A Review of Wavelet Denoising in MRI and Ultrasound Brain Imaging

Pižurica, Aleksandra; Wink, Alle Meije; Vansteenkiste, Ewout; Philips, Wilfried; Roerdink, B.Jos T.M.

doi:10.2174/157340506776930665

Cited by 109 publications

(58 citation statements)

References 72 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [1][3] [9] and [17] noise in magnetic resonance magnitude images is shown to obey a Rician distribution. Rician noise is signal-dependent and unlike where noise is Gaussian distributed where spatial and other frequency transform denoising is satisfactorily, separating signal from noise is a difficult task.…”

Section: Rician Noisementioning

confidence: 99%

A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

Kagoiya¹,

Mwangi²

2017

IJCA

View full text Add to dashboard Cite

Magnetic Resonance Imaging is one of the most advanced and effective medical diagnosis methods ,however the raw image data is normally corrupted by random noise from the measurement process this reduces the accuracy and reliability of the results. Denoising methods are often used to increase the Signal-to-Noise Ratio (SNR) and improve image clarity .In this paper an adaptive Non-Local Means filter is developed in which bilateral filter is used to pre-enhance the images and then multi resolution wavelet domain is used to remove coefficients that contain more noise than signal.In the past different methods have been used to denoise MRI images but many have not taken into consideration the Rician nature of noise distribution therefore they have not been very effective .Adaptation in this case is based on frequency and spatial information obtained from the noisy image. Knowledge of level of noise is used in an optimization procedure to minimize a Rician based likelihood function and by use of square signal intensity bias is also discarded. The method is implemented in Matlab and MRI images with different level of artificial noise are denoised using the algorithm. Measures of performance values are PSNR, 37.12dB, MSE, 15.23, UQI, 0.985, SSIM, 0.894 , EPI,0.69 for a 10% noisy image. These and also visual inspection show that there is significant improvement from results obtained using stand alone methods such as Gaussian smoothing, Wiener filter, NLM filter ,bilateral filter and wavelet thresholding. General TermsMedical image processing, denoising, measures of quality, magnetization vector, adaptive multi resolution, total variation, wavelet coefficient thresholding ,Non-Local Means, frequency localization, proton spin density, discrete inverse Fourier transform, phase unwrapping, signal-dependent bias, local neighbourhood, Rician-based log-likelihood function.

show abstract

Section: Rician Noisementioning

confidence: 99%

A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

Kagoiya¹,

Mwangi²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…al. [9], where the authors reviewed the performance of wavelet denoising specifically in MRI and brain imaging.…”

Section: Related Workmentioning

confidence: 99%

“…Wavelets compression techniques have since then been extensively used in image compression and are part of the JPEG 2000 standard [7]. Wavelet transforms have also been extensively used in medical data analysis including ECG diagnosis [8] and ultrasound image processing [9]. More recently, researchers have applied wavelet transforms in the field of Non-Intrusive Load Monitoring (NILM) [10,11].…”

Section: Introductionmentioning

confidence: 99%

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Bissyandé

Klein

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

show abstract

“…Here the general assumption is that noise is modeled by Gaussian distribution after log compression. In order to relax this condition Pizurica et al [13] proposed a wavelet-based Generalized Likelihood ratio formulation and imposed no prior on noise and signal statistics. The motive of speckle reduction filters is smoothing of the input image with preservation of edge information because in ultrasonic images, edges contains the useful information that the experts are concerned about, but there is always a tradeoff between noise suppression and loss of information.…”

Section: Transform Domain Filtersmentioning

confidence: 99%

A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Lal¹,

Kaur²,

Gupta³

2015

IJIGSP

View full text Add to dashboard Cite

Abstract-Speckle is a multiplicative noise that degrades the quality of ultrasound images and its presence makes the visual inspection difficult. In addition, it limits the professional application of image processing techniques such as automatic lesion segmentation. So speckle reduction is an essential step before further processing of ultrasonic images. Numerous techniques have been developed to preserve the edges while reducing speckle noise, but these filters avoid smoothing near the edges to preserve fine details. The objective of this work is to suggest a new technique that enhances B-Scan breast ultrasound images by increasing the speckle reduction capability of an edge sensitive filter. In the proposed technique a local statics based filter is applied in the non homogeneous regions, on the output of an edge preserving filter and an edge map is used to retain the original edges. Experiments are conducted using synthetic test image and real time ultrasound images. The effectiveness of the proposed technique is evaluated qualitatively by experts and quantitatively in terms of various quality metrics. Results indicate that proposed method can reduce more noise and simultaneously preserve important diagnostic edge information in breast ultrasound images.

show abstract

A Review of Wavelet Denoising in MRI and Ultrasound Brain Imaging

Cited by 109 publications

References 72 publications

A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Contact Info

Product

Resources

About