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

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FAST FAN/PARALLEL BEAM CS-BASED LOW-DOSE CT reconstruction

FAST FAN/PARALLEL BEAM CS-BASED LOW-DOSE CT RECONSTRUCTION

引用

IEEE International conference on Acoustics, Speech, and Signal Processing

作者： SayedMasoud Hashemi Soosan Beheshti Patrick R. Gill Narinder S. Paul Richard S. C. Cobbold Institute of Biomaterials & Biomedical Eng. University of Toronto Toronto Canada Electrical Engineering Department Ryerson University Toronto Canada Rambus Labs Rambus Inc. Sunnyvale California USA

ISBN: (纸本)9781479903573

Low dose X-ray Computed Tomography (CT) is clinically desired to reduce the risk of cancer caused by X-ray radiation. Compressed Sensing (CS), which allows images to be formed from incomplete data, enables large dose reduction to be achieved. Though this remains to be clinically unrealized due to excessive computation times. In this paper we demonstrate a fast, complete CS-based l_2-Tv minimizing CT reconstruction method applicable to both parallel and fan beam geometries to recover high quality images from highly undersampled (thus low-dose) data. We apply the fast pseudo-polar Fourier algorithm and the Central Slice Theorem to reduce the computation time of CS recovery. On a typical desktop computer, we are able to reconstruct a 512×512 CT image in approximately 30 seconds: a clinically-significant speedup compared to the many hours required by previous CS methods.

关键词： Computed Tomography Central Slice Theorem Pseudo-Polar Fourier Transform Compressed Sensing X-Ray Computed Tomography Compressed sensing desktop computers computational time reconstructions X-rays image quality CONTAINMENT SPRAY carbon monosulfide conditioned stimulus Cesium images BEAM Beams and girders

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Noise Estimation in Magnetic Resonance SENSE Reconstructed data

Noise Estimation in Magnetic Resonance SENSE Reconstructed D...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Santiago Aja-Fernandez Gonzalo vegas-Sanchez-Ferrero Rodrigo de Luis-Garcia Antonio Tristan-vega LPI ETSI Telecomunicacion Universidad de Valladolid

ISBN: (纸本)9781457702150

Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. SENSE is one of the most popular reconstruction methods proposed in order to suppress the artifacts created by this subsampling. However, the SENSE reconstruction process yields to a variance of noise value which is dependent on the position within the image. Hence, the traditional noise estimation methods based on a single noise level for the whole image fail. Accordingly, we propose a novel method to recover the complete spatial pattern of the variance of noise in SENSE reconstructed images up from the sensitivity maps of each receiver coil. Our method fits applications in statistical image processing tasks such as image denoising.

关键词： Noise estimation Parallel imaging SENSE Magnetic Resonance

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Super Resolution image Created Automatically from Mosaics

Super Resolution Image Created Automatically from Mosaics

引用

International conference on Computer Sciences and Applications (CSA)

作者： Leandro L. De Almeida Francisco A. Da Silva Maria Stela v. De Paiva Lúcio André de C. Jorge Univ. of Western Sao Paulo (Unoeste) Presidente Prudente Brazil Empresa Brasileira de Pesquisa Agropecuaria Brasilia DF BR

In order to extract information from the images, both the resolution and its application must be observed. This resolution depends basically on the physical characteristics of the sensor responsible for capturing these images, i.e. the disposition of the lenses, radiometric resolution, positioning, dimensions of the same, which determines the pixel size acquired by the device, among other technical factors. The objective of this work is to develop efficient algorithms, robust and automated fusion image frames for mosaics and super resolution from the selection of a region in the mosaic. image registration is a fundamental step in combining several images that make up the scene. Our research is based on the determination and extraction of characteristics defined by the SIFT algorithm (detector / descriptor) and RANSAC. validations real data show the effectiveness of our techniques proposed. The difference of this work is the way to get the matching and merging of images because it occurs dynamically between elements common images that are stored in a sparse matrix.

