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

共 1583 条记录，以下是461-470 订阅

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Computational wavelength resolution for in-line lensless holography: phase-coded diffraction patterns and wavefront group-sparsity

Computational wavelength resolution for in-line lensless hol...

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conference on Digital Optical Technologies

作者： Katkovnik, vladimir Shevkunov, Igor Petrov, Nikolay v. Egiazarian, Karen Tampere Univ Technol Lab Signal Proc POB 553 FI-33101 Tampere Finland ITMO Univ Dept Photon & Opt Informat Technol St Petersburg Russia

ISBN: (数字)9781510611160

ISBN: (纸本)9781510611160;9781510611153

In-line lensless holography is considered with a random phase modulation at the object plane. The forward wavefront propagation is modelled using the Fourier transform with the angular spectrum transfer function. The multiple intensities (holograms) recorded by the sensor are random due to the random phase modulation and noisy with Poissonian noise distribution. It is shown by computational experiments that high-accuracy reconstructions can be achieved with resolution going up to the two thirds of the wavelength. With respect to the sensor pixel size it is a super-resolution with a factor of 32. The algorithm designed for optimal super-resolution phase/amplitude reconstruction from Poissonian data is based on the general methodology developed for phase retrieval with a pixel-wise resolution in v. Katkovnik, "Phase retrieval from noisy data based on sparse approximation of object phase and amplitude", http://***/similar to lasip/DDT/***.

关键词： Superresolution Phase retrieval image processing Noise in imaging systems Discrete optical signal processing

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Cloud-based Platform for Computer vision Applications 17

Cloud-based Platform for Computer Vision Applications

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International conference on Smart Digital Environment (ICSDE)

作者： Mahmoudi, Sidi Ahmed El Adoui, Mohammed Belarbi, Mohammed Amin Larhmam, Mohammed Amine Lecron, Fabian Univ Mons Mons Belgium

ISBN: (纸本)9781450352819

During last years, images and videos have become widely used in many daily applications. Indeed, they can come from cameras, smartphones, social networks of from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc.) in many situations such as airports, train stations, public areas, sport events, hospitals, etc. Thus, image and video processing algorithms have got increasing importance, they are required from various computer visions applications such as motion tracking, real time event detection, database (images and videos) indexation and medical computer aided diagnosis methods. In this paper, we propose a cloud platform that integrates the above-mentioned methods, which are generally developed with popular open source image and video processing libraries (OpenCv(1), OpenGL(2), ITK3, vTK4, etc.). Theses modules are automatically integrated and configured in the cloud application. Thus, the platform users will have access to different computer vision techniques without the need to download, install and configure the corresponding software. Each guest can select the required application, load its data and get the output results in a safe and simple way. The cloud platform can handle the variety of Operating Systems and programming languages (C++, Java, Python, etc.). Experimentations were conducted within two kinds of applications. The first represents medical methods such as image segmentation in MR images, 3D image reconstruction from 2D radiographs, left ventricle segmentation and tracking from 2D echocardiography. The second kind of applications is related to video processing such as face, people and cars tracking, and abnormal event detection in crowd videos.

关键词： Cloud computing image and video processing video surveillance medical image analysis

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Subject-Specific Convolutional Neural Networks for Accelerated Magnetic Resonance Imaging

Subject-Specific Convolutional Neural Networks for Accelerat...

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International Joint conference on Neural Networks

作者： Mehmet Akcakaya Steen Moeller Sebastian Weingartner Kamil Ugurbil Center for Magnetic Resonance Research University of Minnesota Minneapolis MN Computer Assisted Clinical Medicine University Hospital Mannheim Heidelberg University Heidelberg Germany

Magnetic Resonance Imaging (MRI) is one of the leading modalities for medical imaging, providing excellent softtissue contrast without exposure to ionizing radiation. Despite continuing advances in MRI, long scan times remain a major limitation in clinical applications. Parallel imaging is a technique for scan time acceleration in MRI, which utilizes the spatial variations in the reception profiles of receiver coil arrays to reconstruct images from undersampled Fourier space, i.e. k-space. One of the most commonly used parallel imaging techniques employs interpolation of missing k-space information by using linear shift-invariant convolutional kernels. These kernels are trained on a limited amount of autocalibration signal (ACS) for each scan. We propose a novel method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI), which uses scan-specific convolutional neural networks (CNNs) to perform improved k-space interpolation. Three-layer CNNs are trained using only scan-specific ACS data, alleviating the need for large training databases. The proposed method was tested in ultra-high resolution brain MRI and quantitative cardiac MRI, acquired with various acceleration rates. Improved noise resilience as compared to existing parallel imaging methods was observed for high acceleration rates or in the presence of low signal-to-noise ratio (SNR). Furthermore, RAKI successfully reconstructed images for quantitative cardiac MRI, even when using the same CNN across images with varying contrasts. These results indicate that RAKI achieves improved noise performance without overfitting to specific image contents, and offers great promise for improved acceleration in a wide range of MRI applications.

