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conference on Computational Imaging v

作者： Zheng, Yibin Li, Heng Wang, Jiong Stolin, Alexander v. Pole, Joe Williams, Mark B. Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22903 USA Univ Texas Houston MD Anderson Canc Ctr Dept Radiat Phys Houston TX 77030 USA Univ Virginia Dept Radiol Charlottesville VA 22903 USA

ISBN: (纸本)9780819466112

Pinhole imaging is a promising approach for high spatial resolution single gamma emission imaging in situations when the required field of view (FOv) is small, as is the case for small animal imaging. However, all pinhole collimators exhibit steep decrease in sensitivity with increasing angle of incidence from the pinhole axis. This in turn degrades the reconstruction images, and requires higher dose of radiotracer. We developed a novel pinhole SPECT system for small animal imaging which uses two opposing and offset small cone-angle square pinholes, each looking at half of the FOv. This design allows the pinholes to be placed closer to the object and greatly increases detection efficiency and spatial resolution, while not requiring larger size detectors. Iterative image reconstruction algorithms for this system have been developed. Preliminary experimental data have demonstrated marked improvement in contrast and spatial resolution.

关键词： Single photon emission computed tomography Sensors Imaging systems Spatial resolution Cameras Point spread functions reconstruction algorithms Signal attenuation image restoration Preclinical imaging

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from tomographic reconstruction to automatic text recognition - the next frontier task for the artificial intelligence 15

From tomographic reconstruction to automatic text recognitio...

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15th International conference on Machine vision (ICMv)

作者： Polevoy, D. v. Kulagin, P. A. Ingacheva, A. S. Soldatova, Zh. v. Chukalina, M. v. Nikolaev, D. P. Arlazarov, v. v. Smart Engines Serv LLC Moscow 117312 Russia RAS FSRC Comp Sci & Control Moscow 119333 Russia RAS Inst Informat Transmiss Problems Kharkevich Inst Moscow 127051 Russia Natl Univ Sci & Technol MISIS Moscow 119049 Russia Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia RAS FFSRC Crystallog & Photon Moscow 119333 Russia

ISBN: (纸本)9781510666184;9781510666191

virtual unrolling or unfolding, digital unwrapping, flattening or unfurling - all these terms are used to describe the process of surface straightening of a tomographically reconstructed digital object. For many objects of historical heritage, tomography is the only way to obtain a hidden image of the original object without its destruction. Digital flattening is no longer considered a unique methodology. It being applied by many research group, but AI-based methods are used insignificantly in such projects, despite the amazing success of AI in computer vision, in particular optical text recognition. It can be explained by the fact that the success of AI depends on large, broad and high quality datasets, but there are very few published CT-based datasets relevant to the task of digital flattening. Accumulation of a sufficient amount of data necessary for training models is a key point for the next technological breakthrough. In this paper, we present open and cumulative dataset CT-OCR-2022. dataset includes 6 packages data for different model objects that help to enrich tomographic solutions and to train machine learning models. Each package contains optically scann ed image of model objects, 400 measured X-ray projections, 2687 CT- reconstructed cross-sections of 3D reconstructed image, segmentation markups. We believe that CT-OCR- 2022 dataset will serve as a benchmark for reconstructed object digital flattening and recognition systems, and that it will prove invaluable for advancement of the field of CT-reconstruction, symbols analysis and recognition. The data presented are openly available in Zenodo at doi:10.5281/zenodo.7123495 and linked repositories.

关键词： virtual unrolling virtual unwrapping digital unfolding computational flattening computed tomography non-destructive analysis open dataset

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Quantitative reconstruction of PIXE-tomography data for thin samples using GUPIX X-ray emission yields

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS 2015年 348卷 92-99页

作者： Michelet, C. Barberet, Ph Deves, G. Bouguelmouna, B. Bourret, S. Delville, M. -H. Le Trequesser, Q. Gordillo, N. Beasley, D. G. Marques, A. C. Farau, R. Toko, B. R. Campbell, J. Maxwell, J. Motetto, Ph Seznec, H. Univ Bordeaux CENBG UMR 5797 F-33170 Gradignan France CNRS IN2P3 CENBG UMR 5797 F-33170 Gradignan France Univ Bordeaux ICMCB UPR9048 CNRS F-33608 Pessac France UCL Dept Med Phys & Bioengn CMIC London WC1E 6BT England Univ Lisbon IST IPFN P-2685066 Bobadela Lrs Portugal Univ Guelph Guelph Waterloo Phys Inst Guelph ON N1G 2W1 Canada

