MRI denoising by NeighShrink based on chi-square unbiased risk estimation

Zhang, Changjiang; Huang, Xueyou; Fang, Mingchao

doi:10.1016/j.artmed.2018.12.001

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…Klosowski and Frahm [ 14 ] conducted a review of the current status of MRI image denoising in 2017. Zhang et al [ 15 ] confirmed the value of the MRI image denoising algorithm based on chi-squared unbias theory in clinical diagnosis in 2019. Huang and Lin [ 16 ] improved the evaluation parameters of MRI images.…”

Section: Discussionmentioning

confidence: 99%

Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

2021

Computational and Mathematical Methods in Medicine

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This study was to explore the effect of a low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on magnetic resonance imaging (MRI) images of patients with cerebral aneurysm and to evaluate the practical value of the LRMD algorithm in the clinical diagnosis of cerebral aneurysm. In this study, the intracranial MRI data of 40 patients with cerebral aneurysm were selected to study the denoising effect of the low-rank matrix denoising algorithm based on the Gaussian mixture model on MRI images of cerebral aneurysm under the influence of Rice noise, to evaluate the PSNR value, SSIM value, and clarity of MRI images before and after denoising. The diagnostic accuracy of MRI images of cerebral aneurysms before and after denoising was compared. The results showed that after the low-rank matrix denoising algorithm based on the Gaussian mixture model, the PSNR, SSIM, and sharpness values of intracranial MRI images of 10 patients were significantly improved ( P < 0.05 ), and the diagnostic accuracy of MRI images of cerebral aneurysm increased from 76.2 ± 5.6 % to 93.1 ± 7.9 % , which could diagnose cerebral aneurysm more accurately and quickly. In conclusion, the MRI images processed based on the low-rank matrix denoising algorithm under the Gaussian mixture model can effectively remove the interference of noise, improve the quality of MRI images, optimize the accuracy of MRI image diagnosis of patients with cerebral aneurysm, and shorten the average diagnosis time, which is worth promoting in the clinical diagnosis of patients with cerebral aneurysm.

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Section: Discussionmentioning

confidence: 99%

Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

2021

Computational and Mathematical Methods in Medicine

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“…When the mathematical expectation is equal to the real value of the estimated parameters, the estimator is called unbiased value; that is, the systematic error is zero. The estimation steps [21] of unbiased risk estimation are:…”

Section: Threshold Optimizationmentioning

confidence: 99%

Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory

et al. 2021

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Soil moisture is an important factor determining yield. With the increasing demand for agricultural irrigation water resources, evaluating soil moisture in advance to create a reasonable irrigation schedule would help improve water resource utilization. This paper established a continuous system for collecting meteorological information and soil moisture data from a litchi orchard. With the acquired data, a time series model called Deep Long Short-Term Memory (Deep-LSTM) is proposed in this paper. The Deep-LSTM model has five layers with the fused time series data to predict the soil moisture of a litchi orchard in four different growth seasons. To optimize the data quality of the soil moisture sensor, the Symlet wavelet denoising algorithm was applied in the data preprocessing section. The threshold of the wavelets was determined based on the unbiased risk estimation method to obtain better sensor data that would help with the model learning. The results showed that the root mean square error (RMSE) values of the Deep-LSTM model were 0.36, 0.52, 0.32, and 0.48%, and the mean absolute percentage error (MAPE) values were 2.12, 2.35, 1.35, and 3.13%, respectively, in flowering, fruiting, autumn shoots, and flower bud differentiation stages. The determination coefficients (R2) were 0.94, 0.95, 0.93, and 0.94, respectively, in the four different stages. The results indicate that the proposed model was effective at predicting time series soil moisture data from a litchi orchard. This research was meaningful with regards to acquiring the soil moisture characteristics in advance and thereby providing a valuable reference for the litchi orchard’s irrigation schedule.

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“…(2) e series of white noise can be transformed by wavelet base coefficients so that it can be represented by zero mean white noise. (3) In the wavelet transform domain of the noisy image, the signal energy is mostly near the coefficient with higher absolute value, while the noise is completely the opposite [12]. erefore, a threshold value is set, the coefficient not exceeding the threshold value is set to 0, and the wavelet coefficients beyond the threshold value are stored.…”

Section: Wavelet Transform Of Noisy Motionmentioning

confidence: 99%

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Wang

Hou

2021

Scientific Programming

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Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

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MRI denoising by NeighShrink based on chi-square unbiased risk estimation

Cited by 11 publications

References 14 publications

Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm

Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

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