500 research outputs found
Tomographic Reconstruction Methods for Decomposing Directional Components
Decomposition of tomographic reconstructions has many different practical
application. We propose two new reconstruction methods that combines the task
of tomographic reconstruction with object decomposition. We demonstrate these
reconstruction methods in the context of decomposing directional objects into
various directional components. Furthermore we propose a method for estimating
the main direction in a directional object, directly from the measured computed
tomography data. We demonstrate all the proposed methods on simulated and real
samples to show their practical applicability. The numerical tests show that
decomposition and reconstruction can combined to achieve a highly useful
fibre-crack decomposition
Wavelet transforms and their applications to MHD and plasma turbulence: a review
Wavelet analysis and compression tools are reviewed and different
applications to study MHD and plasma turbulence are presented. We introduce the
continuous and the orthogonal wavelet transform and detail several statistical
diagnostics based on the wavelet coefficients. We then show how to extract
coherent structures out of fully developed turbulent flows using wavelet-based
denoising. Finally some multiscale numerical simulation schemes using wavelets
are described. Several examples for analyzing, compressing and computing one,
two and three dimensional turbulent MHD or plasma flows are presented.Comment: Journal of Plasma Physics, 201
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation
Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise
Full waveform inversion with extrapolated low frequency data
The availability of low frequency data is an important factor in the success
of full waveform inversion (FWI) in the acoustic regime. The low frequencies
help determine the kinematically relevant, low-wavenumber components of the
velocity model, which are in turn needed to avoid convergence of FWI to
spurious local minima. However, acquiring data below 2 or 3 Hz from the field
is a challenging and expensive task. In this paper we explore the possibility
of synthesizing the low frequencies computationally from high-frequency data,
and use the resulting prediction of the missing data to seed the frequency
sweep of FWI. As a signal processing problem, bandwidth extension is a very
nonlinear and delicate operation. It requires a high-level interpretation of
bandlimited seismic records into individual events, each of which is
extrapolable to a lower (or higher) frequency band from the non-dispersive
nature of the wave propagation model. We propose to use the phase tracking
method for the event separation task. The fidelity of the resulting
extrapolation method is typically higher in phase than in amplitude. To
demonstrate the reliability of bandwidth extension in the context of FWI, we
first use the low frequencies in the extrapolated band as data substitute, in
order to create the low-wavenumber background velocity model, and then switch
to recorded data in the available band for the rest of the iterations. The
resulting method, EFWI for short, demonstrates surprising robustness to the
inaccuracies in the extrapolated low frequency data. With two synthetic
examples calibrated so that regular FWI needs to be initialized at 1 Hz to
avoid local minima, we demonstrate that FWI based on an extrapolated [1, 5] Hz
band, itself generated from data available in the [5, 15] Hz band, can produce
reasonable estimations of the low wavenumber velocity models
Penalized Weighted Least-Squares Image Reconstruction for Positron Emission Tomography
Presents an image reconstruction method for positron-emission tomography (PET) based on a penalized, weighted least-squares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, the author argues statistically that a least-squares objective function is as appropriate, if not more so, than the popular Poisson likelihood objective. The author proposes a simple data-based method for determining the weights that accounts for attenuation and detector efficiency. A nonnegative successive over-relaxation (+SOR) algorithm converges rapidly to the global minimum of the PWLS objective. Quantitative simulation results demonstrate that the bias/variance tradeoff of the PWLS+SOR method is comparable to the maximum-likelihood expectation-maximization (ML-EM) method (but with fewer iterations), and is improved relative to the conventional filtered backprojection (FBP) method. Qualitative results suggest that the streak artifacts common to the FBP method are nearly eliminated by the PWLS+SOR method, and indicate that the proposed method for weighting the measurements is a significant factor in the improvement over FBP.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85851/1/Fessler105.pd
Core Imaging Library - Part II:multichannel reconstruction for dynamic and spectral tomography
The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL’s capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots
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