We present here a new development of the TomoRebuild software package, to perform quantitative Particle Induced X-ray Emission Tomography (PIXET) reconstruction. X-ray yields are obtained from the GUPIX code. The GUPIX data base is available for protons up to 5 Mev and also in the 20-100 Mev energy range, deuterons up to 6 Mev, He-3 and alphas up to 12 Mev. In this version, X-ray yields are calculated for thin samples, i.e. without simulating X-ray attenuation. PIXET data reconstruction is kept as long as possible independent from Scanning Transmission Ion Microscopy Tomography (STIMT). In this way, the local mass distribution (in g/cm(3)) of each X-ray emitting element is reconstructed in all voxels of the analyzed volume, only from PIXET data, without the need of associated STIMT data. Only the very last step of data analysis requires STIMT data, in order to normalize PIXET data to obtain concentration distributions, in terms of normalized mass fractions (in mu g/g). For this, a noise correction procedure has been designed in imageJ. Moreover sinogram or image misalignment can be corrected, as well as the difference in beam size between the two experiments. The main features of the TomoRebuild code, user friendly design and modular C++ implementation, were kept. The software package is portable and can run on Windows and Linux operating systems. An optional user-friendly graphic interface was designed in Java, as a plugin for the imageJ graphic software package. reconstruction example'S are presented from biological specimens of Caenorhabditis elegans - a small nematode constituting a reference model for biology studies. The reconstruction results are compared between the different codes TomoRebuild, DISRA and JPDCET, and different reconstruction methods: Filtered BackProjection (FBP) and Maximum Likelihood Expectation Maximization (MLEM). (C) 2014 Elsevier B.v. All rights reserved.

关键词： PIXE tomography Filtered backprojection MLEM imageJ Caenorhabditis elegans

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A new modality of bidimensional Compton camera 37

A new modality of bidimensional Compton camera

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37th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Cebeiro, Javier Lebailly, Quentin Morvidone, Marcela A. Nguyen, Mai K. Univ Nacl San Martin Ctr Matemat Aplicada Ave 25 Mayo & FranciaB1650HMP Buenos Aires DF Argentina ENSEA Univ Cergy Pontoise ETIS CNRS UMR 8051 F-95014 Cergy Pontoise France

ISBN: (纸本)9781424492701

The concept of Compton camera originates from the need to improve sensitivity in Single Photon Emission Imaging, which uses hole collimators. It advocates electronic collimation which registers radiation emitted by the radiating object and scattered by a scattering detector placed before an absorption detector. The data consists of three dimensional conical projections of the activity density of a radio-tracer. An analytic reconstruction method of this density is presently not available. To evaluate its imaging power, we consider a particular two-dimensional Compton camera in which the collected data consists of the set of integrals of the density on rotating v-lines. In this communication, we present simulation results obtained from an algebraic reconstruction technique, which illustrate and validate the imaging capability of this Compton camera modality.

关键词： Compton effect iterative methods single photon emission computed tomography absorption detector activity density algebraic reconstruction technique bidimensional Compton camera modality electronic collimation hole collimators radiating object radiotracer rotating v-lines scattering detector single photon emission imaging three-dimensional conical projections two-dimensional Compton camera Cameras Detectors image reconstruction Photonics Scattering Subspace constraints

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Dictionary Learning from incomplete data for Efficient image Restoration

Dictionary Learning from Incomplete Data for Efficient Image...

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European Signal Processing conference

作者： valeriya Naumova Karin Schnass Section for Computing and Software Simula Research Laboratory Fornebu Norway Department of Mathematics University of Innsbruck Innsbruck Austria

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： Dictionaries Signal processing algorithms Machine learning Manganese image color analysis image reconstruction Matching pursuit algorithms

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FPGA Implementation of Satellite image Fusion Using Wavelet Substitution Method

FPGA Implementation of Satellite Image Fusion Using Wavelet ...

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Science and Information conference (SAI)

作者： Mamatha, G. Sumalatha, v. Lakshmaiah, M. v. JNTUACEA Dept ECE Ananthapuramu India

ISBN: (纸本)9781479985470

image fusion is a process of merging the relevant information from a set of images into a single image. The goal of image fusion in remote sensing is to create new images that contain both low spatial resolution multispectral data (color information) and high spatial resolution panchromatic data (details).. The choice of appropriate wavelet filters for the design and implementation on FPGA, a detailed analysis has been carried out in MATLAB Simulink R2010b software using averaging, additive and substitutive fusion rules. In this paper substitutive rule using the Haar, Daubechies 3 (db3) and Cohen Daubechies Feauveau (CDF) 9/7 filters are used for image decomposition and reconstruction. CDF 9/7 is found to be the best filter and is chosen for FPGA implementation. Single level 2D-DWT based image fusion has been performed using substitutive method and then the hardware software co-simulation design has been synthesized in Xilinx ISE 13.1 and implemented on ML605 virtex 6 FPGA kit. from the results, it is observed that the design consumes a total power of 4.349W and operates at a maximum frequency of 849.618MHz.

