A novel algorithm based on wavelet-trilateral filter for de-noising medical ultrasound images

Zhang, Ju; Cui, Wenqiang; Wu, Lili; Lin, Guangkuo; Cheng, Yun

doi:10.1109/ccdc.2016.7531648

Cited by 5 publications

(2 citation statements)

References 11 publications

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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%

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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Section: Introductionmentioning

confidence: 99%

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

2017

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“…Several new ultrasound image analysis algorithms are currently being researched for noise 30 reduction [1][2][3], segmentation [4], registration and volume reconstruction [5]. Online ultrasound 31 image databases are now becoming increasingly available and this has greatly benefitted researchers 32 in obtaining reference images for testing and evaluating algorithms [5][6][7].…”

Section: Introduction 27mentioning

confidence: 99%

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

Singh¹,

Mukundan²,

Ryke³

2017

Preprint

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Speckle noise reduction is an important area of research in the field of ultrasound image 10 processing. Several algorithms for speckle noise characterization and analysis have been recently 11 proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods 12 as they can be used to generate a variety of speckle noise models under different interpolation and 13 sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of 14 the chosen methods. However, not much work has been done in the area of modelling synthetic 15 ultrasound images, and in simulating speckle noise generation to get images that are as close as 16 possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images 17 is the requirement for extensive quality assessment for ensuring that they have the texture 18 characteristics and gray-tone features of real images. This paper presents texture feature analysis of 19 synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a 20 set of LBP features for image quality assessment. Experimental results presented in the paper 21 clearly show how these features could provide an accurate quality metric that correlates very well 22 with subjective evaluations performed by clinical experts. 23Keywords: ultrasound image analysis; speckle noise; synthetic ultrasound images; texture features; 24 local binary patterns; image quality assessment 25 26

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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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A novel algorithm based on wavelet-trilateral filter for de-noising medical ultrasound images

Cited by 5 publications

References 11 publications

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

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

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

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

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