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< Page ,Total 47 >
Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction Scopus SCIE
期刊论文 | 2019 , 38 (2) , 371-382 | IEEE Transactions on Medical Imaging
WoS CC Cited Count: 1 SCOPUS Cited Count: 3
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Abstract :

IEEE Reducing the exposure to X-ray radiation while maintaining a clinically acceptable image quality is desirable in various CT applications. To realize low-dose CT (LdCT) imaging, model-based iterative reconstruction (MBIR) algorithms are widely adopted, but they require proper prior knowledge assumptions in the sinogram and/or image domains and involve tedious manual optimization of multiple parameters. In this work, we propose a deep learning (DL)-based strategy for MBIR to simultaneously address prior knowledge design and MBIR parameter selection in one optimization framework. Specifically, a parameterized plug-and-play alternating direction method of multipliers (3pADMM) is proposed for the general penalized weighted least-squares (PWLS) model, and then, by adopting the basic idea of DL, the parameterized plug-and-play (3p) prior and the related parameters are optimized simultaneously in a single framework using a large number of training data. The main contribution of this work is that the 3p prior and the related parameters in the proposed 3pADMM framework can be supervised and optimized simultaneously to achieve robust LdCT reconstruction performance. Experimental results obtained on clinical patient datasets demonstrate that the proposed method can achieve promising gains over existing algorithms for LdCT image reconstruction in terms of noise-induced artifact suppression and edge detail preservation.

Keyword :

Biomedical imaging Computed tomography deep learning Image reconstruction low-dose CT Machine learning Optimization parameterized plug-and-play ADMM X-ray imaging

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GB/T 7714 He, Ji , Yang, Yan , Wang, Yongbo et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction [J]. | IEEE Transactions on Medical Imaging , 2019 , 38 (2) : 371-382 .
MLA He, Ji et al. "Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction" . | IEEE Transactions on Medical Imaging 38 . 2 (2019) : 371-382 .
APA He, Ji , Yang, Yan , Wang, Yongbo , Zeng, Dong , Bian, Zhaoying , Zhang, Hao et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction . | IEEE Transactions on Medical Imaging , 2019 , 38 (2) , 371-382 .
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Efficient projected gradient methods for cardinality constrained optimization Scopus CSCD SCIE
期刊论文 | 2019 , 62 (2) , 245-268 | Science China Mathematics
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© 2018 Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature Sparse optimization has attracted increasing attention in numerous areas such as compressed sensing, financial optimization and image processing. In this paper, we first consider a special class of cardinality constrained optimization problems, which involves box constraints and a singly linear constraint. An effcient approach is provided for calculating the projection over the feasibility set after a careful analysis on the projection subproblem. Then we present several types of projected gradient methods for a general class of cardinality constrained optimization problems. Global convergence of the methods are established under suitable assumptions. Finally, we illustrate some applications of the proposed methods for signal recovery and index tracking. Especially for index tracking, we propose a new model subject to an adaptive upper bound on the sparse portfolio weights. The computational results demonstrate that the proposed projected gradient methods are effcient in terms of solution quality.

Keyword :

global convergence index tracking projected gradient method signal recovery sparse approximation

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GB/T 7714 Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu et al. Efficient projected gradient methods for cardinality constrained optimization [J]. | Science China Mathematics , 2019 , 62 (2) : 245-268 .
MLA Xu, Fengmin et al. "Efficient projected gradient methods for cardinality constrained optimization" . | Science China Mathematics 62 . 2 (2019) : 245-268 .
APA Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu , Xu, Zongben . Efficient projected gradient methods for cardinality constrained optimization . | Science China Mathematics , 2019 , 62 (2) , 245-268 .
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An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization SCIE
期刊论文 | 2019 , 38 (2) , 360-370 | IEEE TRANSACTIONS ON MEDICAL IMAGING
WoS CC Cited Count: 2
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Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. Specifically, a newly developed low-rank tensor decomposition with spatial-temporal total variation (LRTD-STTV) regularization is incorporated into the reconstruction model. In the LRTD-STTV regularization, the tensor Tucker decomposition is used to describe global spatial-temporal correlations hidden in the sequential CPCT images, and it is superior to the matricization model (i.e., low-rank model) that fails to fully investigate the prior knowledge of the intrinsic structures of the CPCT images after vectorizing the CPCT images. Moreover, the spatial-temporal TV regularization is used to characterize the local piecewise smooth structure in the spatial domain and the pixels' similarity with the adjacent frames in the temporal domain, because the intensity at each pixel in CPCT images is similar to its neighbors. Therefore, the presented LRTD-STTV model can efficiently deliver faithful underlying information of the CPCT images and preserve the spatial structures. An efficient alternating direction method of multipliers algorithm is also developed to solve the presented LRTD-STTV model. Extensive experimental results on numerical phantom and patient data are clearly demonstrated that the presented model can significantly improve the quality of CPCT images and provide accurate diagnostic features in hemodynamic maps for low-dose cases compared with the existing popular algorithms.

