Despeckle Filtering Algorithms and Software for Ultrasound Imaging

Loizou, Christos P.; Pattichis, Constantinos S.

doi:10.2200/s00116ed1v01y200805ase001

Cited by 73 publications

(127 citation statements)

References 93 publications

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“…Wiener filter (Jin 2003) performs smoothing of the image based May 8, 2014 Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization article on the computation of local image variance. Ideal Fourier and Butterworth filtering performs image enhancement by applying the filter function and inverse FFT on the image (Loizou 2008). Bilateral filtering technique (Tomasi 1998), basically is a combination of a spatial and range filter, where each output pixel value is a Gaussian weighted average of its neighbours in both space and intensity range.…”

Section: Adaptive Local Filtersmentioning

confidence: 99%

Performance analysis of speckle ultrasound image filtering

Rosa

Monteiro

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Over the last three decades, several despeckling filters have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. This paper compiles and compares well-known techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. A comparison of the methods studied is done based on an experiment, using quality metrics, texture analysis and interpretation of row profiles to evaluate their performance and show the benefits each one can contribute to denoising and feature preservation. To test the methods, a noise-free image of a kidney is used and then the Field II program simulates a B-mode ultrasound image. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of seventeen different speckle reduction algorithms have been documented based on spatial filtering, diffusion filtering and wavelet filtering, with fifteen qualitative metrics estimation. We use the tendencies observed in our study in real images. A new evaluation metric is proposed to evaluate the despeckling results.

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Section: Adaptive Local Filtersmentioning

confidence: 99%

Performance analysis of speckle ultrasound image filtering

Rosa

Monteiro

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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“…Wiener filter [7] performs smoothing of the image based on the computation of local image variance. Ideal Fourier and Butterworth filtering performs image enhancement by applying the filter function and inverse FFT on the image [11]. Bilateral filtering technique is a combination of a spatial and range filter, where each output pixel value is a Gaussian weighted average of its neighbours in both space and intensity range.…”

Section: Speckle Filtering Techniquesmentioning

confidence: 99%

“…However, B-mode ultrasound images are usually corrupted by the speckle artifact, which introduces fictitious structures that can not be removed by the imaging system [11,14]. Speckle noise is defined as multiplicative noise with a granular pattern formed due to coherent processing of backscattered signals from multiple distributed targets.…”

Section: Introductionmentioning

confidence: 99%

Towards a Comprehensive Evaluation of Ultrasound Speckle Reduction

Monteiro

Rufino

Cadavez

2014

Lecture Notes in Computer Science

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“…The transformed image is then denoised by wavelet thresholding or by Bayesian shrinkage. The medical ultrasound devices often include internal data pre-processing like a logarithmic compression of the dynamic range of the data (Loizou and Pattichis, 2008). Noise in the resulting image is not purely multiplicative and additional logarithmic transformation prior to speckle filtering seems less appropriate.…”

Section: Wavelet Thresholdingmentioning

confidence: 99%

“…US has achieved excellent patient acceptance because it is safe, fast, painless and relatively inexpensive when compared with the other imaging modalities. One of the major drawbacks of the ultrasound image is poor image quality due to speckle noise (Loizou and Pattichis, 2008). Only skilled radiologist can make effective diagnosis and hence limiting its use over a wide network.…”

Section: Introductionmentioning

confidence: 99%

Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering

Vanithamani¹

2014

Journal of Computer Science

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Speckle is a random multiplicative noise which obscures the perception and extraction of fine details in ultrasound images and speckle reduction is necessary to improve the visual quality of ultrasound images for better diagnosis. This study aims at introducing an algorithm by hybridizing bilateral filter with NeighShrink. The bilateral filter is applied before decomposition and after reconstruction of the image using discrete wavelet transform to improve the denoising efficiency and preserve the edge features effectively. The wavelet thresholding scheme NeighShrink is used for thresholding of wavelet coefficients. The algorithm is tested with synthetically speckled and real ultrasound images. Quality evaluation metrics such as Peak Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Correlation Coefficient (CoC) are used to assess the performance of the proposed method. Experimental results show that the proposed scheme improves the visual quality of ultrasound images by suppressing the speckle noise while retaining edges.

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Despeckle Filtering Algorithms and Software for Ultrasound Imaging

Cited by 73 publications

References 93 publications

Performance analysis of speckle ultrasound image filtering

Performance analysis of speckle ultrasound image filtering

Towards a Comprehensive Evaluation of Ultrasound Speckle Reduction

Speckle Reduction in Ultrasound Images Using Neighshrink and Bilateral Filtering

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