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arXiv 2018年

作者： Du, Wenchao Chen, Hu Liao, Peixi Yang, Hongyu Wang, Ge Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Department of Scientific Research and Education Sixth People’s Hospital of Chengdu Chengdu610065 China Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intractable in the context of LDCT due to the statistical and technical uncertainties. In this paper, we apply the generative adversarial network (GAN) framework with a visual attention mechanism to deal with this problem in a data-driven/machine learning fashion. Our main idea is to inject visual attention knowledge into the learning process of GAN to provide a powerful prior of the noise distribution. By doing this, both the generator and discriminator networks are empowered with visual attention information so they will not only pay special attention to noisy regions and surrounding structures but also explicitly assess the local consistency of the recovered regions. Our experiments qualitatively and quantitatively demonstrate the effectiveness of the proposed method with clinic CT images. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Video Emotion Recognition using Hand-Crafted and Deep Learning Features

Video Emotion Recognition using Hand-Crafted and Deep Learni...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Xiaohan Xia Jiamu Liu Tao Yang Dongmei Jiang Wenjing Han Hichem Sahli NPU-VUB Joint AVSP Research Lab School of Computer Science Northwestern Polytechnical University (NPU) Shaanxi Key Lab on Speech and Image Information Processing 127 Youyi Xilu Xi’an China Samsum R&D Institute China-Beijing Floor 18 Sun Palace Building 12A Sun Palace Road Chaoyang District Beijing China Interuniversity Microelectronics Centre(IMEC) Kepeldreef 75 Heverlee Belgium

ISBN: (纸本)9781538653128

In this paper, we present our system designed for the video emotion recognition task of the Multimodal Emotion Challenge (MEC 2017). Histogram of Oriented Gradients (HOG), face shape (SHAPE), and geometric (GEO) features are extracted from the detected face images as hand-crafted video features. A pre-trained VGG-Face model is fine-tuned with the face images and emotion labels from the training set of CHEAVD 2.0, the outputs of the penultimate fully-connected layer (FC6) and the last fully-connected layer (FC7) are adopted as Deep Convolutional Neural Network (DCNN) based features. For each video clip, the hand-crafted features and DCNN based features are input into corresponding hidden Markov models (HMMs, one for each emotion class), respectively, for the initial emotion recognitions. The output logarithm likelihood probabilities from the HMMs are then ranked, and the orders constitute an eight-dimensional feature vector as inputs to a Naive Bayes classifier for decision fusion. Experimental results on the CHEAVD 2.0 database show that the combination of FC6, GEO, SHAPE and HOG features obtains the highest macro average precisions (MAPs) on both the validation set (46.61%) and test set (43.88%), which are 12.51% and 22.18% higher than the baseline results, respectively.

关键词： Face Emotion recognition Shape Feature extraction Hidden Markov models Task analysis

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Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications

arXiv

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arXiv 2018年

作者： Kim, Daeun Wisnowski, Jessica L. Nguyen, Christopher T. Haldar, Justin P. Biomedical Imaging Research Institute Cedars-Sinai Medical Center CA United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California CA United States Signal and Image Processing Institute University of Southern California CA United States Radiology Children's Hospital Los Angeles CA United States Pediatrics Children's Hospital Los Angeles CA United States Harvard Medical School Cardiovascular Research Center Massachusetts General Hospital MA United States Martinos Center for Biomedical Imaging Radiology Massachusetts General Hospital MA United States

Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this paper, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by leveraging multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients 1

引用

20th International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Chen, Lei Zhang, Han Thung, Kim-Han Liu, Luyan Lu, Junfeng Wu, Jinsong Wang, Qian Shen, Dinggang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Radiology and BRIC University of North Carolina Chapel Hill United States School of Biomedical Engineering Med-X Research Institute Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner. © Springer International Publishing AG 2017.

关键词： Forecasting

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Multi-scale application of camera calibration in tourism service platforms

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Journal of Information Hiding and Multimedia Signal processing 2017年第4期8卷 929-936页

作者： Ding, Xintao Luo, Yonglong Cai, Guorong Zhang, Xiaobo School of Territorial Resources and Tourism Anhui Normal University No. 819 South Jiuhua Road Wuhu241002 China School of Mathematics and Computer Science Anhui Normal University No. 819 South Jiuhua Road Wuhu241002 China Computer Engineering College Jimei University No. 183 Yinjiang Road Xiamen China Institute of Graphics and Image Processing Xianyang Normal University No. 43 Wenlin Road Xianyang China

Full view panoramas are able to simulate tele-presence or virtual reality experience, thus panoramic technology is an important implementation approach for the visual expression of tourism service platforms (TSP). On one hand, camera calibration, which is generally implemented under a certain resolution, may be a necessary preprocessing stage for panoramic technology. On the other hand, multi-scale applications are potentially involved in TSP for visualization expression. Therefore, calibration parameters resulting in one scale should be extended to multi-scale applications, but the extension research on this topic is insufficient. In this paper, the relations of intrinsic and distortion calibration parameters under different scales were both presented. Then, based on these two claims, a multi-scale correction algorithm is proposed. After demonstration experiments, with the help of the algorithm, different scale panoramas were employed for visualization expression in TSP. The multi-scale application of camera calibration in TSP is beneficial to bring intuition feelings to tourists. © 2017, Ubiquitous International. All rights reserved.

关键词： Calibration

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A Novel Reversible Ternary Embedding Algorithm Based on Modified Full Context Prediction Errors

A Novel Reversible Ternary Embedding Algorithm Based on Modi...

引用

2016 IEEE International Conference on Signal and image processing

作者： Li Li Chin-Chen Chang K.Bharanitharan Yanjun Liu Institute of Computer Graphics and Image Processing Hangzhou Dianzi University Department of Information Engineering and Computer Science Feng Chia University The Australian National University ACT

We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors(MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary *** the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by 1, which significantly reduces *** results confirm that the proposed algorithm achieves high PSNR while providing a higher embedding ***, results indicate that MFCPE outperforms the existing methods in terms of payload and the watermarked image quality.

关键词： reversible watermarking ternary embedding histogram shifting full context prediction

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

引用

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Reeb graphs through local binary patterns 10

Reeb graphs through local binary patterns

引用

10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

This paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. The nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such critical points using Local Binary Patterns. For one pixel the Local Binary Pattern feature vector is derived comparing this pixel to its neighbouring pixels in an environment of a certain radius. We start with an initial segmentation and a medial axis representation. Along this axis critical points are computed using Local Binary Patterns with the radius, defining the neighbouring pixels, set a bit larger than the radius according to the medial axis transformation. Critical points obtained in this way form the node set in a Reeb graph, edges are given through the connectivity of the skeleton. This approach aims at improving the representation of flawed segmented data. In the same way segmentation artefacts, as for example single pixels representing noise, may be corrected based on this analysis. © Springer International Publishing Switzerland 2015.

关键词： image segmentation

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