关键词： Magnetic resonance imaging image reconstruction Receivers Convolution Acceleration Kernel

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Predicting slice-to-volume transformation in presence of arbitrary subject motion 1

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20th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

作者： Hou, Benjamin Alansary, Amir McDonagh, Steven Davidson, Alice Rutherford, Mary Hajnal, Jo v. Rueckert, Daniel Glocker, Ben Kainz, Bernhard Biomedical Image Analysis Group Imperial College London London United Kingdom Division of Imaging Sciences and Biomedical Engineering Kings College London London United Kingdom

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotations and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7 mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography (CT) and X-Ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios. © Springer International Publishing AG 2017.

关键词： Motion compensation

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Brain Perfusion Imaging Using a reconstruction-of-Difference (RoD) Approach for Cone-Beam Computed Tomography

Brain Perfusion Imaging Using a Reconstruction-of-Difference...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Mow, M. Zbijewski, W. Sisniega, A. Xu, J. Dang, H. Stayman, J. W. Wang, X. Foos, D. H. Koliatsos, v. Aygun, N. Siewerdsen, J. H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Neurol Baltimore MD 21218 USA Johns Hopkins Univ Dept Radiol Baltimore MD USA Carestream Hlth Inc Rochester NY USA

ISBN: (数字)9781510607101

ISBN: (纸本)9781510607095;9781510607101

Purpose: To improve the timely detection and treatment of intracranial hemorrhage or ischemic stroke, recent efforts include the development of cone-beam CT (CBCT) systems for perfusion imaging and new approaches to estimate perfusion parameters despite slow rotation speeds compared to multi-detector CT (MDCT) systems. This work describes development of a brain perfusion CBCT method using a reconstruction of difference (RoD) approach to enable perfusion imaging on a newly developed CBCT head scanner prototype. Methods: A new reconstruction approach using RoD with a penalized-likelihood framework was developed to image the temporal dynamics of vascular enhancement. A digital perfusion simulation was developed to give a realistic representation of brain anatomy, artifacts, noise, scanner characteristics, and hemo-dynamic properties. This simulation includes a digital brain phantom, time-attenuation curves and noise parameters, a novel forward projection method for improved computational efficiency, and perfusion parameter calculation. Results: Our results show the feasibility of estimating perfusion parameters from a set of images reconstructed from slow scans, sparse data sets, and arc length scans as short as 60 degrees. The RoD framework significantly reduces noise and time-varying artifacts from inconsistent projections. Proper regularization and the use of overlapping reconstructed arcs can potentially further decrease bias and increase temporal resolution, respectively. Conclusions: A digital brain perfusion simulation with RoD imaging approach has been developed and supports the feasibility of using a CBCT head scanner for perfusion imaging. Future work will include testing with data acquired using a 3D-printed perfusion phantom currently and translation to preclinical and clinical studies.

关键词： Cone-beam CT Perfusion Imaging reconstruction of Difference

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Magnetic Resonance Fingerprinting - Principles and First Clinical Results 11

Magnetic Resonance Fingerprinting - Principles and First Cli...

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11th International conference on Measurement

作者： Mlynarik, v. Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy High Field MR Ctr Vienna Austria

ISBN: (纸本)9788097262914

The paper reviews a recently introduced experimental methodology for acquiring quantitative magnetic resonance data. Unlike standard imaging techniques, magnetic resonance fingerprinting (MRF) allows the reconstruction of several parameter maps, e.g., T1, T2 and proton density, from one scan. The method is based on the acquisition of a series of data in a pulse sequence, in which some relevant sequence parameters are varied in a predefined, regular or pseudo-random, way. The courses of signal intensities in the series for individual pixels (fingerprints) are then processed by pattern recognition against a library of the simulated courses. Several practical implementations of MRF for mapping relaxation times or other dynamic parameters of tissues are shown, and future perspectives of the MRF methodology are shortly outlined.

关键词： MR Fingerprinting Parametric Maps Pattern Recognition

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FEASIBILITY STUDY OF LOW-COST image-BASED HERITAGE DOCUMENTATION IN NEPAL

FEASIBILITY STUDY OF LOW-COST IMAGE-BASED HERITAGE DOCUMENTA...

