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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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Abstract :

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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Active Self-Paced Learning for Cost-Effective and Progressive Face Identification SCIE
期刊论文 | 2018 , 40 (1) , 7-19 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
WoS CC Cited Count: 11
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Abstract :

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/

Keyword :

Cost-effective model face identification self-paced learning incremental processing active learning

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GB/T 7714 Lin, Liang , Wang, Keze , Meng, Deyu et al. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) : 7-19 .
MLA Lin, Liang et al. "Active Self-Paced Learning for Cost-Effective and Progressive Face Identification" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40 . 1 (2018) : 7-19 .
APA Lin, Liang , Wang, Keze , Meng, Deyu , Zuo, Wangmeng , Zhang, Lei . Active Self-Paced Learning for Cost-Effective and Progressive Face Identification . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2018 , 40 (1) , 7-19 .
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Infrared small-dim target detection based on Markov random field guided noise modeling EI SCIE Scopus
期刊论文 | 2018 , 76 , 463-475 | PATTERN RECOGNITION
WoS CC Cited Count: 9 SCOPUS Cited Count: 12
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Abstract :

Small target detection is one of the key techniques in infrared search and tracking applications. When small targets are very dim and of low signal-to-noise ratio, they are very similar to background noise, which usually causes high false alarm rates for conventional methods. To address this problem, we novelly treat the small-dim targets as a special sparse noise component of the complex background noise and adopt Mixture of Gaussians (MoG) with Markov random field (MRF) to model this problem. Firstly, the spatio-temporal patch image is constructed using several consecutive frames to utilize the temporal information of the image sequence. Then, the MRF guided MoG noise model under the Bayesian framework is proposed to model the small target detection problem. After that, by variational Bayesian, the small target component can be effectively separated from complex background noise. Finally, a simple adaptive segmentation method is used to extract small targets. Several series of experiments are done to evaluate the proposed method and the results show that the proposed method is robust for real infrared images with complex background. (C) 2017 Elsevier Ltd. All rights reserved.

Keyword :

Infrared image Mixture of Gaussians Variational Bayesian Markov random field Small target detection

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GB/T 7714 Gao, Chenqiang , Wang, Lan , Xiao, Yongxing et al. Infrared small-dim target detection based on Markov random field guided noise modeling [J]. | PATTERN RECOGNITION , 2018 , 76 : 463-475 .
MLA Gao, Chenqiang et al. "Infrared small-dim target detection based on Markov random field guided noise modeling" . | PATTERN RECOGNITION 76 (2018) : 463-475 .
APA Gao, Chenqiang , Wang, Lan , Xiao, Yongxing , Zhao, Qian , Meng, Deyu . Infrared small-dim target detection based on Markov random field guided noise modeling . | PATTERN RECOGNITION , 2018 , 76 , 463-475 .
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Deep self-paced learning for person re-identification EI SCIE Scopus
期刊论文 | 2018 , 76 , 739-751 | PATTERN RECOGNITION
WoS CC Cited Count: 5 SCOPUS Cited Count: 6
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Abstract :

Person re-identification (Re-ID) usually suffers from noisy samples with background clutter and mutual occlusion, which makes it extremely difficult to distinguish different individuals across the disjoint camera views. In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive the adaptive weights to samples based on both the training loss and model age. As a result, the high-confidence fidelity samples will be emphasized and the low-confidence noisy samples will be suppressed at early stage of the whole training process. Such a learning regime is naturally implemented under a self-paced learning (SPL) framework, in which samples weights are adaptively updated based on both model age and sample loss using an alternative optimization method. Secondly, we introduce a symmetric regularizer term to revise the asymmetric gradient back-propagation derived by the relative distance metric, so as to simultaneously minimize the intra-class distance and maximize the inter-class distance in each triplet unit. Finally, we build a part-based deep neural network, in which the features of different body parts are first discriminately learned in the lower convolutional layers and then fused in the higher fully connected layers. Experiments on several benchmark datasets have demonstrated the superior performance of our method as compared with the state-of-the-art approaches. (C) 2017 Published by Elsevier Ltd.

