CT Reconstruction From Parallel and Fan-Beam Projections by a 2-D Discrete Radon Transform

Averbuch, Amir; Sedelnikov, I.; Shkolnisky, Yoel

doi:10.1109/tip.2011.2164416

Cited by 35 publications

(14 citation statements)

References 11 publications

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“…Many recent researchers have shown the interest of Radon transform such as image processing [10] and pattern recognition [11,12]. Using this method, the image is mapped with lines into a subspace of intensities.…”

Section: Proposed Methodsmentioning

confidence: 99%

Texture matching using local and global descriptor

Hamouchene

Aouat

2014

2014 5th European Workshop on Visual Information Processing (EUVIP)

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Image processing is one of the important areas of research, which provides efficient solutions to many real and industrial problems. Texture analysis is the most important field in image processing because all objects are textured in real world. In this work, we propose a new texture segmentation method based on the dynamic segmentation architecture. This architecture decomposes the image into blocks with the same size of a main window. After that, the size of the main window is reduced and the same process is applied to extract other blocks with different size. This process is repeated until the size of the main window reaches a minimum size. Neuroscience studies said that the human brain combines between Local and Global features to recognize objects. Based on these studies, the feature extraction step of the proposed segmentation method is based on the combination between two methods. The first method extracts the local feature using the Local binary patterns (LBP) method. And the second method captures the global information of the texture using Radon transform. Synthesis images and generated images from Brodatz album database have been used in the evaluation part. The experiments illustrate the efficiency and the improvement made by the proposed combination, compared to [1] and [2], to extract the local and directional information of the texture.

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Section: Proposed Methodsmentioning

confidence: 99%

Texture matching using local and global descriptor

Hamouchene

Aouat

2014

2014 5th European Workshop on Visual Information Processing (EUVIP)

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“…A fast and accurate PPFT calculation method was proposed by the same group in [10,14] and has been used in different tomography based modalities such as electron microscopy [19,20,21], X-ray absorption and phase-contrast CT [22]. In [22,19], TV smoothing is used iteratively with forward/backward application of PPFT to reduce the artifacts which are made by incompleteness of the available data.…”

Section: Relation With Prior Workmentioning

confidence: 99%

“…In [22,19], TV smoothing is used iteratively with forward/backward application of PPFT to reduce the artifacts which are made by incompleteness of the available data. In [14], a 2D Discrete radon transform is proposed for fanbeam geometry for linear X-ray movement. Here we used a new combination of CST, CS and PPFT to improve our CS-based CT reconstructions.…”

Section: Relation With Prior Workmentioning

confidence: 99%

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Fast fan/parallel beam CS-based low-dose CT reconstruction

Hashemi

Beheshti

Gill

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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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 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.

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“…Unfortunately, the Shepp-Logan phantom is still largely used in papers that focus on 2D image reconstruction [19, 20, 21, 22, 23, 24, 25, 26, 27, 28]. We attribute this situation to the fact that software for data simulation with the Shepp-Logan phantom is not readily applicable to the FORBILD head phantom.…”

Section: Introductionmentioning

confidence: 99%

Simulation tools for two-dimensional experiments in x-ray computed tomography using the FORBILD head phantom

Noo

Dennerlein

et al. 2012

Phys. Med. Biol.

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Mathematical phantoms are essential for the development and early-stage evaluation of image reconstruction algorithms in x-ray computed tomography (CT). This note offers tools for computer simulations using a two-dimensional (2D) phantom that models the central axial slice through the FORBILD head phantom. Introduced in 1999, in response to a need for a more robust test, the FORBILD head phantom is now seen by many as the gold standard. However, the simple Shepp-Logan phantom is still heavily used by researchers working on 2D image reconstruction. Universal acceptance of the FORBILD head phantom may have been prevented by its significantly-higher complexity: software that allows computer simulations with the Shepp-Logan phantom is not readily applicable to the FORBILD head phantom. The tools offered here address this problem. They are designed for use with Matlab®, as well as open-source variants, such as FreeMat and Octave, which are all widely used in both academia and industry. To get started, the interested user can simply copy and paste the codes from this PDF document into Matlab® M-files.

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CT Reconstruction From Parallel and Fan-Beam Projections by a 2-D Discrete Radon Transform

Cited by 35 publications

References 11 publications

Texture matching using local and global descriptor

Texture matching using local and global descriptor

Fast fan/parallel beam CS-based low-dose CT reconstruction

Simulation tools for two-dimensional experiments in x-ray computed tomography using the FORBILD head phantom

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