Keyword :

low-rank Computed tomography tensor regularization cerebral perfusion

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GB/T 7714 Li, Sui , Zeng, Dong , Peng, Jiangjun et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) : 360-370 .
MLA Li, Sui et al. "An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 38 . 2 (2019) : 360-370 .
APA Li, Sui , Zeng, Dong , Peng, Jiangjun , Bian, Zhaoying , Zhang, Hao , Xie, Qi et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) , 360-370 .
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Model-driven deep-learning SCIE Scopus CSCD
期刊论文 | 2018 , 5 (1) , 22-24 | NATIONAL SCIENCE REVIEW
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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GB/T 7714 Xu, Zongben , Sun, Jian . Model-driven deep-learning [J]. | NATIONAL SCIENCE REVIEW , 2018 , 5 (1) : 22-24 .
MLA Xu, Zongben et al. "Model-driven deep-learning" . | NATIONAL SCIENCE REVIEW 5 . 1 (2018) : 22-24 .
APA Xu, Zongben , Sun, Jian . Model-driven deep-learning . | NATIONAL SCIENCE REVIEW , 2018 , 5 (1) , 22-24 .
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Robust subspace clustering via penalized mixture of Gaussians EI SCIE Scopus
期刊论文 | 2018 , 278 , 4-11 | NEUROCOMPUTING
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Many problems in computer vision and pattern recognition can be posed as learning low-dimensional subspace structures from high-dimensional data. Subspace clustering represents a commonly utilized subspace learning strategy. The existing subspace clustering models mainly adopt a deterministic loss function to describe a certain noise type between an observed data matrix and its self-expressed form. However, the noises embedded in practical high-dimensional data are generally non-Gaussian and have much more complex structures. To address this issue, this paper proposes a robust subspace clustering model by embedding the Mixture of Gaussians (MoG) noise modeling strategy into the low-rank representation (LRR) subspace clustering model. The proposed MoG-LRR model is capitalized on its adapting to a wider range of noise distributions beyond current methods due to the universal approximation capability of MoG. Additionally, a penalized likelihood method is encoded into this model to facilitate selecting the number of mixture components automatically. A modified Expectation Maximization (EM) algorithm is also designed to infer the parameters involved in the proposed PMoG-LRR model. The superiority of our method is demonstrated by extensive experiments on face clustering and motion segmentation datasets. (C) 2017 Elsevier B. V. All rights reserved.

Keyword :

Mixture of Gaussians Subspace clustering Expectation maximization Low-rank representation

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GB/T 7714 Yao, Jing , Cao, Xiangyong , Zhao, Qian et al. Robust subspace clustering via penalized mixture of Gaussians [J]. | NEUROCOMPUTING , 2018 , 278 : 4-11 .
MLA Yao, Jing et al. "Robust subspace clustering via penalized mixture of Gaussians" . | NEUROCOMPUTING 278 (2018) : 4-11 .
APA Yao, Jing , Cao, Xiangyong , Zhao, Qian , Meng, Deyu , Xu, Zongben . Robust subspace clustering via penalized mixture of Gaussians . | NEUROCOMPUTING , 2018 , 278 , 4-11 .
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Denoising Hyperspectral Image With Non-i.i.d. Noise Structure EI SCIE Scopus
期刊论文 | 2018 , 48 (3) , 1054-1066 | IEEE TRANSACTIONS ON CYBERNETICS
WoS CC Cited Count: 3 SCOPUS Cited Count: 4
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Abstract :

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

Keyword :

low-rank matrix factorization (LRMF) Hyperspectral image (HSI) denoising non independent and identically distributed (i.i.d.) noise modeling

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GB/T 7714 Chen, Yang , Cao, Xiangyong , Zhao, Qian et al. Denoising Hyperspectral Image With Non-i.i.d. Noise Structure [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) : 1054-1066 .
MLA Chen, Yang et al. "Denoising Hyperspectral Image With Non-i.i.d. Noise Structure" . | IEEE TRANSACTIONS ON CYBERNETICS 48 . 3 (2018) : 1054-1066 .
APA Chen, Yang , Cao, Xiangyong , Zhao, Qian , Meng, Deyu , Xu, Zongben . Denoising Hyperspectral Image With Non-i.i.d. Noise Structure . | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) , 1054-1066 .
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Greedy Criterion in Orthogonal Greedy Learning EI SCIE Scopus
期刊论文 | 2018 , 48 (3) , 955-966 | IEEE TRANSACTIONS ON CYBERNETICS
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Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then builds the estimator through orthogonal projection. In this paper, we found that SGD is not the unique greedy criterion and introduced a new greedy criterion, called as "delta-greedy threshold" for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL. Numerical experiments are also provided to support that this new scheme can achieve almost optimal generalization performance while requiring less computation than OGL.

