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A simple radar simulation tool for 3D objects based on blender 18

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A simple radar simulation tool for 3D objects based on blend...

18th International Radar Symposium, IRS 2017

作者： Ouza, Monika Ulrich, Michael Bin, Yang Institute of Signal Processing and System Theory University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

ISBN: (纸本)9783736993433

Simulation is a powerful tool for radar modulation and array design, the development of radar signal processing algorithms and the evaluation of defined scenarios. With the evolution of radar hardware, the range resolution of radar is often much smaller than the size of the radar targets which cannot be treated as point targets any more. In the literature, various electromagnetic field simulations or ray tracing approaches exist. However, these approaches are often computationally complex and a sound understanding of the algorithms is required. In this paper, we present a simple radar simulation framework for the simulation of 3D objects larger than the radar range resolution. For this purpose, we use the existing ray tracer of the graphic simulation program Blender and process the rendering results of Blender to simulate radar raw signals.. © 2017 DGON.

关键词： Radar

Automated Detection of High FDG Uptake Regions in CT Images

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Automated Detection of High FDG Uptake Regions in CT Images

IEEE International Conference on Acoustics, Speech and signal processing

作者： Annika Liebgott Florian Liebgott Bin Yang Sergios Gatidis Konstantin Nikolaou Institute of Signal Processing and System Theory University of Stuttgart Germany Department of Diagnostic and Interventional Radiology University Hospital of Tübingen Germany

ISBN: (纸本)9781538646595

Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thus, both modalities are necessary for diagnosis. Since both techniques expose the patient to radiation, it would be desirable to get the same information about metabolic activity contained in the PET image from a CT scan only. To investigate the relationship between both imaging modalities, we propose a machine learning approach to automatically identify regions in a CT scan corresponding to areas with high FDG uptakes in a PET image.

关键词： CT PET Radiomics machine learning support vector machine PET Scan X-Ray Computed Tomography Polyethylene Terephthalates Support Vector Network images Artificial Intelligence Machine Learning SMUG1 gene Automation

AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING

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AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC ...

IEEE International Conference on Acoustics, Speech and signal processing

作者： Thomas Küstner Marvin Jandt Annika Liebgott Lukas Mauch Petros Martirosian Fabian Bamberg Konstantin Nikolaou Sergios Gatidis Fritz Schick Bin Yang Department of Radiology Universtity of Tübingen Tübingen Germany Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781538646595

Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying image content or can be misinterpreted as anatomy. Patient-induced motion artifacts are still one of the major extrinsic factors which degrade image quality. In this work, an automatic reference-free motion artifact detection, including localization and quantification, is proposed which can be used prospectively as quality control (e.g. scan adjustment) or retrospectively as quality control (e.g. supported diagnosis). The detection is achieved via trained convolutional neural networks (CNN). This study focuses on investigating the optimal CNN architecture and required training set composition to derive a general and robust network for MR motion artifact detection. In a volunteer cohort an average accuracy of 91% was achieved.

关键词： machine-learning magnetic resonance imaging artifacts neural networks quality assessment Magnetic Resonance Imaging Quality assessment Neural network Artifacts Machine Learning Quality Control AUTOMATIC Medical imaging

LMPIT-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure

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arXiv 2018年

作者： Pries, Aaron Ramírez, David Schreier, Peter J. Signal & System Theory Group Paderborn University Paderborn Germany Signal Processing Group Universidad Carlos III de Madrid Leganés Spain Gregorio Marañón Health Research Institute Madrid Spain

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios;here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework. Copyright © 2018, The Authors. All rights reserved.

关键词： Cognitive radio

A machine-learning framework for automatic reference-free quality assessment in MRI

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arXiv 2018年

作者： Küstner, T. Gatidis, S. Liebgott, A. Schwartz, M. Mauch, L. Martirosian, P. Schmidt, H. Schwenzer, N.F. Nikolaou, K. Bamberg, F. Yang, B. Schick, F. Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Department of Radiology University of Tübingen Tübingen Germany Section on Experimental Radiology University of Tübingen Germany

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7% for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging

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Graphical User Interface for Medical Deep Learning - Applica...

Asia-Pacific signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Sebastian Milde Annika Liebgott Ziwei Wu Wenyi Feng Jiahuan Yang Lukas Mauch Petros Martirosian Fabian Bamberg Konstantin Nikolaou Sergios Gatidis Fritz Schick Bin Yang Thomas Küstner Institute of Signal Processing and System Theory University of Stuttgart Germany Department of Diagnostic and Interventional Radiology University Hospital of Tübingen Germany Section on Experimental Radiology University Hospital of Tübingen Germany

ISBN: (纸本)9781728102436;9789881476852

In clinical diagnostic, magnetic resonance imaging (MRI) is a valuable and versatile tool. The acquisition process is, however, susceptible to image distortions (artifacts) which may lead to degradation of image quality. Automated and reference-free localization and quantification of artifacts by employing convolutional neural networks (CNNs) is a promising way for early detection of artifacts. Training relies on high amount of expert labeled data which is a time-demanding process. Previous studies were based on global labels, i.e. a whole volume was automatically labeled as artifact-free or artifact-affected. However, artifact appearance is rather localized. We propose a local labeling which is conducted via a graphical user interface (GUI). Moreover, the GUI provides easy handling of data viewing, preprocessing (labeling, patching, data augmentation), network parametrization and training, data and network evaluation as well as deep visualization of the learned network content. The GUI is not limited to these features and will be extended in the future. The developed GUI is made publicly available and features a modular outline to target different applications of machine learning and deep learning, such as artifact detection, classification and segmentation.

关键词： Graphical user interfaces Training Labeling Data visualization Motion artifacts Three-dimensional displays Magnetic resonance imaging

Adversarial inpainting of medical image modalities

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arXiv 2018年

作者： Armanious, Karim Mecky, Youssef Gatidis, Sergios Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany University of Tübingen Department of Radiology Tübingen Germany German University in Cairo Faculty of Information Engineering and Technology Cairo Egypt

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Active learning with cross-dataset validation in event-based non-intrusive load monitoring

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Active learning with cross-dataset validation in event-based...

European signal processing Conference (EUSIPCO)

作者： Florian Liebgott Bin Yang Institute of Signal Processing and System Theory University of Stuttgart

Supervised event-based NILM systems usually require a large set of labeled training data to achieve high classification accuracies. To minimize the cost of labeling a sufficient amount of events, active learning can be employed. By using only a small set of labeled samples for initial training followed by selecting only the most informative samples to be labeled, the total number of labeled training samples can be reduced significantly. The performance of an active learning system strongly depends on the choice of the initial training set and the used query strategy. We thus investigated the impact of different methods to select the dataset for initial training as well as various query strategies on the resulting classification accuracy in an event-based NILM framework. For evaluation we used two datasets, BLUED and ISS kitchen, on which we were able to achieve high classification accuracies with significantly less training samples compared to conventional training without active learning.

关键词： Training Labeling Learning systems Uncertainty Training data Voltage measurement Feature extraction

Retrospective correction of rigid and non-rigid mr motion artifacts using GANS

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arXiv 2018年

作者： Armanious, Karim Gatidis, Sergios Nikolaou, Konstantin Yang, Bin Küstner, Thomas University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany University of Tübingen Department of Radiology Tübingen Germany King's College London Biomedical Engineering Department London United Kingdom

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance