Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method

2020 ◽  
Vol 28 (6) ◽  
pp. 1037-1054
Author(s):  
Temitope E. Komolafe ◽  
Qiang Du ◽  
Yin Zhang ◽  
Zhongyi Wu ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Breast Tissue ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Dual Energy ◽  
Hybrid Optimization ◽  
Signal To Noise ◽  
Breast Ct ◽  
Different Levels

BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

Fade Duration Based Neighbor Cell List Optimization for Handover in Femtocell Networks

2017 ◽  
Vol 9 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Aklilu Assefa Gebremichail ◽  
Cory Beard
Keyword(s):  
Interference Mitigation ◽  
Selection Process ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Signal To Noise ◽  
Femtocell Networks ◽  
Femtocell Network ◽  
Traffic Performance ◽  
Neighbor List ◽  
Simulation Results

In a dense femtocell network, beyond co-tier and cross-tier interference mitigation, handover femtocell- femtocell and macrocell-femtocell is a major challenge. In order to perform successful handover, avoiding the scanning of a large neighbor list and shortening the handover period is required to identify the optimal neighbor. In this paper, a neighbor cell list optimization method based on fade duration along with an algorithm for open and hybrid femtocell networks is proposed. The proposed method considers fade duration outage probability (FDOP), distance between femtocell access points, and the operating frequency as benchmarks for optimization of the neighbor list selection process. FDOP determines a duration beyond which a connection is considered in an outage state. The simulation results based on this proposed method also show an improvement over previously proposed methods that create neighboring lists based on received signal and signal-to-noise ratio. Fade duration based optimization provides a much better prediction of traffic performance.

STOCHASTIC RESONANCE IN A BALANCED PAIR OF SINGLE-ELECTRON BOXES

2011 ◽  
Vol 10 (03) ◽  
pp. 267-275 ◽  
Author(s):  
TAKAHIDE OYA ◽  
ALEXANDRE SCHMID ◽  
TETSUYA ASAI ◽  
AKIRA UTAGAWA
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Stochastic Resonance ◽  
Thermal Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Single Electron ◽  
Signal To Noise ◽  
Thermal Agitation ◽  
Internal States

Stochastic resonance in a fundamental single-electron circuit, i.e., a balanced pair of single-electron boxes, is observed and presented theoretically, where the signal-to-noise ratio (SNR) of the internal states stimulated by responding to a periodic subthreshold or suprathreshold input is enhanced by thermal agitation in tunneling junctions. Through extensive Monte-Carlo simulations, the peak SNR was determined as a function of temperature and input amplitudes. These results imply the possibility to design single-electron circuits that may "exploit" thermal noise, instead of employing conventional noise-suppression strategies.

Dictionary learning for signal-to-noise ratio enhancement

2015 ◽  
Author(s):  
Pierre Turquais* ◽  
Endrias G. Asgedom ◽  
Walter Söllner ◽  
Einar Otnes
Keyword(s):  
Dictionary Learning ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Signal-to-noise ratio evaluation in dual-energy radiography with synchrotron radiation

2002 ◽  
Vol 47 (22) ◽  
pp. 4093-4105 ◽  
Author(s):  
S Fabbri ◽  
A Taibi ◽  
R Longo ◽  
M Marziani ◽  
A Olivo ◽  
...  
Keyword(s):  
Synchrotron Radiation ◽  
Signal To Noise Ratio ◽  
Dual Energy ◽  
Signal To Noise ◽  
Noise Ratio

DE-NOISING AND BASELINE WANDERING REMOVAL OF ELECTROCARDIOGRAM USING DOUBLE DENSITY DISCRETE WAVELET

2007 ◽  
Vol 05 (03) ◽  
pp. 399-415 ◽  
Author(s):  
R. SHANTHA SELVA KUMARI ◽  
V. SADASIVAM
Keyword(s):  
Noise Level ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Wavelet Approximation ◽  
Baseline Wandering ◽  
Noise Ratio ◽  
Four Levels ◽  
Approximation Coefficients ◽  
Different Levels

In this paper, an off-line double density discrete wavelet transform based de-noising and baseline wandering removal methods are proposed. Different levels decomposition is used depending upon the noise level, so as to give a better result. When the noise level is low, three levels decomposition is used. When the noise level is medium, four levels decomposition is used. When the noise level is high, five levels decomposition is used. Soft threshold technique is applied to each set of wavelet detail coefficients with different noise level. Donoho's estimator is used as a threshold for each set of wavelet detail coefficients. The results are compared with other classical filters and improvement of signal to noise ratio is discussed. Using the proposed method the output signal to noise ratio is 19.7628 dB for an input signal to noise ratio of -7.11 dB. This is much higher than other methods available in the literature. Baseline wandering removal is done by using double density discrete wavelet approximation coefficients of the whole signal. This is an unsupervised method allowing the process to be used in off-line automatic analysis of electrocardiogram. The results are more accurate than other methods with less effort.

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