Wavelet domain non-linear filtering for MRI denoising

Anand, C.; Sahambi, J. S.

doi:10.1016/j.mri.2010.03.013

Cited by 113 publications

(58 citation statements)

References 29 publications

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“…We have compared, qualitative and quantitatively the performance of our proposed algorithm with optimal estimated parameters (R neighbor = 7, δ d = 5, δ r = 1.5σ n ) with other three state-of-the-art filtering algorithms: the unbiased nonlocal means filter (UNLM) [12], the adaptive blockwise non-local means filter (ABONLM) [9], and the 2D wavelet domain bilateral filter (2DW-BF) [19].…”

Section: Validation On Simulated Datasetmentioning

confidence: 99%

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Nonlinear filtering based on 3D wavelet transform for MRI denoising

Yang

Che

2012

EURASIP J. Adv. Signal Process.

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Magnetic resonance (MR) images are normally corrupted by random noise which makes the automatic feature extraction and analysis of clinical data complicated. Therefore, denoising methods have traditionally been applied to improve MR image quality. In this study, we proposed a 3D extension of the wavelet transform (WT)-based bilateral filtering for Rician noise removal. Due to delineating capability of wavelet, 3D WT was employed to provide effective representation of the noisy coefficients. Bilateral filtering of the approximation coefficients in a modified neighborhood improved the denoising efficiency and effectively preserved the relevant edge features. Meanwhile, the detailed subbands were processed with an enhanced NeighShrink thresholding algorithm. Validation was performed on both simulated and real clinical data. Using the peak signal-to-noise ratio (PSNR) to quantify the amount of noise of the MR images, we have achieved an average PSNR enhancement of 1.32 times with simulated data. The quantitative and the qualitative measures used as the quality metrics demonstrated the ability of the proposed method for noise cancellation.

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Section: Validation On Simulated Datasetmentioning

confidence: 99%

“…The proposed method can be used jointly with existing denoising methods to improve their effectiveness. Bilateral filtering in wavelet domain has been shown to preserve the edges efficiently [19]. Moreover, wavelet has been used for MRI denoising in combination with Radon transform, which estimates noise variance in different scales [20].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear filtering based on 3D wavelet transform for MRI denoising

Yang

Che

2012

EURASIP J. Adv. Signal Process.

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“…Anand et al [45] discussed a wavelet-based bilateral filtering scheme for noise reduction in magnetic resonance images. In this method, an algorithm was proposed for 2D image de-noising and segmentation using redundant discrete wavelet transform.…”

Section: Image Segmentation Using Region Growing (Rg) Methodsmentioning

confidence: 99%

“…The Larmor frequency will be in the radio frequency region (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50).…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Advanced Brain Tumour Segmentation from MRI Images

Rajasekaran¹,

Chellamuthu²

2018

High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications

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Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies.

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“…The classical NLM approach uses the region or patch based comparisons and exploits pattern redundancy to remove Gaussian noise without loss of significant image information. The approach provides the superior performance over many well-known techniques, such as median filtering [3], anisotropic filtering [4], total variation [5], bilateral filtering [6,7], and wavelet based approaches [8][9][10][11], however, it suffers from few limitations. First, although the approach provides good performance over smooth areas with higher redundancy and repetitive textures it fails on singular structures such as the edges and fine structure details.…”

Section: Introductionmentioning

confidence: 99%