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Adaptive Noise Reduction in Ultrasonic Images

Authors:
Somkait Udomhunsakul

Keywords: Stationary Wavelet Transform; Multiplicative Noise Reduction; Wiener Filter; Ultrasonic Images

Abstract:
Ultrasonic image is an imaging technique that is commonly used for medical diagnostics. Unfortunately, the quality of ultrasonic images is limited, mainly due to speckle noise. Speckle noise reduction is one of the most important processes in enhancing the quality of ultrasonic images. In this paper, an adaptive noise reduction technique in wavelet domains for ultrasonic images is studied. First, a logarithmic transformation is performed on an original ultrasonic image in order to convert a multiplicative noise to an additive one. Next, Stationary Wavelet Transform is used to decompose the image resulting from the first step into four subbands. Then, an adaptive Wiener filter is applied to all detailed subbands in order to suppress additive noises in these subbands. Subsequently, the reconstructed image is derived by performing an inverse Stationary Wavelet Transform on those resulting subbands and following by an exponential transformation. The performance of the studied algorithm is evaluated objectively and subjectively on several ultrasonic images and it is compared against several well-known methods, such as Median filter, Wiener filter, Discrete Wavelet Transform based on soft thresholding, and Discrete Wavelet Transform along with Wiener filter. The results clearly demonstrate the superior performance of the studied method in terms of signal to mse ratio (S/mse), edge preservation ( ) values as well as perceptible image quality.

Pages: 74 to 79

Copyright: Copyright (c) IARIA, 2015

Publication date: June 21, 2015

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-416-9

Location: Brussels, Belgium

Dates: from June 21, 2015 to June 26, 2015