Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 October 2017 / Approved: 30 October 2017 / Online: 30 October 2017 (09:37:59 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: J. Imaging 2018, 4, 3
DOI: 10.3390/jimaging4010003

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. 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.

Subject Areas

ultrasound image analysis; speckle noise; synthetic ultrasound images; texture features; local binary patterns; image quality assessment

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