Spherically symmetric volume elements as basis functions for image reconstructions in computed laminography

Trampert, Patrick; Vogelgesang, Jonas; Schorr, Christian; Maisl, Michael; Bogachev, Sviatoslav; Marniok, Nico; Louis, Alfred K.; Dahmen, Tim; Slusallek, Philipp

doi:10.3233/xst-16230

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…In layman's terms, Gaussian filtering is the process of weighted averaging the entire image. The value of each pixel is obtained by weighted average of its own and other pixel values in the neighborhood [24]. The specific operation of Gaussian filtering is to scan each pixel in the image with a template (or convolution, mask), and use the weighted average gray value of the pixels in the neighborhood determined by the template to replace the value of the center pixel of the template.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Pei

Wang

et al. 2022

Mobile Information Systems

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With the development of non-destructive testing technology and computer technology, wood non-destructive testing technology will develop towards intelligence and automation. The primary condition for development is to be able to carry out the testing of various physical properties of wood without destroying the wood itself. Wood defects refer to abnormal wood structure. Its existence will affect the quality of wood, change the normal performance of wood, and reduce the utilization rate and use value of wood. The purpose of this article is to try to find an effective detection method without damaging the original structure of the wood. It can accurately and quickly determine the defect information on the wood surface. This research mainly discusses the visual analysis of wood internal defect detection based on tomographic image reconstruction algorithm. According to the analysis of the planks to be tested, this paper determines the structural characteristics, the types of defects to be tested, and the classification standards. To analyze the principle of machine vision imaging, this paper designs a hardware experiment system for wood board imaging, by observing the collected Biyun Temple building wood images and summarizing the Biyun Temple building wood defects, surface texture features, and various appearance features in the image. Based on digital image processing technology, this paper designs a complete set of real-time timber classification and detection algorithms. The algorithm realizes the extraction of Biyun Temple’s architectural wood region, texture region extraction, and the comprehensive feature vector extraction of Biyun Temple’s architectural wood. In the image preprocessing stage, the color image is converted into a grayscale image through the grayscale processing of the image. Through the equalization processing of the histogram, the defect features in the defect image are highlighted. Through image smoothing and denoising processing, the noise points that may exist in the image are removed. The signal-to-noise ratio of the Marching Cube algorithm is maintained at a relatively high level under different noise conditions, which is about 3-4db higher than the Gaussian method with OPED. This research is helpful for the continuation and inheritance of ancient architectural attainments.

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Section: Mobile Information Systemsmentioning

confidence: 99%

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Pei

Wang

et al. 2022

Mobile Information Systems

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“…Considering that the k‐space spectral profiles of lipid and metabolite signals are concentrated at the center of k‐space with different k‐space spectral widths because of their different spatial confinements, their profiles could be modeled as a set of Gaussian functions. Gaussian‐shaped spatial elements have been used previously in tomographic reconstructions, 18‐21 where they were called “blobs.” The Gaussian function is favorable because it has no sidelobes in either spatial or k‐space domains, and the apodization preserves the rank of the matrix. We thus define the elements of G as

G_{nm} = w_{m} \underset{\sum}{false} e^{(‐ r_{m, ℓ}^{2} / (2 σ_{R}^{2}))} e^{‐ j 2 π k_{n} \cdot r_{ℓ}}

where

σ_{R}

is a radius parameter expressed as standard deviation, and

r_{m, ℓ}

the distance between the m th and

ℓ

th image elements.…”

Section: Theorymentioning

confidence: 99%

Method for fast lipid reconstruction and removal processing in¹H MRSI of the brain

Adany

Choi

Lee

2021

Magnetic Resonance in Med

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Purpose To develop a new rapid spatial filtering method for lipid removal, fast lipid reconstruction and removal processing (FLIP), which selectively isolates and removes interfering lipid signals from outside the brain in a full‐FOV 2D MRSI and whole‐brain 3D echo planar spectroscopic imaging (EPSI). Theory and Methods FLIP uses regularized least‐squares regression based on spatial prior information from MRI to selectively remove lipid signals originating from the scalp and measure the brain metabolite signals with minimum cross contamination. FLIP is a noniterative approach, thus allowing a rapid processing speed, and uses only spatial information without any spectral priors. The performance of FLIP was compared with the Papoulis‐Gerchberg algorithm (PGA), Hankel singular value decomposition (HSVD), and fast image reconstruction with L2 regularization (L2). Results FLIP in both 2D and 3D MRSI resulted in consistent metabolite quantification in a greater number of voxels with less concentration variation than other algorithms, demonstrating effective and robust lipid‐removal performance. The percentage of voxels that met quality criteria with FLIP, PGA, HSVD, and L2 processing were 90%, 57%, 29%, and 42% in 2D MRSI, and 80%, 75%, 76%, and 74% in 3D EPSI, respectively. The quantification results of full‐FOV MRSI using FLIP were comparable to those of volume‐localized MRSI, while allowing significantly increased spatial coverage. FLIP performed the fastest in 2D MRSI. Conclusion FLIP is a new lipid‐removal algorithm that promises fast and effective lipid removal with improved volume coverage in MRSI.

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“…However, the analysis of thin objects perpendicular to the source could diminish significantly share and thus be suitable for our proposed model. For different reasons, the study of thin objects is at the centre of computed laminography [31].…”

Section: Contour Extraction On 3d Csi: Application On Simulated Datamentioning

confidence: 99%

3D Compton scattering imaging and contour reconstruction for a class of Radon transforms

Rigaud

Hahn

2018

Inverse Problems

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Compton scattering imaging is a nascent concept arising from the current development of high-sensitive energy detectors and is devoted to exploit the scattering radiation to image the electron density of the studied medium. Such detectors are able to collect incoming photons in terms of energy. This paper introduces potential 3D modalities in Compton scattering imaging (CSI). The associated measured data are modeled using a class of generalized Radon transforms. The study of this class of operators leads to build a filtered backprojection kind algorithm preserving the contours of the sought-for function and offering a fast approach to partially solve the associated inverse problems. Simulation results including Poisson noise demonstrate the potential of this new imaging concept as well as the proposed image reconstruction approach.

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Spherically symmetric volume elements as basis functions for image reconstructions in computed laminography

Cited by 5 publications

References 25 publications

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Method for fast lipid reconstruction and removal processing in¹H MRSI of the brain

3D Compton scattering imaging and contour reconstruction for a class of Radon transforms

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Spherically symmetric volume elements as basis functions for image reconstructions in computed laminography

Cited by 5 publications

References 25 publications

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm

Method for fast lipid reconstruction and removal processing in1H MRSI of the brain

3D Compton scattering imaging and contour reconstruction for a class of Radon transforms

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Method for fast lipid reconstruction and removal processing in¹H MRSI of the brain