Multilevel magnetic resonance imaging compression using compressive sensing

Tashan, Tariq; Mahmood, Mahir K.

doi:10.1049/iet-ipr.2018.5611

Cited by 13 publications

(17 citation statements)

References 23 publications

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“…Medical imaging uses many different methods such as magnetic resonance (MR) imaging [1][2][3][4][5][6][7][8], radiography [4,[9][10][11], radionuclide [8,12], optical [11,13,14], ultrasound [1,15] and medical robotics [16,17]. The typical medical imaging system consists of three components (Figure 1): data acquisition, data consolidation and data processing.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.

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Section: Introductionmentioning

confidence: 99%

“…Various transforms are used to solve problems of 2D and 3D medical images denoising and compression in practice. The most common of them are discrete Fourier transform (DFT) [3,7,14,22] and discrete wavelet transform (DWT) [1,9,11,14]. DFT is widely used in the frequency domain but the domain characteristics disappeared after it.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

2020

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“…(3) Initial the temperature t 0 ; (4) Cooling rate α; (5) e outer-loop iteration out max ; (6) e inner-loop iteration inner max . (7) S * ⟵ S, t ⟵ t 0 (8) Iteration: (9) While i < out max do (10) for j < inner max do (11) S′ ⟵ random neighbor of a random neighborhood k, S′ ∈ N H (S)…”

Section: Theorem 1 Suppose the Support Set I Is The Unique Solution mentioning

confidence: 99%

“…CS technology can reduce the hardware requirements, further reduce the sampling rate, improve the signal restoration quality, and save the cost of signal processing and transmission. Currently, CS has been widely used in wireless sensor networks [5,6], information theory [7], image processing [8][9][10], earth science, optical/microwave imaging, pattern recognition [11], wireless communications [12,13], atmosphere, geology, and other fields. CS theory is mainly divided into three aspects: (1) sparse representation; (2) uncorrelated sampling; (3) sparse reconstruction.…”

Section: Introductionmentioning

confidence: 99%

The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

Zhang

Sun

et al. 2019

Journal of Electrical and Computer Engineering

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is paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. is algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. en, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. e proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.

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“…The work in [15, 16], compared different families of wavelet bases in terms of compression performance. The authors in [17] proposed the concept of compressive sensing and its application for image compression is introduced in many articles, such as [18]. In [19], image compression using discrete anamorphic stretch transform is presented.…”

Section: Introductionmentioning

confidence: 99%

Image compression using adaptive multiresolution image decomposition algorithm

Alkishriwo

2020

IET image process

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Multilevel magnetic resonance imaging compression using compressive sensing

Cited by 13 publications

References 23 publications

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

Image compression using adaptive multiresolution image decomposition algorithm

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