Despeckle Filtering for Ultrasound Imaging and Video, Selected Applications, 2nd Edition

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In ultrasound imaging and video visual perception is prevented by speckle multiplicative noise that deteriorates the quality. Sound reduction is therefore vital for improving the visual observation quality or as a pre-processing action for additional automatic analysis, such as image/video segmentation, texture analysis and encoding in ultrasound imaging and video. The objective of the very first book (book 1 of 2 books) was to introduce the issue of speckle in ultrasound image and video in addition to the theoretical background, algorithmic actions, 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 objective of this book (book 2 of 2 books) is to demonstrate the use of a comparative assessment structure based upon these despeckle filters (introduced on book 1) on cardiovascular ultrasound image and video processing and analysis. More particularly, the despeckle filtering examination framework is based upon texture analysis, image quality examination metrics, and visual examination by professionals. This framework is applied in cardiovascular ultrasound image/video processing on the tasks of division and structural measurements, texture analysis for differentiating between two classes (i.e. regular vs illness) and for efficient encoding for mobile applications. It is revealed that despeckle noise decrease enhanced segmentation and measurement (of tissue structure investigated), increased the texture feature range between typical and abnormal tissue, improved image/video quality examination and understanding and produced considerably lower bitrates in video encoding. In order to facilitate additional applications we have actually established in MATLABTM 2 different toolboxes that incorporate image (IDF) and video (VDF) despeckle filtering, texture analysis, and image and video quality examination metrics. The code for these toolsets is open source and these are readily available to download complementary to the two monographs.

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