Keyword :

Self-paced learning Convolutional neural network Person re-identification Metric learning

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GB/T 7714 Zhou, Sanping , Wang, Jinjun , Meng, Deyu et al. Deep self-paced learning for person re-identification [J]. | PATTERN RECOGNITION , 2018 , 76 : 739-751 .
MLA Zhou, Sanping et al. "Deep self-paced learning for person re-identification" . | PATTERN RECOGNITION 76 (2018) : 739-751 .
APA Zhou, Sanping , Wang, Jinjun , Meng, Deyu , Xin, Xiaomeng , Li, Yubing , Gong, Yihong et al. Deep self-paced learning for person re-identification . | PATTERN RECOGNITION , 2018 , 76 , 739-751 .
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Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure SCIE PubMed Scopus
期刊论文 | 2018 , 19 (1) | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity.

Keyword :

descriptor selection regularization log-sum QSAR biological activity

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GB/T 7714 Xia, Liang-Yong , Wang, Yu-Wei , Meng, De-Yu et al. Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure [J]. | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2018 , 19 (1) .
MLA Xia, Liang-Yong et al. "Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure" . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 19 . 1 (2018) .
APA Xia, Liang-Yong , Wang, Yu-Wei , Meng, De-Yu , Yao, Xiao-Jun , Chai, Hua , Liang, Yong . Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure . | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES , 2018 , 19 (1) .
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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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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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Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition EI SCIE Scopus
期刊论文 | 2018 , 11 (4) , 1227-1243 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
WoS CC Cited Count: 4 SCOPUS Cited Count: 4
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Abstract :

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the l(1) norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

Keyword :

low-rank tensor decomposition Hyperspectral image (HSI) total variation (TV) mixed noise

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GB/T 7714 Wang, Yao , Peng, Jiangjun , Zhao, Qian et al. Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2018 , 11 (4) : 1227-1243 .
MLA Wang, Yao et al. "Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11 . 4 (2018) : 1227-1243 .
APA Wang, Yao , Peng, Jiangjun , Zhao, Qian , Leung, Yee , Zhao, Xi-Le , Meng, Deyu . Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2018 , 11 (4) , 1227-1243 .
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CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization 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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Abstract :

Ischemic stroke is the leading cause of serious and long-term disability worldwide. The cerebral perfusion computed tomography (CPCT) is an important imaging modality for diagnosis in case of an ischemic stroke event by providing cerebral hemodynamic information. However, due to the dynamic sequential scans in CPCT, the associative radiation dose unavoidably increases compared with conventional CT. In this work, we present a robust CPCT image restoration algorithm with a spatial total variation (TV) regularization and a low rank tensor decomposition (LRTD) to estimate high-quality CPCT images and corresponding hemodynamic map in the case of low-dose, which is termed "CPCT-LRTDTV". Specifically, in the LRTDTV regularization, the spatial TV is introduced to describe local smoothness of the CPCT images, and the LRTD is adopted to fully characterize spatial and time dimensional correlation existing in the CPCT images. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the presented CPCT-LRTDTV algorithm, both qualitative and quantitative experiments are conducted on digital perfusion brain phantom and clinical patient. Experimental results demonstrate that the present CPCT-LRTDTV algorithm is superior to other existing algorithms with better noise-induced artifacts reduction, resolution preservation and accurate hemodynamic map estimation.

Keyword :

low-dose cerebral perfusion computed tomography restoration Low rank tensor decomposition total variation

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GB/T 7714 Peng, Jianjun , Zeng, Dong , Ma, Jianhua et al. CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization [C] . 2018 .
MLA Peng, Jianjun et al. "CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization" . (2018) .
APA Peng, Jianjun , Zeng, Dong , Ma, Jianhua , Wang, Yao , Meng, Deyu . CPCT-LRTDTV: Cerebral perfusion CT image restoration via a low rank tensor decomposition with total variation regularization . (2018) .
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