关键词： image resolution Algorithm design and analysis image registration Sparse matrices data structures image reconstruction Equations

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Imaging and Estimation of Human Abdominal Fat by Electrical Impedance Tomography Using Multiple voltage Measurement Patterns

Imaging and Estimation of Human Abdominal Fat by Electrical ...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Tohru F. Yamaguchi Mitsuhiro Katashima Li-qun Wang Shinya Kuriki Health Care Food Research Laboratories Kao Corporation The Research Institute for Science and Technology Tokyo Denki University

ISBN: (纸本)9781457702150

A measuring device for human abdominal fat from the conductivity image derived by electrical impedance tomography (EIT) is rarely found. This study was aimed to reconstruct precise conductivity images from multiple voltage measurements in different patterns of the combination of current and voltage electrodes. We examined two voltage measuring patterns using electrodes located at upper and lower levels around the abdomen of a subject. In the experiment, after 1024 voltage data were taken from one specified voltage measurement pattern, another 1024 data were also taken continuously using another pattern. The reconstruction of conductivity image was made using entire data. As a result, the tomography image was improved compared with the image obtained from single voltage measurement pattern. We then obtained the histogram of the conductivities and estimated the area of abdominal fat. The present method using multiple voltage measurement patterns would be effective, if the measuring time can be much reduced through future modification of the tomography device.

关键词： voltage measurement Tomography impedance Abdominal Fat images conductivity imagery (Psychotherapy) measuring time image histograms Electrodes

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WATERSHED MERGE FOREST CLASSIFICATION FOR ELECTRON MICROSCOPY image STACK SEGMENTATION

WATERSHED MERGE FOREST CLASSIFICATION FOR ELECTRON MICROSCOP...

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IEEE International conference on image Processing

作者： Ting Liu Mojtaba Seyedhosseini Mark Ellisman Tolga Tasdizen Scientific Computing and Imaging Institute University of Utah National Center for Microscopy and Imaging Research University of California San Diego

ISBN: (纸本)9781479923427

Automated electron microscopy (EM) image analysis techniques can be tremendously helpful for connectomics research. In this paper, we extend our previous work [1] and propose a fully automatic method to utilize inter-section information for intra-section neuron segmentation of EM image stacks. A watershed merge forest is built via the watershed transform with each tree representing the region merging hierarchy of one 2D section in the stack. A section classifier is learned to identify the most likely region correspondence between adjacent sections. The inter-section information from such correspondence is incorporated to update the potentials of tree nodes. We resolve the merge forest using these potentials together with consistency constraints to acquire the final segmentation of the whole stack. We demonstrate that our method leads to notable segmentation accuracy improvement by experimenting with two types of EM image data sets.

关键词： Machine learning neuron segmentation watershed random forest neural circuit reconstruction watersheds watershed Furnace conveyors Electron Microscope tree nodes Watershed transform images Artificial Intelligence Machine Learning Correspondence Connectome segmentation Segmentation

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Scalable Uncertainty Management: 7th International conference, SUM 2013, Washington, DC, USA, September 16-18, 2013. Proceedings 1

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丛书名： Lecture Notes in Computer Science 8078

2013年

作者： Anthony Hunter (auth.), Weiru Liu, v. S. Subrahmanian, Jef Wijsen (eds.)

ISBN: (数字)9783642403811

ISBN: (纸本)9783642403804;9783642403811

This book constitutes the refereed proceedings of the 7th International conference on Scalable Uncertainty Management, SUM 2013, held in Washington, DC, USA, in September 2013. The 26 revised full papers and 3 revised short papers were carefully reviewed and selected from 57 submissions. The papers cover topics in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information including applications in decision support systems, machine learning, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.

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Emission profile reconstruction of edge plasma of Aditya Tokamak

Emission profile reconstruction of edge plasma of Aditya Tok...

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International conference on Communication and Signal Processing

作者： Bhumika Makwana Manojkumar Gupra Hiren Mewada Charotar Institute of Technology Changa Ta Anand India. the institute of Plasma Research at Ghandhinagr Gujarat India. the Charotar Institute of Technology Changa Ta Anand India.

ISBN: (纸本)9781467348652

Nuclear fusion will be one of the promising ways to generate electrical power in the next century. This source should be a safe and clean source of electricity for which fuels are abundant. Most current research into nuclear fusion is directed towards magnetic confinement of extremely hot plasma in a device called Tokamak. Before Fusion reactors become operational, still many problems need to be solved, both in fundamental and technological problems from which plasma wall interaction and magnetic field fluctuation are some of the major ones. To address these problems Aditya Tokamak in the Institute for plasma research is equipped with several high resolution diagnostics. One of them visible light Tomography setup is described in this paper. This system is suitable for studying fluctuations and to determine the local density of species that give rise to radiation which is important for plasma wall interaction. This paper describes use of tomography diagnostic to resolve the local emission of visible light in plasma from line integrated measurements in one poloidal cross section. Multiplicative algebraic reconstruction technique is developed to reconstruct the H_a emission profile. A code is written in MATLAB for extracting the informative projection data from the raw data file obtained from Aditya Tokamak. MART code written in MATLAB has flexibility to change the various parameters like geometrical parameters, relaxation factor and pixel size.

