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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data VIII"

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Projection Selection for Binary Tomographic reconstruction Using Global Uncertainty 15th

Projection Selection for Binary Tomographic Reconstruction U...

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15th International conference on image Analysis and Recognition (ICIAR)

作者： Leko, Gabor Balazs, Peter Varga, Laszlo G. Univ Szeged Dept Image Proc & Comp Graph Arpad Ter 2 H-6720 Szeged Hungary

ISBN: (纸本)9783319930008;9783319929996

Binary tomography focuses on the problem of reconstructing homogeneous objects from a small number of their projections. In many applications, incomplete projection data holds insufficient information for the correct reconstruction of the original object. In this paper, we provide an optimization based method to select the "most informative" projection set, using information of global uncertainty. Beside the projection data we assume no further knowledge of the image to be reconstructed. Still, we achieve approximately as accurate reconstruction results, as it is possible to gain with a former method that uses blueprint images to find the optimal set of projections. We give experimental results for validating our approach on artificial images of various structures.

关键词： Binary tomography reconstruction Uncertainty Projection selection Optimization

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Radio Interferometric image reconstruction Algorithm Based on Primal Dual

Radio Interferometric Image Reconstruction Algorithm Based o...

引用

10th International conference on Communication Software and Networks (ICCSN)

作者： Lao, Bao-qiang An, Tao Chinese Acad Sci Shanghai Astron Observ Nandan Rd 80 Shanghai 200030 Peoples R China

ISBN: (纸本)9781538672242

The last-standing ill-posed inverse problem of radio interferometric imaging is analyzed. A new convex optimization algorithm, Primal Dual, is applied to radio interferometric imaging to recover the emission structure from the incomplete sampling. The convex optimization problem is defined as the primal problem and its dual problem is calculated. According to the characteristics of these two problems, the final solution of the primal problem is solved and the image of radio sources are reconstructed by iteratively alternating between solving the primal problem and the dual problem by using moreau decomposition, forward-backward method and the proximity operator. The algorithm parameters are analyzed from the simulations based on experimental data. The algorithm is validated and the parameters are optimized. The comparison between the images inferred from the compressed sensing and classical CLEAN algorithms shows that the signal to noise ratio and the dynamic range in the reconstructed images from compressed sensing are significantly higher than the CLEAN images, and the sidelobe levels are much lower in the former based on the real data. Besides the obvious efficacy of the Primal Dual algorithm in reconstructing extended source images, the major attraction is that it is simple to parallelize to support large data sets.

关键词： astronomy interferometric image reconstruction primal dual

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An Efficient Computer Aided Detection for 3D Neurostructural reconstruction of Magnetic Resonance images 9

An Efficient Computer Aided Detection for 3D Neurostructural...

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9th Cairo International Biomedical Engineering conference (CIBEC)

作者： Mabrouk, Mai S. Marzouk, Samir Y. Afify, Heba M. Misr Univ Sci & Technol MUST Univ Dept Biomed Engn Cairo Egypt Arab Acad Sci & Technol Basic & Appl Sci Dept Cairo Egypt Modern Univ Technol & Informat MTI Univ Bioelect Engn Dept Cairo Egypt

ISBN: (纸本)9781538681541

The comprehensive framework for analyzing brain images performs by the integration between three dimensional (3D) reconstruction and neuroimaging approach to realize brain diseases progressions. Computer Aided Detection (CAD) technology has numerous achievements in brain tumor processing for improving the quality of brain visualization to support neuroradiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging data, magnetic resonance imaging (MRI) has some restrictions that related to human errors and incomplete interpretation of brain tumor regions. Also, MRI scan produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, many algorithms are used computer-based classification to accurately distinguish between tumor regions from the brain MR images that provided an early diagnosis of brain diseases. This study investigated the CAD system using 3D image reconstruction of MR brain and tumor structures efficiently under MATLAB platform to recognize the location, volume, and type of brain tumors. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SVM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model depicted an advanced view for estimating of human brain diseases.

关键词： Computer-Aided Detection (CAD) Magnetic resonance imaging (MRI) 3D image reconstruction support vector machine (SVM) Fuzzy C-Means (FCM) algorithm

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Integrating data and image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium

Integrating Data and Image Domain Deep Learning for Limited ...

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International conference on Computer Vision Workshops (ICCV Workshops)

作者： Muhammad Usman Ghani W. Clem Karl Boston University

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 90 degree limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, non-destructive evaluation, and medical imaging.

