Speckle reduction with edges preservation for ultrasound images: using function spaces approach

Lee, M.-S.; Yen, Chieh-Li; Ueng, Shyh‐Kuang

doi:10.1049/iet-ipr.2011.0366

Cited by 23 publications

(16 citation statements)

References 21 publications

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“…The standard deviation σs should vary with the contamination level of the speckle noise. Then an overall filtered image Y is obtained by the proposed Quantuminspired Weighted Bi-lateral Filter (QWBF) [5] , ̂= (1 − )̂+̂ (11).…”

Section: Quantum Inspired Bi-lateral Filteringmentioning

confidence: 99%

Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

Shrihari¹,

Harsha²,

Padmaja³

2016

IJERT

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The ultrasound is a nondestructive technique used widely in the medical field for detection of soft tissues in the human body. But this should be validated by an expert radiologist, since ultrasound images are highly affected by noises. In this paper four methods for denoising the speckle noise are compared and analyzed, namely, diffusion tensors, heavy-tailed Levy's distribution, quantum inspired bilateral filtering and locally adaptive wavelet domain Bayesian processor. Performances of each method were quantified by means of Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index Matrix (MSSIM). It was found that denoising of US images through QWBF has higher PSNR value of 16.75 and MSSIM of 0.88. Hence this method was proved to be more efficient compared to other three methods presented in this paper.

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Section: Quantum Inspired Bi-lateral Filteringmentioning

confidence: 99%

Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

Shrihari¹,

Harsha²,

Padmaja³

2016

IJERT

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“…2.2 Estimation of m r n j Using (6), the rth moment m r n j of the noise n j in (3) can be expressed as follows By exploiting (12), the solution to (14) can be determined as…”

Section: Image Modellingmentioning

confidence: 99%

“…It has been shown that effective signal reconstruction can be achieved by employing wavelet shrinkage based on Bayesian formalism than by using the thresholding techniques [11][12][13][14][15][16][17][18][19]. The Bayesian framework requires a suitable probability density function (PDF) to model the wavelet coefficients.…”

Section: Introductionmentioning

confidence: 99%

Bayesian denoising of ultrasound images using heavy‐tailed Levy distribution

Ranjani

Chithra²

2015

IET Image Processing

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Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre-processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform-based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images.

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“…SRAD removes the speckle noise via the operations of partial differential equations. SRAD is effective for speckle noise removal, but there are significant problems associated with SRAD like blurring, loss of important structural details and generation of artificial smear [25].…”

Section: Pre-processing With Ultrasound Image For Despecklingmentioning

confidence: 99%

Ultrasound guided automatic localization of needle insertion site for epidural anesthesia

Tan

Shen

et al. 2013

2013 IEEE International Conference on Mechatronics and Automation

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In this paper, ultrasound imaging is utilized to detect the anatomical structure of the lumbar spine based on which an image processing algorithm will search for key features to identify the optimal needle insertion site. The key challenge lies in the nature of ultrasound images which are obscure and have low spatial resolution, induced by contamination from random speckle noises. In order to improve the interpretability of ultrasound images, a modified version of local normalization using the Difference of Gaussian algorithm is first used for pre-processing to filter the speckle noise and extract the main anatomical structure in the raw images obtained. Meanwhile, local means induced by non-uniform wave reflection rate is also successfully removed by the proposed pre-processing algorithm, thus a potential element that may degrade the image recognition accuracy is excluded. In the second stage, a template matching algorithm, augmented with a position correlation function, automatically identifies the key features of interest and thus the insertion site. The approach has been tested on more than 200 ultrasound images with a 100% success rate. The proposed system allows the anesthetist to use the approach efficiently without the burden of interpreting real time ultrasound images.

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Speckle reduction with edges preservation for ultrasound images: using function spaces approach

Cited by 23 publications

References 21 publications

Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

Bayesian denoising of ultrasound images using heavy‐tailed Levy distribution

Ultrasound guided automatic localization of needle insertion site for epidural anesthesia

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