Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Bao, Paul; Zhang, Lei

doi:10.1109/tmi.2003.816958

Cited by 258 publications

(139 citation statements)

References 24 publications

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“…Several noise reduction methods in the literature (Bao and Zhang, 2003;Coupé et al, 2008;Gerig et al, 1992) have been applied to MR data from patients with MS. The negative effect of noise in the image can also be reduced by incorporating spatial information in the segmentation (i.e., Markov random fields (MRF) (Ahmed et al, 2002;Zhang et al, 2001)).…”

Section:  Intensity Inhomogeneity (Iih) Correctionmentioning

confidence: 99%

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

García-Lorenzo

Francis

Narayanan

et al. 2013

Medical Image Analysis

322

268

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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra-and interexpert variability, many methods have been proposed to automatically segment lesions.The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.

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Section:  Intensity Inhomogeneity (Iih) Correctionmentioning

confidence: 99%

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

García-Lorenzo

Francis

Narayanan

et al. 2013

Medical Image Analysis

322

268

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“…And, we propose a multiscale scheme to improve the performance of vein detection. This scheme includes multiscale Anisotropic filters and scale production [9] [10] [11].…”

Section: Palm Vein Extractionmentioning

confidence: 99%

“…In [12], Mallat illustrated mathematically that signals and noise have different singularities and that edge structures present observable magnitudes along the scales, while noise decreases rapidly. With this observation, we responded to those problems of edge and line detection and noise reduction by thresholding the multiscale products [9] [10] [11]. For Multiscale analysis, a scale parameter is added to equation (1) to control the filter size:…”

Section: The Anisotropic Filter Was Defined Asmentioning

confidence: 99%

An Enhanced Biometric System for Personal Authentication

Umamaheswari.R¹

2013

IOSR-JECE

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“…The threshold value is selected in a Bayesian framework, through modeling the distribution of the wavelet coefficients as Gaussian, in BayesShrink approach. Several advancement have been proposed in the shrinkage algorithms by considering interscale and intrascale correlations of the wavelet coefficients [8]- [10]- [16]. The paper is organized in the following behavior, in Section II wavelet transform is explained, in Section III Neural network.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Image Denoising Technique using Neural Network

Pathak¹,

Kumar²,

Jaiswal³

et al. 2015

IJCA

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ABSTRCTImage processing is widely applied in various area of applications such as Medical, military, agriculture, etc.. The problem which generally occurs in image processing is the removal of noise generated due to various sources. In this paper a new approach based on neural network technique is proposed for the removal of noise. This technique follows three levels. This technique combines the advantages of filtering, neural network and bayes shrinkage technique. The noisy image is first passed through a bilateral filter and neural network is applied to the filtered image and the output of NN is then applied to bayes shrink. The proposed method outperforms other methods both visually and in case of objective quality peak-signal-to-noise ratio (PSNR) and MSE. Proposed method is verified for additive white Gaussian noise.

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Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Cited by 258 publications

References 24 publications

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

An Enhanced Biometric System for Personal Authentication

Performance Analysis of Image Denoising Technique using Neural Network

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