Wavelet based despeckling of medical ultrasound images with bilateral filter

Vanithamani, R.; Umamaheswari, G.

doi:10.1109/tencon.2011.6129131

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…In the other way around, when T is too large, the noise reduction is over sufficient. In this study, one of the most commonly used method is considered to estimate the value T (Braunisch et al, 2000;Prinosil et al, 2010;Vanithamani and Umamaheswari, 2011), and the estimator is defined asT…”

Section: Sparsificationmentioning

confidence: 99%

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Bayesian sparsity estimation in compressive sensing with application to MR images

Wang

Zhou

Garpebring

et al. 2019

Communications in Statistics: Case Studies, Data Analysis and A

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The theory of compressive sensing (CS) asserts that an unknown signal x ∈ C N can be accurately recovered from m measurements with m N provided that x is sparse. Most of the recovery algorithms need the sparsity s = x 0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.

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Section: Sparsificationmentioning

confidence: 99%

“…where N is the number of image pixels, σ image is the standard deviation of noise of the image which can be estimated by (Donoho, 1995;Prinosil et al, 2010;Vanithamani and Umamaheswari, 2011)…”

Section: Sparsificationmentioning

confidence: 99%

Bayesian sparsity estimation in compressive sensing with application to MR images

Wang

Zhou

Garpebring

et al. 2019

Communications in Statistics: Case Studies, Data Analysis and A

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show abstract

“…In [23], an approach for despeckling of an ultrasound image is proposed which is based on the wavelet thresholding and bilateral filtering. For the thresholding purpose, Neigh Shrink [24] (-thresholds the wavelet coefficients based on the magnitude of the square sum of all the wavelet coefficients within the neighborhood window‖.)…”

Section: F Wavelet Based Bilateral Filtermentioning

confidence: 99%

“…In this transformation, small elements denote noise while large denotes necessary features of an image. The general procedure given in [23] The wavelet coefficient is represented by:…”

Section: Wavelet Thresholdingmentioning

confidence: 99%

Despeckling of Medical Ultrasound Images: A Technical Review

Gupta¹,

Shukla²,

Agarwal³

2016

IJIEEB

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Abstract-Acquisition of digital image and preprocessing methods plays a vital role in clinical diagnosis. The ultrasound medical images are more popular than other imaging modalities, due to portable, adequate, harmless and cheaper nature of it. Because of intrinsic nature of speckle noise (signal based noise), ultrasound medical image leads to degradation of the resolution and contrast of the image. Reduction of this signal based noise is helpful for the purpose of visualization of the ultrasound images. The low quality of image is considered as a barrier for the better extraction of features, recognition, analysis and detection of edges. Because of which inappropriate diagnosis may be done by doctor. Thus, speckle noise reduction is essential and preprocessing step of ultrasound images. Analysts survey manifold reduction methods of speckle noise, yet there is no exact method that takes all the limitations into account. In this review paper, we compare filters that are Lee, Frost, Median, SRAD, PMAD, SRBF, Bilateral, Adaptive Bilateral and Multiresolution on medical ultrasound images. The results are compared with parameter PSNR along with the visual inspection. The conclusion is illustrated by filtered images and data tables.

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“…Authors [5] have implemented the image denoising algorithm based on undecimated versions of the dual tree wavelet transforms to overcome the problem of oscillations. Authors [7] uses bilateral filter to reduce speckle noise from ultrasound images. Authors [8] proposed MRI and X-ray image denoising using Dual tree DWT and curve let transform in their paper.…”

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confidence: 99%

A combined approach for noise reduction in medical images using Dual Tree Discrete Wavelet Transform and Rotated Dual Tree Discrete Wavelet Transform

Thakur¹,

Ambhore²,

Kadam³

et al. 2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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Medical imaging suffers from image noise. To remove this noise spatial domain and transform domain techniques are used. But spatial domain techniques have limitation of edge blurring w.r.t transform based techniques .Therefore in this paper we have proposed a transform based denoising technique. We have used Dual tree DWT and Rotated version of Dual Tree DWT jointly to improve our denoising results. Our main focus is to get more directional information from this transform which will improve denoising results. We have compared our results with existing methods and results are very encouraging. PSNR is used as image quality measure.

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Wavelet based despeckling of medical ultrasound images with bilateral filter

Cited by 15 publications

References 14 publications

Bayesian sparsity estimation in compressive sensing with application to MR images

Bayesian sparsity estimation in compressive sensing with application to MR images

Despeckling of Medical Ultrasound Images: A Technical Review

A combined approach for noise reduction in medical images using Dual Tree Discrete Wavelet Transform and Rotated Dual Tree Discrete Wavelet Transform

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