Keyword :

Generalization performance orthogonal greedy learning (OGL) supervised learning greedy criterion greedy algorithms

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GB/T 7714 Xu, Lin , Lin, Shaobo , Zeng, Jinshan et al. Greedy Criterion in Orthogonal Greedy Learning [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) : 955-966 .
MLA Xu, Lin et al. "Greedy Criterion in Orthogonal Greedy Learning" . | IEEE TRANSACTIONS ON CYBERNETICS 48 . 3 (2018) : 955-966 .
APA Xu, Lin , Lin, Shaobo , Zeng, Jinshan , Liu, Xia , Fang, Yi , Xu, Zongben . Greedy Criterion in Orthogonal Greedy Learning . | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) , 955-966 .
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Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network EI SCIE Scopus
期刊论文 | 2018 , 27 (5) , 2354-2367 | IEEE TRANSACTIONS ON IMAGE PROCESSING
WoS CC Cited Count: 5 SCOPUS Cited Count: 8
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Abstract :

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using alpha-expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Keyword :

Hyperspectral image classification Markov random fields convolutional neural networks

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GB/T 7714 Cao, Xiangyong , Zhou, Feng , Xu, Lin et al. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) : 2354-2367 .
MLA Cao, Xiangyong et al. "Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 27 . 5 (2018) : 2354-2367 .
APA Cao, Xiangyong , Zhou, Feng , Xu, Lin , Meng, Deyu , Xu, Zongben , Paisley, John . Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) , 2354-2367 .
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A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization EI SCIE Scopus
期刊论文 | 2018 , 29 (5) , 1716-1731 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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Abstract :

Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresh-olding methods is to find a so-called k-sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true k-sparse solution. In the noiseless case, it is easy to locate the exact position of the k-sparse solution from the distribution of the solutions found by our proposed method. Therefore, our method has the ability to detect the true sparsity. Moreover, any iterative thresholding methods can be used as a local optimizer in our proposed method, and no prior estimation of sparsity is required. The proposed method can also be extended to solve sparse optimization problems with noise. Extensive experiments have been conducted to study its performance on artificial signals and magnetic resonance imaging signals. Our experimental results have shown that our proposed method is very effective for detecting sparsity and can improve the reconstruction ability of existing iterative thresholding methods.

Keyword :

multiobjective evolutionary approach regularization Sparse optimization iterative thresholding

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GB/T 7714 Li, Hui , Zhang, Qingfu , Deng, Jingda et al. A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (5) : 1716-1731 .
MLA Li, Hui et al. "A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29 . 5 (2018) : 1716-1731 .
APA Li, Hui , Zhang, Qingfu , Deng, Jingda , Xu, Zong-Ben . A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (5) , 1716-1731 .
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LdCT-Net : low-dose CT image reconstruction strategy driven by a deep dual network EI CPCI-S Scopus
会议论文 | 2018 , 10573 | Conference on Medical Imaging - Physics of Medical Imaging
SCOPUS Cited Count: 2
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High radiation dose in CT imaging is a major concern, which could result in increased lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining clinically acceptable CT image quality is desirable in CT application. One of the most successful strategies is to apply statistical iterative reconstruction (SIR) to obtain promising CT images at low dose. Although the SIR algorithms are effective, they usually have three disadvantages: 1) desired-image prior design; 2) optimal parameters selection; and 3) high computation burden. To address these three issues, in this work, inspired by the deep learning network for inverse problem, we present a low-dose CT image reconstruction strategy driven by a deep dual network (LdCT-Net) to yield high-quality CT images by incorporating both projection information and image information simultaneously. Specifically, the present LdCT-Net effectively reconstructs CT images by adequately taking into account the information learned in dual-domain, i.e., projection domain and image domain, simultaneously. The experiment results on patients data demonstrated the present LdCT-Net can achieve promising gains over other existing algorithms in terms of noise-induced artifacts suppression and edge details preservation.

Keyword :

low-dose computed tomography deep learning network statistical iterative reconstruction LdCT-Net

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GB/T 7714 He, Ji , Wang, Yangbo , Yang, Yan et al. LdCT-Net : low-dose CT image reconstruction strategy driven by a deep dual network [C] . 2018 .
MLA He, Ji et al. "LdCT-Net : low-dose CT image reconstruction strategy driven by a deep dual network" . (2018) .
APA He, Ji , Wang, Yangbo , Yang, Yan , Bian, Zhaoying , Zeng, Dong , Sun, Jian et al. LdCT-Net : low-dose CT image reconstruction strategy driven by a deep dual network . (2018) .
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