| Περίληψη: | Before launching into ultrasound research, it is important to
recall that the ultimate goal is to provide the clinician with the
best possible information needed to make an accurate diagnosis.
Ultrasound images are inherently affected by speckle noise, which
is due to image formation under coherent waves. Thus, it appears
to be sensible to reduce speckle artifacts before performing image
analysis, provided that image texture that might distinguish one
tissue from another is preserved.
The main goal of this thesis was the development of novel speckle
suppression methods from medical ultrasound images in the
multiscale wavelet domain. We started by showing, through
extensive modeling, that the subband decompositions of ultrasound
images have significantly non-Gaussian statistics that are best
described by families of heavy-tailed distributions such as the
alpha-stable. Then, we developed Bayesian estimators that exploit
these statistics. We used the alpha-stable model to design both
the minimum absolute error (MAE) and the maximum a posteriori (MAP) estimators for alpha-stable signal mixed in
Gaussian noise. The resulting noise-removal processors perform
non-linear operations on the data and we relate this non-linearity
to the degree of non-Gaussianity of the data. We compared our
techniques to classical speckle filters and current
state-of-the-art soft and hard thresholding methods applied on
actual ultrasound medical images and we quantified the achieved
performance improvement.
Finally, we have shown that our proposed processors can find
application in other areas of interest as well, and we have chosen
as an illustrative example the case of synthetic aperture radar
(SAR) images.
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