A new denoising method for fMRI based on weighted three-dimensional wavelet transform

Özmen, Güzin; Özşen, Seral

doi:10.1007/s00521-017-2995-7

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…Its sensor manufacturing process is simple and small in size, has low performance and power consumption requirements, and has no dependence on the surface texture of the object. Its shortcomings are mainly that it will produce unreliable results at the edge of the field of view and the edge of the object, the resolution is relatively low, the measurement depth at short distances is missing, and there is a problem of multipath interference [26]. erefore, based on the complementary characteristics between ToF depth camera and binocular stereo matching, the combination of ToF depth camera and binocular stereo technology can expand its scope of application and improve performance.…”

Section: Basics Of 3d Reconstructionmentioning

confidence: 99%

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

Luo

Zhang

Cai

et al. 2022

Journal of Healthcare Engineering

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In order to solve the problem that people often have pain in the hip joint, it is more meaningful to study femoral-acetabular impingement syndrome in the future. This article aims to study the finite element analysis of femoral-acetabular impingement based on three-dimensional reconstruction. This paper proposes a selective image matching strategy. In the feature matching stage, all images are not matched in pairs, but the corresponding camera distance between the images is calculated initially, which has little effect on the number of features and greatly reduces the time of feature matching, thereby reducing the time cost of 3D reconstruction. In this experiment, a double-blind experiment was used to check the range of motion of all hip joints. Two senior radiologists read the obtained hip joint orthographic films to screen out the hip joint orthographic films that meet the requirements. Experimental data shows that although the initial matching points of the algorithm in this paper are lower than those of the traditional algorithm, the final number of matching points is higher than that of the traditional algorithm. When the final number of patches is fixed to 10000, the initial patch required by the algorithm in this paper is more than that required by the SAD algorithm, nearly 13%, but the total storage requirement is 56.4% of the SAD algorithm, which is a big improvement.

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Section: Basics Of 3d Reconstructionmentioning

confidence: 99%

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

Luo

Zhang

Cai

et al. 2022

Journal of Healthcare Engineering

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“…One possible remedy is to reduce number of slices and screen voxels more frequently, see Henson et al () and Smith et al (), but still physiological components will be present on scale 1. As a result, a lot of attention in the literature has been devoted to reducing the deterministic physiological noise via technological, bioengineering and statistical improvements, see Lazar (), Welvaert and Rosseel (), Patel and Bullmore () and Ozmen and Ozsen ().…”

Section: Ultra Fast Task Fmrimentioning

confidence: 99%

Statistical analysis of fMRI using wavelets: Big Data, denoising, large‐p‐small‐n matrices

Efromovich

2019

WIREs Computational Stats

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Over the past decade functional Magnetic Resonance Imaging (fMRI) has been intensively used to study the complex functional network organization of the human brain and how it changes in time. An fMRI machine produces 3D timecourse cerebral images that contain hundreds of thousands of voxels and each voxel is scanned for hundreds of times. This potentially allows the researchers to explore functional connectivity on a voxel-to-voxel level, and also yields a number of serious statistical complications. First of all, the high-dimension property of fMRI data turns it into Big Data. Furthermore, the study of functional brain network for so many voxels involves the problem of estimation of and simultaneous inference for large-p-small-n cross-covariance matrices. Furthermore, all problems should be solved in the presence of notoriously large fMRI noise which often forces statisticians to average signals over large areas instead of considering a network between individual voxels. An attractive alternative to the averaging, discussed in the paper, is a multiresolution wavelet analysis complemented by special procedures of estimating noise and estimation and simultaneous inference for cross-covariance and cross-correlation matrices for hundreds of thousands pairs of voxels, and it is fair to say that if wavelets have not been already known, fMRI applications would necessitates their creation. Both task and resting-state fMRI are considered, and lessons from the wavelet analysis of ultra-fast and conventional neuroplasticity fMRI experiments are presented. The article is self-contained and does not require familiarity with wavelets or fMRI. This article is categorized under: Algorithms and Computational Methods > Numerical Methods Applications of Computational Statistics > Signal and Image Processing and Coding K E Y W O R D S Big Data, fMRI, wavelet analysis

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“…In order to effectively identify the feature information contained in the fault signal of rotating machinery and reveal its inherent characteristics, many fault feature extraction methods of rotating machinery have been proposed, such as empirical mode decomposition (EMD) [7,8], mathematical morphology filtering [9,10], wavelet decomposition [11,12], adaptive filtering [13,14], matching pursuit [15,16], cyclostationary signal analysis [17,18], Wiener filter [19], Kalman filter [20,21], and stochastic resonance [22,23] that are widely used in early fault diagnosis of rotating machinery. The EMD proposed by Huang et al [7] is a nonstationary signal analysis method, which can find the hidden characteristic information in the signal, and has been widely used in the extraction and noise reduction of the impact signal of rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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A new denoising method for fMRI based on weighted three-dimensional wavelet transform

Cited by 17 publications

References 16 publications

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

Statistical analysis of fMRI using wavelets: Big Data, denoising, large‐p‐small‐n matrices

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

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