Speckle noise removal in ultrasound images using sparse code shrinkage

Măluţan, Raul; Terebes, Romulus; Germain, Christian; Borda, Monica; Cislariu, Mihaela

doi:10.1109/ehb.2015.7391394

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Therefore, algorithms for ultrasound image filtering and analysis primarily focus on the characteristics of speckle noise and try to minimize its effects on image interpretation [8]. To analyse the effectiveness or accuracy of speckle reduction techniques, it is necessary to add controlled noise to ideal noiseless images [2]. In the absence of such noiseless ground truth images, researchers commonly use standard non-ultrasound test images (eg.…”

Section: Introductionmentioning

confidence: 99%

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

Singh

Mukundan

Ryke

2017

Communications in Computer and Information Science

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Abstract. Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. This paper discusses these aspects, presents novel algorithms for speckle simulation and modelling based on three sampling schemes, and also evaluates the quality of the outputs using image quality metrics. Detailed experimental analysis including both quantitative and subjective assessments are also presented.

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Section: Introductionmentioning

confidence: 99%

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

Singh

Mukundan

Ryke

2017

Communications in Computer and Information Science

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“…Another interesting assert of 3SD method is that it takes benefits from using an over-complete dictionary which reserves details of information and from subspace decomposition which rejects strong noise. On the contrary, some undercomplete dictionary methods [8] and some sparse shrinkage methods [9,10] might lose week information when suppressing noise. Moreover, the 3SD method is very simple with a linear retrieval operation (equation (13)).…”

Section: Discussionmentioning

confidence: 99%

Sparse signal subspace decomposition based on adaptive over-complete dictionary

Sang

Ruyet

2017

J Image Video Proc.

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This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called "sparse signal subspace decomposition" (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, well adapted to subspace decomposition over a dependent basis set, adequately reflects the intrinsic characteristic of regularity of the signal. The 3SD method combines variance, sparsity, and component frequency criteria into a unified framework. It takes benefits from using an over-complete dictionary which preserves details and from subspace decomposition which rejects strong noise. The 3SD method is very simple with a linear retrieval operation. It does not require any prior knowledge on distributions or parameters. When applied to image denoising, it demonstrates high performances both at preserving fine details and suppressing strong noise.

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“…Several new ultrasound image analysis algorithms are currently being researched for noise reduction [1][2][3], segmentation [4], registration and volume reconstruction [5]. Online ultrasound image databases are now becoming increasingly available and this has greatly benefitted researchers in obtaining reference images for testing and evaluating algorithms [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, algorithms for ultrasound image filtering and analysis primarily focus on the characteristics of speckle noise and try to minimize its effects on image interpretation [8]. To analyse the effectiveness or accuracy of speckle reduction techniques, it is necessary to add controlled noise to ideal noiseless images [2]. In the absence of such noiseless ground truth images, the evaluation of despeckling algorithms is usually done by generating highly artificial gray tone patterns (stripes, rings etc.…”

Section: Introductionmentioning

confidence: 99%

Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

2017

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Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modeling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

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Speckle noise removal in ultrasound images using sparse code shrinkage

Cited by 10 publications

References 17 publications

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

Sparse signal subspace decomposition based on adaptive over-complete dictionary

Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

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