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conference on 3D virtual reconstruction and visualization of Complex Architectures

作者： Dhonju, H. K. Xiao, W. Sarhosis, v. Mills, J. P. Wilkinson, S. Wang, Z. Thapa, L. Panday, U. S. Int Ctr Integrated Mt Dev ICIMOD Kathmandu Nepal Newcastle Univ Sch Civil Engn & Geosci Newcastle Upon Tyne NE1 7RU Tyne & Wear England Minist Land Reform & Management Cadastral Survey Div Survey Dept Kathmandu Nepal Kathmandu Univ Dept Civil & Geomat Engn Dhulikhel Nepal

Cultural heritage structural documentation is of great importance in terms of historical preservation, tourism, educational and spiritual values. Cultural heritage across the world, and in Nepal in particular, is at risk from various natural hazards (e.g. earthquakes, flooding, rainfall etc), poor maintenance and preservation, and even human destruction. This paper evaluates the feasibility of low-cost photogrammetric modelling cultural heritage sites, and explores the practicality of using photogrammetry in Nepal. The full pipeline of 3D modelling for heritage documentation and conservation, including visualisation, reconstruction, and structure analysis, is proposed. In addition, crowdsourcing is discussed as a method of data collection of growing prominence.

关键词： 3D modelling Crowd-source Heritage documentation Low-cost Photogrammetry

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Enhanced 3D segmentation techniques for reconstructed 3D medical volumes: Robust and Accurate Intelligent System 8

Enhanced 3D segmentation techniques for reconstructed 3D med...

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8th International conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 7th International conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Al-Zu'bi, Shadi Al-Ayyoub, Mahmoud Jararweh, Yaser Shehab, Mohammed A. Zaytoonah Univ Jordan Amman Sch Sci & Informat Technol Comp Sci Dept Amman Jordan Jordan Univ Sci & Technol Irbid Sch Sci & Informat Technol Comp Sci Dept Irbid Jordan

Medical images play an important role in treating a large number of ailments as they are integral and even indispensable to the diagnosis process of such ailments. Medical images come from different acquisition systems (such as PET, CT, MRI) and, in many situations, automated processing of these images can greatly aid physicians and make their jobs easier. In medical imaging and its applications, 2D segmentation (with its different approaches such as FCM, k-means, MRFM and NN) is the first step which is used to extract ROI. This helps in extracting ROI in each slice (2D medical image) separately regardless of its relation to the next and the previous slices. In this paper, a 3D model of FCM segmentation techniques is proposed to enhance the segmentation process and take in mind the overall 3D-volume as one testing data. (c) 2017 The Authors. Published by Elsevier B.v.

关键词： Medical imaging 3D image Processing Fuzzy C-Means volume reconstruction

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reconstruction of Sparse-view Tomography via Banded Matrices 10

Reconstruction of Sparse-View Tomography via Banded Matrices

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10th Indian conference on vision, Graphics and image Processing (ICvGIP)

作者： Prasad, T. Kumar, P. U. Praveen Sastry, C. S. Jampana, P. v. Indian Inst Technol Hyderabad Dept Math Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Chem Engn Hyderabad Telangana India

ISBN: (纸本)9783319681245;9783319681238

Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. The basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.

关键词： Compressed sensing Split-Bregman Computed tomography Least squares

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Full 3D Rotation Estimation in Scanning Electron Microscope

Full 3D Rotation Estimation in Scanning Electron Microscope

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS) / Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

作者： Kudryavtsev, Andrey v. Dembele, Sounkalo Piat, Nadine Univ Franche Comte CNRS Univ Bourgogne Franche Comte FEMTO ST InstDept AS2MENSMM 24 Rue Savary F-25000 Besancon France

ISBN: (纸本)9781538626825

Estimation of 3D object position is a crucial step for a variety of robotics and computer vision applications including 3D reconstruction and object manipulation. When working in microscale, new types of visual sensors are used such as Scanning Electron Microscope (SEM). Nowadays, micro- and nanomanipulation tasks, namely components assembly, are performed in teleoperated mode in most of the cases. Measuring object position and orientation is a crucial step towards automatic object handling. Current methods of pose estimation in SEM allow recovering full object movement using its computer-aided design (CAD) model. If the model is not known, most methods allow to estimate only in-plane translations and rotation around camera optical axis. In the literature, SEM is considered as a camera with parallel projection or an affine camera, which means image invariance to z-movement and bas-relief ambiguity. In this paper, authors address the problem of measuring full 3D rotation of the unknown scene for uncalibrated SEM without additional sensors. Rotations are estimated from image triplets by solving a spherical triangle from fundamental matrices only, without need of intrinsic calibration, allowing to avoid parallel projection ambiguities. The presented results, obtained in simulation and on real data, allow validating the proposed scheme.

关键词： Cameras Three-dimensional displays Geometry Solid modeling Two dimensional displays Scanning electron microscopy Estimation

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