Adaptive ultrasonic speckle reduction based on the slope-facet model

Huang, Huan-Chao; Chen, Jau-Yuen; Wang, Sheng‐De; Chen, Chung-Ming

doi:10.1016/s0301-5629(03)00927-x

Cited by 27 publications

(15 citation statements)

References 30 publications

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“…As a result, the laser digital speckle interferometry, which was established based on the understanding of the statistical and movement properties of laser speckles, has been widely used in fundamental scientific research and engineering [3][4][5]. As a contrast, the studies of ultrasonic speckles have been limited in the field of medical ultrasonics for a long time, especially in B-scan imagings [6,7]. When ultrasonic waves are incident upon a rough surface, ultrasonic speckles are formed in the backscattering space too.…”

Section: Introductionmentioning

confidence: 99%

In-plane and out-of-plane displacement measurement by ultrasonic speckle correlation method (USCM)

Zhu

Zheng

et al. 2006

Arch Appl Mech

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When an object moves, ultrasonic speckles backscattered from its surface will follow the object to move. From the Kirchhoff diffraction theory and the correlation principles of random signals, the necessary condition for keeping the correlativity between the speckle fields before and after the objective displacement was deduced. Based on this condition, the formulas for the relationship between the speckle displacement and the objective displacement were obtained. Practical measurement was performed. Ultrasonic digital speckle correlation method was used to measure the in-plane displacement and out-of-plane displacement of an object. The displacements of the objective surface were evaluated after the displacements of the speckles were determined.This method can be also used to measure the displacements of an inner objective surface. A mountain-climbing search method was proposed, which enabled us to find the maximum correlation coefficient in the correlation operation quickly and efficiently. The experimental results showed good agreement with the theoretical predictions.

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Section: Introductionmentioning

confidence: 99%

In-plane and out-of-plane displacement measurement by ultrasonic speckle correlation method (USCM)

Zhu

Zheng

et al. 2006

Arch Appl Mech

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“…It has been used to create synthetic images in [1,10]. Empirical models were also used [5,6,7,10,11,14] to validate denoising algorithms. In both methods, images produced lack realism because the image acquisition model has not been considered or fully applied.…”

Section: Introductionmentioning

confidence: 99%

“…Speckle simulation algorithms rely on this false hypothesis [5,6,11], adding speckle on a per-pixel basis without modifying the underlying resolution or geometry to simulate a sectoral scan. Despite a few algorithms [1,7,10,14] that introduce correlation between neighboring pixels, none, to our knowlege, tries to reproduce a sectoral scan of a scene or uses interpolation to augment resolution like a real ultrasonograph would do.…”

Section: Introductionmentioning

confidence: 99%

Speckle Simulation Based on B-Mode Echographic Image Acquisition Model

Perreault

Auclair-Fortier

2007

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

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This paper introduces a novel method to simulate Bmode medical ultrasound speckle in synthetic images. Our approach takes into account both the ultrasound image formation model and the speckle formation model. The algorithm first modifies the geometry of an ideal noiseless image to match that of a sectoral B-mode ultrasonogram, by subsampling a grid of pixels to simulate the acquisition and quantization steps of image formation. Then, speckle is added by simulating a random walk in the plane of the complex amplitude, according to the Burckhardt speckle formation model. We finally interpolate the noisy subsampled pixels in order to fill the space introduced by the sampling step and recover a complete image, as would a real ultrasonograph. Synthetic speckle images generated by this method are visually and theoritically very close to real ultrasonograms.

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“…Speckle mitigation efforts in the past have included various compounding techniques as well as digital signal processing based approaches such as those using local statistics, wavelet transforms, and a combination of those (Achim et al 2001;Chen et al 1996;Cincotti et al 2001;Gupta et al 2005;Hao et al 1999;Huang et al 2003;Dutt and Greenleaf 1991;Ghofrani et al 2001;Stippel et al 2005). Some of these used statistical information on the speckle to make filters robust and adaptive while others were purely ad hoc.…”

Section: Introductionmentioning

confidence: 99%

Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter

Shankar

2006

Phys. Med. Biol.

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Speckle poses serious problems in the interpretation of ultrasound images. It reduces contrast and resolution, making it difficult to identify the presence of abnormalities in B mode images. Using a recently proposed compound probability density function (pdf) for the statistics of the backscattered ultrasonic signals, an adaptive filter for speckle reduction is implemented and tested on B mode images of a tissue mimicking phantom. Results suggest that the adaptive filter based on a maximum likelihood approach improves the ability to classify targets in images while retaining the details in the original unprocessed image.

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Adaptive ultrasonic speckle reduction based on the slope-facet model

Cited by 27 publications

References 30 publications

In-plane and out-of-plane displacement measurement by ultrasonic speckle correlation method (USCM)

In-plane and out-of-plane displacement measurement by ultrasonic speckle correlation method (USCM)

Speckle Simulation Based on B-Mode Echographic Image Acquisition Model

Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter

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