#
Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns^{ †}

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

## 3. The Modeling and Speckle Simulation

_{min}and d

_{max}. The image width is denoted by w. We also denote the total number of divisions along each radial line (axial resolution) by m, and the number of division of the sector angle (lateral resolution) by n. The Cartesian coordinates of the sampled points for radial-polar sampling are given by

_{j}= d

_{min}+ j(d

_{max}− d

_{min})/(m − 1); θ

_{i}= (3π − Φ)/2 + iΦ/(n − 1)

x(i, j) = d

_{j}cos θ

_{i}+ w/2; y(i, j) = −d

_{j}sin θ

_{i}; i = 0…(n − 1); j = 0…(m − 1)

_{ij}= (3π − Φ)/2 + iΔ/d

_{j}

_{u}along an arc at distance d

_{i}given by

_{u}= Φ d

_{j}/Δ

_{min}) > 0 or f(x, y, θ

_{max}) < 0, the point (x, y) is outside the sector region.

_{min}= (3π − Φ)/2; θ

_{max}= (3π + Φ)/2

f(x, y, θ) = (x − w/2) sinθ + y cosθ

_{σ}. The complex amplitude of each pixel is initialized with the square-root of the sampled intensity value. The number of incoherent phasors M(x, y) at each pixel (x, y) is set as the value of a random number under a uniform distribution within a pre-specified range [a, b]. The incoherent phasors are generated and added M times to both the real and imaginary components of the complex value at each pixel. The noisy intensity value is then given by the amplitude of the complex number.

## 4. Synthetic Ultrasound Images

## 5. Analysis of Texture Features

#### 5.1. Local Binary Patterns (LBP)

#### 5.2. LBP Features of Synthetic Ultrasound Images

_{i}, i = 0…255. We propose the following feature vector consisting of eight LBP features for quality assessment:

_{8}, L

_{15}, L

_{120}, L

_{128}, L

_{135}, L

_{143}, L

_{240}, L

_{248}}

## 6. Experimental Analysis and Validation

#### 6.1. LBP Feature Vector for Reference Images

_{Ref}= {447.3, 597.3, 508.7, 433.7, 691.7, 435.3, 459.3, 290}

- When the parameters controlling the resolution in a sampling method are adjusted from coarse to fine, do the values of the corresponding LBP feature vector consistently tend towards the reference feature vector?
- Do the synthetic images that give feature values close to the reference vector also have consistently high subjective evaluation scores assigned by clinical experts?
- Which one of the three modelling schemes generated feature values that are closest to the reference feature vector?

#### 6.2. LBP Feature Vector for Radial-Polar Sampling

#### 6.3. LBP Feature Vector for Radial-Uniform Sampling

_{u}given in Equation (3). The effect of variation of this parameter in the quality of the synthetic images is shown in Figure 13.

_{u}are shown in the graphs in Figure 14.

_{u}of the images is increased from 10 to 120.

#### 6.4. LBP Feature Vector for Uniform-Grid Sampling

#### 6.5. Comparative Analysis of Sampling Techniques

## 7. Conclusions and Future Work

- Radial polar: when the parameter n is increased from 10 to 110
- Radial uniform: when the parameter n
_{u}is increased from 10 to 100 - Uniform grid: when the spacing parameter δ is reduced from 14 to 2

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Reference ultrasound images [7] used in our work.

**Figure 4.**Sampling models that can be used in simulating speckle noise ([13], reproduced with permission).

**Figure 5.**Effect of changing axial resolution (m) in radial-polar sampling ([13], reproduced with permission).

**Figure 6.**Effect of changing axial resolution (m) in radial-uniform sampling ([13], reproduced with permission).

**Figure 7.**Image artifacts produced by large values of sampling and noise parameters ([13], reproduced with permission).

**Figure 8.**Application of the proposed local binary patterns (LBP) features in the evaluation of filtering algorithms.

**Figure 11.**Synthetic images generated using radial polar sampling with a coarse to fine variation of lateral resolution parameter n.

**Figure 12.**Variations of LBP feature vector components with lateral resolution in radial-polar sampling. The x-axis gives the values of n. The y-axis gives the range of values of an LBP feature shown in the chart title.

**Figure 13.**Synthetic images generated using radial uniform sampling with a coarse to fine variation of lateral resolution parameter n

_{u}.

**Figure 14.**Variations of LBP feature vector components with lateral resolution in radial-uniform sampling. The x-axis gives the values of n

_{u}. The y-axis gives the range of values of an LBP feature shown in the chart title.

**Figure 15.**Synthetic images generated using uniform-grid sampling scheme with increasing values of the grid spacing parameter δ.

**Figure 16.**Variations of LBP feature vector components with grid spacing in uniform-grid sampling. The x-axis gives the values of δ. The y-axis gives the range of values of an LBP feature shown in the chart title.

**Figure 17.**Plots showing the closest matching positions of the LBP feature vector with reference vector for images generated using (

**a**) radial-polar sampling; (

**b**) radial-uniform sampling; (

**c**) uniform-grid sampling.

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**MDPI and ACS Style**

Singh, P.; Mukundan, R.; De Ryke, R.
Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns. *J. Imaging* **2018**, *4*, 3.
https://doi.org/10.3390/jimaging4010003

**AMA Style**

Singh P, Mukundan R, De Ryke R.
Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns. *Journal of Imaging*. 2018; 4(1):3.
https://doi.org/10.3390/jimaging4010003

**Chicago/Turabian Style**

Singh, Prerna, Ramakrishnan Mukundan, and Rex De Ryke.
2018. "Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns" *Journal of Imaging* 4, no. 1: 3.
https://doi.org/10.3390/jimaging4010003