关键词： image Fusion image Resampling image Registration DWT FPGA

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Scanning of logs with linear cone-beam tomography

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2003年第1-3期41卷 45-62页

作者： Seger, MM Danielsson, PE Linkoping Univ Dept Elect Engn Comp Vis Lab SE-58183 Linkoping Sweden

This paper reports an ongoing feasibility study in industrial X-ray tomography. Full volume scanning of logs may have a large potential in terms of increased sales value. On the other hand, there is no technology commercially available today which can meet the requirements for speed. In conventional medical 3D-tomography, the source-detector system rotates around a slowly translating patient. We do not believe such an arrangement is able to reach our speed requirements. Instead, we have been simulating an arrangement with two fixed source-detector systems rotated 90degrees relative to each other. Each system consists of an X-ray source and a 2D detector. The logs are to be translated through this arrangement at relatively high speed (2-3 m/s) lengthwise on a conveyor belt, while cone-beam projections are acquired by each of the source-detector systems. The 2D-detectors are composed of a number of ID-detectors side by side. For cost reasons we will try to limit the number of rows to nine, forming a 2D-array of 256 x 9 detector elements. Due to the unconventional scanning geometry, we have developed a new reconstruction algorithm, which also takes special care to avoid many artifacts due to sparse and/or missing data. Experiments (still simulated), performed on voxelized CT data, indicate that the knots are reconstructed with sufficient accuracy to allow for quantitative optimization. Due to the missing data, however, heartwood can barely be distinguished from sapwood, but his fact does not seem to compromise the subsequent knot detection. Note that this paper only describes the reconstruction part of the simulated experiments. To be complete, the feasibility study should also include segmentation, which is outside the scope of this paper. (C) 2003 Elsevier Science B.v. All rights reserved.

关键词： x-ray log scanning 3D linear cone-beam tomography incomplete data

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A synthetic sensor/image simulation tool to support the landsat data continuity mission (LDCM)

A synthetic sensor/image simulation tool to support the land...

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American Society for Photogrammetry and Remote Sensing Annual conference 2010: Opportunities for Emerging Geospatial Technologies

作者： Schott, John R. Raqueno, Rolando v. Raqueno, Nina G. Brown, Scott D. Digital Imaging and Remote Sensing Laboratory Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester NY 14623 United States

ISBN: (纸本)9781617389160

The next generation of Landsat sensors will incorporate many changes from the existing whiskbroom designs. In particular, the Operational Land imager (OLI) incorporates a multispectral pushbroom design which has different bands imaging the same spot on the Earth at slightly different angles and times. This requires more involved knowledge of the sensor and the scene, as input to more complex processing algorithms, to form spectrally registered images. In addition, the thermal data for the LDCM will come from a separate Thermal Infrared Sensing Instrument (TIRS) flown on the same platform. Thus, there are additional complexities associated with registering the OLI and TIRS data to each other and the ground. This paper presents a synthetic sensor/scene model designed to generate synthetic OLI and TIRS data sets to support development and testing of LDCM image reconstruction algorithms. This is part of a larger effort to use the synthetic sensor/scene models to support a range of LDCM development and monitoring activities. Methods for generation of a synthetic scene incorporating digital elevation models, spatial texture from high resolution satellite data, spectral variability from Hyperion satellite data, and atmospheric effects from the MODTRAN radiative transfer code are presented. Within the Digital Imaging and Remote Sensing image Generation model, OLI and TIRS sensor models are being created that can be "flown" over the synthetic scene to form synthetic images that mimic the geometric sampling of the actual sensors. The synthetic images will be used to support the Landsat image quality assessment program with an initial focus on image reconstruction algorithms.

关键词： image reconstruction

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Fast reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression 18th

Fast Reconstruction of Accelerated Dynamic MRI Using Manifol...

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18th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Bhatia, Kanwal K. Caballero, Jose Price, Anthony N. Sun, Ying Hajnal, Jo v. Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Biomed Image Anal Grp London SW7 2AZ England Kings Coll London Div Imaging Sci & Biomed Engn London WC2R 2LS England Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9783319245744;9783319245737

We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.

关键词： Compressed sensing

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Phase imaging from noisy interferometric observations via sparse coding of phase and amplitude 1

Phase imaging from noisy interferometric observations via sp...

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1st International Scientific School on Methods of Digital image Processing in Optics and Photonics

作者： Katkovnik, v. Tampere Univ Technol Dept Signal Proc Tampere 33720 Finland

Phase-shifting interferometry is a coherent optical method that combines high accuracy with high measurement speeds. However, despite its advantageous properties, the inference of the object amplitude and the phase, herein termed wavefront reconstruction is not a trivial task owing to, namely, the Poissonian noise associated with the measurement process and to the 27 phase periodicity of the observation mechanism. In this paper, we formulate the wavefront reconstruction (phase and amplitude) as an inverse problem where the amplitude and the absolute phase are assumed to admit sparse linear representations in suitable sparsifying transforms. We introduce the sparse phase and amplitude reconstruction (SPAR) algorithm. SPAR takes into full consideration the Poissonian (photon counting) measurements and uses data adaptive BM3D frames as a sparse representation for amplitude and absolute phase.

关键词： Inverse problems

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