A novel method for removing Rician noise from MRI based on variational mode decomposition

Pankaj, Divya; Govind, D.; Narayanankutty, K.

doi:10.1016/j.bspc.2021.102737

Cited by 18 publications

(8 citation statements)

References 55 publications

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“…The mechanism of the algorithm starts by removing the higher frequency and using the lower frequency to retrieve the image. The last step in the algorithm is to use a total variation (tv)regularization to remove remnant noise details from the first stage [23], as shown in the next block diagram in Fig. 4.…”

Section: Vibrational Mode Decompositionmentioning

confidence: 99%

Novel Framework of Segmentation 3D MRI of Brain Tumors

El-Henawy¹,

Elbaz²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the values of Peak Signal to Noise Ratio (PSNR) and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor. The framework has been divided into three stages: removing speckle and Rician noise, the segmentation stage, and the feature extraction stage. The framework presents solutions for each problem at a different stage of the segmentation. In the first stage, the framework uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D) algorithms to remove the Rician. Afterwards, the most significant Rician noise-free images are passed to the three different methods: Deep Residual Network (DeRNet), Dilated Convolution Auto-encoder Denoising Network (Di-Conv-AE-Net), and Denoising Generative Adversarial Network (DGAN-Net) for removing the speckle noise. VMD and Bm3D have achieved PSNR values for levels of noise (0, 0.25, 0.5, 0.75) for reducing the Rician

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Section: Vibrational Mode Decompositionmentioning

confidence: 99%

Novel Framework of Segmentation 3D MRI of Brain Tumors

El-Henawy¹,

Elbaz²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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“…8,9 Magnetic Resonance images (MRI) have Gaussian, Rician, and Rayleigh noises. 10 The image type and the noise model determine the method used to remove the noise in the image. Several methods filter noise without affecting important features in images.…”

Section: Introductionmentioning

confidence: 99%

A powerful probabilistic model for noise analysis in medical images

Gürman

Bilgehan²,

Sabuncu³

et al. 2022

Int J Imaging Syst Tech

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The statistical properties in various medical images demonstrate uncorrelated noise fluctuations. The signal noise fluctuations are generally due to physical imaging processes and have nothing to do with the tissue textures. Adding the noise types (e.g., quantization, electronics, photon) usually degrade medical images. The noise variation is usually assumed to be additive with zero-mean, constant variance Gaussian distribution. However, close consideration of different medical images indicates the need for better model representation to minimise the noise that can be vital in decision-making. This research proposed a probabilistic method to represent all real-type noise in general medical images. The method aims to cover most classical statistical models such as Gaussian, lognormal, Rayleigh, Weibull, and Nakagami without a prior examination to test for fitness. The proposed model was applied to actual clinical images to test the performance of the noise originating from the physical processes. The noise is assumed to be additive white Gaussian type with a zero mean and constant variance. The theoretical literature indicates that a nonlinear function can better represent noise. This research helps to form a relationship between the image intensity and the noise variance that yields the fitting parameters in the introduced nonlinear function. The validity of the proposed method was proved mathematically and tested using the well know Kolmogorov-Smirnov (K-S) and Akaike Information Criteria (AIC) tests. The method was successfully applied to various clinical images such as magnetic resonance, x-ray, and panoramic images. The model's performance is compared with the classical models using root mean squared error (RMSE), relative error (RE), and R 2 as the evaluation matrices. The presented model has outperformed all classic models.

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“…But, it is rarely used in PQDs identification. Some of its applications have been implemented in literature [23,24]. A wind speed interval forecasting model based on VMD and multi-objective problem was proposed by Wang and Cheng [23].…”

mentioning

confidence: 99%

“…This model included forecasting and noise reduction beside combination module to provide precise wind speed forecast for power model operation. Based on the VMD, a two stage method for eliminating the noise of Magnetic resonance imaging (MRI) was proposed by Pankaj et al [24]. The VMD was introduced by Pankaj et al [24] as an efficient technique to sequestrate the image into its components or modes based on frequency.…”

mentioning

confidence: 99%

“…Based on the VMD, a two stage method for eliminating the noise of Magnetic resonance imaging (MRI) was proposed by Pankaj et al [24]. The VMD was introduced by Pankaj et al [24] as an efficient technique to sequestrate the image into its components or modes based on frequency. An approach was proposed by Jalilian and Samadinasab [25] for measuring and finding short term PQ events in power systems by using microphasor measurement units.…”

mentioning

confidence: 99%

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Identification of Combined Power Quality Disturbances in the Presence of Distributed Generations using Variational Mode Decomposition and K-nearest Neighbors Classifier

2022

IJE

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Identification of combined power quality disturbances in the modern power systems by considering the development of different types of loads and distribution generations has become increasingly important. The novelty of this paper comes from the accurate and fast identification of the combined power quality disturbances in the presence of different distributed generations and loads such as photovoltaic cell, wind turbine with doubly fed induction generators, diesel engines, electric arc furnace, DC machine, 6-pulse and 12-pulse rectifier loads. In this paper, the features are extracted using variational mode decomposition, just from voltage waveforms. To reduce the redundant data, dimension of features vector, and time, the Relief-F method and correlation feature selection method are applied on the extracted features and these two methods are compared together. In this paper, the K-nearest neighbors classifier is used to classify the multiple power quality disturbances. To verify the effectiveness of the proposed method, different scenarios such as misfiring, variation of sun radiation and wind speed, entrance and exit of loads, capacitors and distributed generators, different fault at the grid in half-load to full-load were simulated. This method can be used as an added algorithm for smart metering in modern and smart power systems.

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A novel method for removing Rician noise from MRI based on variational mode decomposition

Cited by 18 publications

References 55 publications

Novel Framework of Segmentation 3D MRI of Brain Tumors

Novel Framework of Segmentation 3D MRI of Brain Tumors

A powerful probabilistic model for noise analysis in medical images

Identification of Combined Power Quality Disturbances in the Presence of Distributed Generations using Variational Mode Decomposition and K-nearest Neighbors Classifier

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