关键词： Computed tomography image reconstruction Generators Machine learning data models

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Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen images

Generative Adversarial Networks Conditioned on Brain Activit...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： St-Yves, Ghislain Naselaris, Thomas Med Univ South Carolina Dept Neurosci 96 Jonathan Lucas St CSB 325c Charleston SC 29425 USA Med Univ South Carolina Dept Neurosci 96 Jonathan Lucas St CSB 325h Charleston SC 29425 USA

ISBN: (纸本)9781538666500

We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus, reconstructions of complex images from brain activity require a strong prior. Here we propose to train generative adversarial networks (GANs) to learn a generative model of images that is conditioned on measurements of brain activity. We consider two challenges of this approach: First, given that GANs require far more data to train than is typically collected in an fMRI experiment, how do we obtain enough samples to train a GAN that is conditioned on brain activity? Secondly, how do we ensure that our generated samples are robust against noise present in fMRI data? Our strategy to surmount both of these problems centers around the creation of surrogate brain activity samples that are generated by an encoding model. We find that the generative model thus trained generalizes to real fMRI data measured during perception of images and is able to reconstruct the basic outline of the stimuli.

关键词： Gallium nitride image reconstruction Brain modeling Generative adversarial networks Encoding Training

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Dictionary Learning from incomplete data for Efficient image Restoration 25

Dictionary Learning from Incomplete Data for Efficient Image...

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25th European Signal Processing conference (EUSIPCO)

作者： Naumova, Valeriya Schnass, Karin Simula Res Lab Sect Comp & Software Martin Linges 25 Fornebu Norway Univ Innsbruck Dept Math Technikerstr 13 A-6020 Innsbruck Austria

ISBN: (纸本)9780992862671

In real-world image processing applications, the data is high dimensional but the amount of high-quality data needed to train the model is very limited. In this paper, we demonstrate applicability of a recently presented method for dictionary learning from incomplete data, the so-called Iterative Thresholding and K residual Means for Masked data, to deal with high-dimensional data in an efficient way. In particular, the proposed algorithm incorporates a corruption model directly at the dictionary learning stage, also enabling reconstruction of the low-rank component again from corrupted signals. These modifications circumvent some difficulties associated with the efficient dictionary learning procedure in the presence of limited or incomplete data. We choose an image inpainting problem as a guiding example, and further propose a procedure for automatic detection and reconstruction of the low-rank component from incomplete data and adaptive parameter selection for the sparse image reconstruction. We benchmark the efficacy and efficiency of our algorithm in terms of computing time and accuracy on colour, 3D medical, and hyperspectral images by comparing it to its dictionary learning counterparts.

关键词： image reconstruction

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15th Pacific Rim International conference on Artificial Intelligence, PRICAI 2018

15th Pacific Rim International Conference on Artificial Inte...

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15th Pacific Rim International conference on Artificial Intelligence, PRICAI 2018

ISBN: (纸本)9783319973098

The proceedings contain 140 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Context-Aware Phrase Representation for Statistical Machine Translation;collaborating aesthetic change and heterogeneous information into recommender systems;latent subspace representation for multiclass classification;low-rank graph regularized sparse coding;decentralized multiagent reinforcement learning for efficient robotic control by coordination graphs;construction of microblog-specific chinese sentiment lexicon based on representation learning;Phonologically aware BiLSTM model for mongolian phrase break prediction with attention mechanism;multi-label Crowdsourcing Learning with incomplete Annotations;Multiple kernel fusion with HSIC lasso;DKE-RLS: A manifold reconstruction algorithm in label spaces with double kernel embedding-regularized least square;visualizing and Understanding Policy Networks of Computer Go;a Multi-objective optimization model for determining the optimal standard feasible neighborhood of intelligent vehicles;efficient detection of critical links to maintain performance of network with uncertain connectivity;mixed neighbourhood local search for customer order scheduling problem;graph based family relationship recognition from a single image;ACGAIL: Imitation learning about multiple intentions with auxiliary classifier GANs;Matching attention network for domain adaptation optimized by joint GANs and KL-MMD;attention based meta path fusion for heterogeneous information network embedding;an efficient auction with variable reserve prices for ridesourcing;matrix entropy driven maximum margin feature learning;learning relations from social tagging data;spectral image visualization using generative adversarial networks;fusing semantic prior based deep hashing method for fuzzy image retrieval;topic-Guided automatical human-simulated tweeting system;network embedding based on a quasi-local similarity measure;subclass maximum m

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15th Pacific Rim International conference on Artificial Intelligence, PRICAI 2018

15th Pacific Rim International Conference on Artificial Inte...