关键词： image reconstruction Multiplicative algebraic reconstruction technique Plasma diagnostics Tomography

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Transmission-less brain TOF PET imaging using MLACF

Transmission-less brain TOF PET imaging using MLACF

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： v. Y. Panin H. Bal M. Defrise C. Hayden M.E. Casey Siemens Healthcare Molecular Imaging Knoxville TN USA Department of Nuclear Medicine University Hospital UZ-VUB Brussels Belgium

Recent theoretical investigations concluded that both activity and attenuation distributions can be obtained from PET emission TOF data alone up to the knowledge of the sinogram scaling parameter. Attenuation is recovered in the form of attenuation factors. This allows for development of a fast iterative algorithm, referred to as ML-ACF, where iterative reconstruction is performed with respect to the activity image. Attenuation is a by-product of the estimation of activity. In general, the estimation of attenuation from PET data alone is not currently necessary due to the diagnostic value of PET-CT scanning. Nevertheless, it is desirable to limit patient CT doses. CT-less PET brain imaging is currently being pursued by manufacturers. This transmission-less brain imaging uses the assumption of a uniform attenuation map. The boundary can be derived from the processing of the activity image and reconstructed without attenuation correction. In this paper we enrich this approach by exploiting this uniform attenuation map as a starting condition for MLACF iterations. The scatter correction and the proper scaling of the activity image are based on this information. The head, especially the slices close to the top of the head, is relatively small compared to account the current scanner's time resolution. Therefore it is not obvious a priori that TOF information can be exploited for brain imaging. In this paper we demonstrate that the brain image attenuation can be reconstructed by exploiting just two TOF bins, with the TOF bin size currently used on the Siemens mCT scanner. Preliminary investigations showed that the head activity image will be improved by MLACF compared to the standard CT-less processing based on a uniform attenuation.

关键词： Attenuation image reconstruction Computed tomography Estimation Head Brain

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Handling Noise in Single image Deblurring using Directional Filters

Handling Noise in Single Image Deblurring using Directional ...

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IEEE conference on Computer vision and Pattern Recognition

作者： Lin Zhong Sunghyun Cho Dimitris Metaxas Sylvain Paris Jue Wang Rutgers University Adobe Research

ISBN: (纸本)9781467364102

State-of-the-art single image deblurring techniques are sensitive to image noise. Even a small amount of noise, which is inevitable in low-light conditions, can degrade the quality of blur kernel estimation dramatically. The recent approach of Tai and Lin [17] tries to iteratively denoise and deblur a blurry and noisy image. However, as we show in this work, directly applying image denoising methods often partially damages the blur information that is extracted from the input image, leading to biased kernel estimation. We propose a new method for handling noise in blind image deconvolution based on new theoretical and practical insights. Our key observation is that applying a directional low-pass filter to the input image greatly reduces the noise level, while preserving the blur information in the orthogonal direction to the filter. Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image. Finally, we reconstruct the blur kernel using inverse Radon transform. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than previous approaches on blurry and noisy images.~1

关键词： blurry noisy image estimates Noisy image image reconstruction directional filter Noise Radon transforms image images imagery (Psychotherapy) Seeds Noise levels

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LEARNING A TREE-STRUCTURED DICTIONARY FOR EFFICIENT image REPRESENTATION WITH ADAPTIvE SPARSE CODING

LEARNING A TREE-STRUCTURED DICTIONARY FOR EFFICIENT IMAGE RE...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Jeremy Aghaei Mazaheri Christine Guillemot Claude habit INRIA Rennes Campus universitaire de Beaulieu 35042 Rennes Cedex France

ISBN: (纸本)9781479903573

We introduce a new method, called Tree K-SvD, to learn a tree-structured dictionary for sparse representations, as well as a new adaptive sparse coding method, in a context of image compression. Each dictionary at a level in the tree is learned from residuals from the previous level with the K-SvD method. The tree-structured dictionary allows efficient search of the atoms along the tree as well as efficient coding of their indices. Besides, it is scalable in the sense that it can be used, once learned, for several sparsity constraints. We show experimentally on face images that, for a high sparsity, Tree K-SvD offers better rate-distortion performances than state-of-the-art "flat" dictionaries learned by K-SvD or Sparse K-SvD, or than the predetermined overcomplete DCT dictionary. We also show that our adaptive sparse coding method, used on a tree-structured dictionary to adapt the sparsity per level, improves the quality of reconstruction.

关键词： Dictionary learning tree-structured dictionary sparse coding sparse representations image coding Sparse coding Dictionaries as Topic image coding sparse representation image compression data compression Trees images

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