Study of various preprocessing schemes and wavelet filters for speckle noise reduction in digital speckle pattern interferometric fringes

Mirza, Saba; Kumar, Rajesh; Shakher, Chandra

doi:10.1117/1.1886749

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…For an image of size MxN pixels, it is defined in terms of the Discrete Fourier Transform (DFT) coefficients of the image in the frequency domain as follows: (8) where, F(j, k) denotes the DFT coefficient at position (j, k) [16]. Other traditionally used objective measures such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and speckle index (SI) [15] are useful only in the context of noise reduction and filtering algorithms. For example, the SI value measures the level of residual speckle noise in an image, and is therefore not a useful measure in a synthetic image modelling application.…”

Section: The Evaluation Modelmentioning

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: The Evaluation Modelmentioning

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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show abstract

“…Figure 5 shows the variations when the axial resolution m is increased in radial-polar sampling, keeping the lateral resolution fixed at n = 40. The interpolation used was Lanczos-3 [19]. As the number of sampling points along axial beams is increased, we observe that the sector image becomes smoother and less grainy.…”

Section: Synthetic Ultrasound Imagesmentioning

confidence: 95%

“…Commonly used interpolation methods are BSpline and cubic Hermite [16,17]. In [9], the authors used an interpolation scheme using the Lanczos-3 kernel [15,[18][19][20][21].…”

Section: Synthetic Ultrasound Imagesmentioning

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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“…Each sampling scheme has its own set of parametersthat can be varied over a wide range of values. The speckle noise generation algorithm also has a setLanczos-3 [18]. …”

Section: Synthetic Ultrasound Images 164mentioning

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

show abstract

Study of various preprocessing schemes and wavelet filters for speckle noise reduction in digital speckle pattern interferometric fringes

Cited by 38 publications

References 25 publications

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images

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

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