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15th Pacific Rim International conference on Artificial Intelligence, PRICAI 2018

ISBN: (纸本)9783319973036

The proceedings contain 82 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Context-Aware Phrase Representation for Statistical Machine Translation;collaborating aesthetic change and heterogeneous information into recommender systems;latent subspace representation for multiclass classification;low-rank graph regularized sparse coding;decentralized multiagent reinforcement learning for efficient robotic control by coordination graphs;construction of microblog-specific chinese sentiment lexicon based on representation learning;Phonologically aware BiLSTM model for mongolian phrase break prediction with attention mechanism;multi-label Crowdsourcing Learning with incomplete Annotations;Multiple kernel fusion with HSIC lasso;DKE-RLS: A manifold reconstruction algorithm in label spaces with double kernel embedding-regularized least square;visualizing and Understanding Policy Networks of Computer Go;a Multi-objective optimization model for determining the optimal standard feasible neighborhood of intelligent vehicles;efficient detection of critical links to maintain performance of network with uncertain connectivity;mixed neighbourhood local search for customer order scheduling problem;graph based family relationship recognition from a single image;ACGAIL: Imitation learning about multiple intentions with auxiliary classifier GANs;Matching attention network for domain adaptation optimized by joint GANs and KL-MMD;attention based meta path fusion for heterogeneous information network embedding;an efficient auction with variable reserve prices for ridesourcing;matrix entropy driven maximum margin feature learning;learning relations from social tagging data;spectral image visualization using generative adversarial networks;fusing semantic prior based deep hashing method for fuzzy image retrieval;topic-Guided automatical human-simulated tweeting system;network embedding based on a quasi-local similarity measure;subclass maximum ma

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Notice of Violation of IEEE Publication Principles: Radio Interferometric image reconstruction Algorithm Based on Primal Dual

Notice of Violation of IEEE Publication Principles: Radio In...

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International conference on Communication Software and Networks, ICCSN

作者： Bao-qiang Lao Tao An Shanghai Astronomical Observatory Chinese Academy of Sciences Nandan Road 80 Shanghai China

Notice of Violation of IEEE Publication Principles "Radio Interferometric image reconstruction Algorithm Based on Primal Dual" by Bao-qiang Lao and Tao An in the Proceedings of the 10th International conference on Communications Software and Networks, October 2018, pp.405-410 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicated content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. "Scalable Splitting Algorithms for Big-data Interferometric Imaging in the SKA Era" by Alexandru Onose, Rafael E. Carrillo, Audrey Repetti, Jason D. McEwen, Jean-Philippe Thiran, Jean-Christophe Pesquet and Yves Wiaux in Monthly Notices of the Royal Astronomical Society (MNRAS), September 2016, pp. 4314-4335 The last-standing ill-posed inverse problem of radio interferometric imaging is analyzed. A new convex optimization algorithm, Primal Dual, is applied to radio interferometric imaging to recover the emission structure from the incomplete sampling. The convex optimization problem is defined as the primal problem and its dual problem is calculated. According to the characteristics of these two problems, the final solution of the primal problem is solved and the image of radio sources are reconstructed by iteratively alternating between solving the primal problem and the dual problem by using moreau decomposition, forward-backward method and the proximity operator. The algorithm parameters are analyzed from the simulations based on experimental data. The algorithm is validated and the parameters are optimized. The comparison between the images inferred from the compressed sensing and classical CLEAN algorithms shows that the signal to noise ratio and the dynamic range in the reconstructed

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CT reconstruction from Limited-view Cone-beam Projections Using 3D image Moments 4

CT Reconstruction from Limited-view Cone-beam Projections Us...

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4th International conference on Information Science and Control Engineering (ICISCE)

作者： Xu, Kaikai Dai, Xiubin Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Geog & Biol Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538630136

Limiting the views of cone-beam CT scan can effectively reduce the damage to the human's health brought by X-rays when taking CT examination. However, the CT images reconstructed from the limited-view cone-beam projections will degrade greatly. To improve the quality of the reconstructed images when the cone-beam projections are incomplete, this paper proposes a new method by using 3D image moments for limited-view cone-beam CT reconstruction. It uses 3D geometric moments to establish the relationship between 3D Radon data and image moments. After converting the cone-beam projection acquired in limited views into known 3D Radon data, we use this relationship to estimate unknown 3D Radon data from known ones. In this way, 3D Radon data become complete. Finally, with help of the inverse formula of the first derivative of the 3D Radon transform, we reconstruct the 3D CT images from the complete 3D Radon data. The experimental results show that our method has good performance in limited-view CT reconstruction. The experimental results show that our method can improve the reconstructed cone-beam CT images when only limited-view projections are given.

关键词： cone-beam CT reconstruction limited-view projections 3D geometric moments 3D Radon transform

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