TENCON 2011 - 2011 IEEE Region 10 Conference 2011
DOI: 10.1109/tencon.2011.6129131
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Wavelet based despeckling of medical ultrasound images with bilateral filter

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Cited by 15 publications
(11 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…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%
“…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%
“…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.…”
mentioning
confidence: 99%