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Despeckle Filtering for Ultrasound Imaging and Video, Volume II - Selected Applications, Second Edition (Paperback, 2nd Revised edition)
Loot Price: R1,330
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Despeckle Filtering for Ultrasound Imaging and Video, Volume II - Selected Applications, Second Edition (Paperback, 2nd Revised edition)
Series: Synthesis Lectures on Algorithms and Software in Engineering
Expected to ship within 10 - 15 working days
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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.
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