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Proceedings Paper

Enhanced method for reducing ultrasound speckle noise using wavelet transform
Author(s): Aiman Albert Abdel-Malek; Aaron M. Dentinger
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Paper Abstract

This paper presents the results of the development of an adaptive method for reducing signal-dependent noise, such as speckle noise, in a coherent imaging system signal, such as in medical ultrasound imaging. Speckle noise is filtered using nonlinear adaptive thresholding of received echo wavelet transform coefficients. Filtering speckle noise in ultrasound imaging enhances the resultant image by improving the signal-to-noise ratio. This method includes the steps of transforming the imaging system signal using discrete wavelet transformation to provide wavelet transform coefficients for each of the wavelet scales having different levels of resolution ranging from a finest wavelet scale to a coarsest wavelet scale; deleting the wavelet transform coefficients representing the finest wavelet scale; identifying, for each wavelet scale other than the finest wavelet scale, which of the wavelet transform coefficients are related to noise and which are related to a true signal through the use of adaptive nonlinear thresholding; selecting those wavelet transform coefficients which are identified as being related to a true signal; and inverse transforming the selected wavelet transform coefficients using an inverse discrete wavelet transformation to provide an enhanced true signal with reduced noise. This method is shown to improve the signal-to-noise ratio by 2 - 5 dB in digital ultrasound images of real and phantom objects for a range of thresholding levels while preserving the contrast differences between regions and maintaining feature edges. The filtered images have an enhanced apparent contrast resulting from the reduction in the speckle noise and the preservation of the contrast differences.

Paper Details

Date Published: 30 October 1997
PDF: 12 pages
Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); doi: 10.1117/12.279682
Show Author Affiliations
Aiman Albert Abdel-Malek, GE Corporate Research and Development Ctr. (United States)
Aaron M. Dentinger, GE Corporate Research and Development Ctr. (United States)

Published in SPIE Proceedings Vol. 3169:
Wavelet Applications in Signal and Image Processing V
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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