sensing and military. Mutual information (MI) has proved effective and successful in infrared and visual image Registration process. To find the most appropriate Registration Parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning, while the weakness is low search performance in late search stage. In image Registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image Registration, it is often obstructed by local maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a Registration Parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision Registration Parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a good solution to register infrared difference of two images and has a greater performance on time and precision than traditional and visible images.
Xianghu Miu - One of the best experts on this subject based on the ideXlab platform.
-
Infrared and visual image Registration based on mutual information with a combined particle swarm optimization – Powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Gong XuemeiAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
infrared and visual image Registration based on mutual information with a combined particle swarm optimization powell search algorithm
Optik, 2016Co-Authors: Youwen Zhuang, Kun Gao, Xianghu Miu, Lu Han, Xuemei GongAbstract:Abstract Infrared and visual image Registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image Registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate Registration Parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a Registration Parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision Registration Parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.
-
ir and visual image Registration based on mutual information and pso powell algorithm
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 2014Co-Authors: Youwen Zhuang, Kun Gao, Xianghu MiuAbstract:Infrared and visual image Registration has a wide application in the fields of remote sensing and military. Mutual information (MI) has proved effective and successful in infrared and visual image Registration process. To find the most appropriate Registration Parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning, while the weakness is low search performance in late search stage. In image Registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image Registration, it is often obstructed by local maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a Registration Parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision Registration Parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a good solution to register infrared difference of two images and has a greater performance on time and precision than traditional and visible images.
Yugang Min - One of the best experts on this subject based on the ideXlab platform.
-
tu h campus jep1 01 characterization of a Parameterized image similarity cost function for automated patient and site specific dir accuracy optimizations
Medical Physics, 2016Co-Authors: J Neylon, Yugang Min, Katelyn Hasse, D Low, Anand P SanthanamAbstract:Purpose: Clinical deformable image Registration (DIR) accuracy is difficult to quantify, making validation and verification manual and time consuming. It has also been seen that DIR accuracy can be improved by patient and site specific optimization. We previously investigated a cost function to Parameterize image similarity metrics (ISMs) and provide a quantifiable comparison of DIR performance. In this abstract, we characterize and optimize the cost function response (CFR) and correlation to target Registration error (TRE) in a dense Registration Parameter space. Methods: A validated head-and-neck biomechanical model was used to induce clinical deformations in patient kVCT data (source), outputting known ground truth deformation vector fields (DVFs) and simulated CTs of deformed anatomy (target). The source and target were registered repeatedly using an in-house optical flow DIR, systematically sampling the Registration Parameter space, and producing TREs and data volumes deformed according to the DIR DVF (warp). Normalized mutual information (NMI) was chosen as the test ISM. The cost function variables (CFV) were also systematically sampled to characterize their effect on the CFR. Sub-volume analysis was performed on the data, defined by structures of interest in the head-and-neck region. Results: Strong inverse correlation was observed between the TRE and CFR when the data was subdivided around specific structures like the PTV, parotids, and mandible, reaching values of −0.95. The CFR slope changed by an order of magnitude by manipulating the CFV. Response and correlation were improved by adjusting on a structure specific basis. The CFR achieved a predominantly convex response, which should facilitate its use as an energy term in an optimization scheme. Conclusions: Replacing the time consuming, manually placed landmark assessment methodology is necessary for patient and site specific Registration optimization to be clinically feasible. The proposed cost function is a promising alternative for fast, fully automated DIR performance quantification.
-
fast simulated annealing and adaptive monte carlo sampling based Parameter optimization for dense optical flow deformable image Registration of 4dct lung anatomy
Proceedings of SPIE, 2016Co-Authors: T Dou, Yugang Min, John Neylon, David Thomas, Patrick A Kupelian, Anand P SanthanamAbstract:Deformable image Registration (DIR) is an important step in radiotherapy treatment planning. An optimal input Registration Parameter set is critical to achieve the best Registration performance with the specific algorithm. Methods In this paper, we investigated a Parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex Parameter optimization problem. The metric for Registration error for a given Parameter set was computed using landmark-based mean target Registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the Parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes. Numerical analyses on the Parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets. Results showed that the proposed method efficiently estimated the optimum Parameters for optical-flow and closely matched the best Registration Parameters obtained using an exhaustive Parameter search method.
-
Medical Imaging: Image-Guided Procedures - Fast simulated annealing and adaptive Monte Carlo sampling based Parameter optimization for dense optical-flow deformable image Registration of 4DCT lung anatomy
Medical Imaging 2016: Image-Guided Procedures Robotic Interventions and Modeling, 2016Co-Authors: T Dou, Yugang Min, John Neylon, David Thomas, Patrick A Kupelian, Anand P SanthanamAbstract:Deformable image Registration (DIR) is an important step in radiotherapy treatment planning. An optimal input Registration Parameter set is critical to achieve the best Registration performance with the specific algorithm. Methods In this paper, we investigated a Parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex Parameter optimization problem. The metric for Registration error for a given Parameter set was computed using landmark-based mean target Registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the Parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes. Numerical analyses on the Parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets. Results showed that the proposed method efficiently estimated the optimum Parameters for optical-flow and closely matched the best Registration Parameters obtained using an exhaustive Parameter search method.