Multicomponent MR Image Denoising

Manjón, José V.; Thacker, Neil A.; Lull, Juan José; García-Martí, Gracián; Martí‐Bonmatí, Luis; Robles, Montserrat

doi:10.1155/2009/756897

Cited by 54 publications

(49 citation statements)

References 26 publications

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“…In MRI, early works using the NLM method are from Coupe et al (2008) and Manjón et al (2008). The bibliography related to this method is quite extensive (Tristan-Vega et al, 20012;Coupe et al, 2012;Manjón et al, 2009Manjón et al, , 2010Manjón et al, , 2012Wiest-Daesslé et al, 2008;He and Greenshields, 2009;Rajan et al 2012Rajan et al , 2014.…”

Section: Introductionmentioning

confidence: 99%

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MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

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152

122

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This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.3

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Section: Introductionmentioning

confidence: 99%

“…In Manjón et al (2009) PCA was used as a postprocessing step to remove remaining noise after the application of a multicomponent non-local means filter for multimodal MRI. Recently, a nonparametric PCA based filter was proposed for 2D MR images where patch similarities are estimated using rank limited PCA coefficients (Kim et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

Self Cite

152

122

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“…Since they are accomplished of suppressing noise, recent wavelet thresholding [3,4] based denoising methods have proved capable, during the conservation the high-frequency signal details. However, by standard wavelet thresholding methods [5][6][7], the local space-scale information of the image is not adaptively considered. The threshold [8] at certain scale is a constant for all wavelet coefficients in standard wavelet thresholding based noise reduction methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Noise Filtering Algorithm Based on Neuro-Type 2 Fuzzy Systems

Rai

2017

Asian J Pharm Clin Res

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Objetcive: Medical images are often subjected to noise due to the failure of data acquisition hardware at the source. Thus, making it difficult for the radiologist to perform image analysis and give correct diagnosis of the disease. Methods:This research presents a new image denoising algorithm based on the combination of neuro-type 2 fuzzy systems. The method not only preserves the information relevant for diagnostic details but also provides a cost-effective solution for recovery of lost information due to noise. Resulst and Conclusion:The pixel transformation achieved by the proposed method is found to be more efficient than the wavelet methods in image denoising particularly for the removal of Gaussian noise while ensuring that the denoising is done while keeping intact of the integrity and granularity of the image.

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“…Manjon et al [15] modified the original NLM algorithm to denoise the multispectral MRI. In the multispectral sequences, the similarity measure can be obtained by combining information of various channels.…”

Section: Introductionmentioning

confidence: 99%

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan¹,

Krishnaveni²,

Guo³

2013

Measurement Science Review

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In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

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Multicomponent MR Image Denoising

Cited by 54 publications

References 26 publications

MRI noise estimation and denoising using non-local PCA

MRI noise estimation and denoising using non-local PCA

Hybrid Noise Filtering Algorithm Based on Neuro-Type 2 Fuzzy Systems

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

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