In ultrasound imaging and video visual perception is hindered by speckle multiplicative noise
that degrades the quality. Noise reduction is therefore essential for improving the visual
observation quality or as a pre-processing step for further automated analysis such as image
video segmentation texture analysis and encoding in ultrasound imaging and video. The goal of
the first book (book 1 of 2 books) was to introduce the problem of speckle in ultrasound image
and video as well as the theoretical background algorithmic steps and the MatlabTM for the
following group of despeckle filters: linear despeckle filtering non-linear despeckle
filtering diffusion despeckle filtering and wavelet despeckle filtering. The goal of this
book (book 2 of 2 books) is to demonstrate the use of a comparative evaluation framework based
on these despeckle filters (introduced on book 1) on cardiovascular ultrasound image and video
processing and analysis. More specifically the despeckle filtering evaluation framework is
based on texture analysis image quality evaluation metrics and visual evaluation by experts.
This framework is applied in cardiovascular ultrasound image video processing on the tasks of
segmentation and structural measurements texture analysis for differentiating between two
classes (i.e. normal vs disease) and for efficient encoding for mobile applications. It is
shown that despeckle noise reduction improved segmentation and measurement (of tissue structure
investigated) increased the texture feature distance between normal and abnormal tissue
improved image video quality evaluation and perception and produced significantly lower
bitrates in video encoding. Furthermore in order to facilitate further applications we have
developed in MATLABTM two different toolboxes that integrate image (IDF) and video (VDF)
despeckle filtering texture analysis and image and video quality evaluation metrics. The code
for these toolsets is open source and these are available to download complementary to the two
monographs.
