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Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 131-153).

1. Introduction and review of despeckle filtering -- 1.1 An overview of despeckle filtering techniques -- 1.2 Despeckle filtering evaluation protocol -- 1.3 Selected despeckle filtering applications in ultrasound imaging and video -- 1.4 Selected despeckle filtering software -- 1.5 The image and video despeckle filtering toolboxes -- 1.6 Guide to book contents --

2. Segmentation of the intima-media complex and plaque in CCA ultrasound imaging and video following despeckle filtering -- 2.1 Segmentation of the IMC, ML and IL in ultrasound imaging and video -- 2.1.1 Methodology for the segmentation of the IMC, ML and IL in ultrasound imaging -- 2.1.2 Methodology for the segmentation of the IMC in ultrasound video -- 2.1.3 Results of the segmentation of the IMC, ML and IL in ultrasound imaging -- 2.1.4 Results of the segmentation of the IMC in ultrasound video -- 2.1.5 An overview of IMC image and video segmentation techniques -- 2.2 Segmentation of the atherosclerotic carotid plaque in ultrasound imaging and video -- 2.2.1 Methodology for the segmentation of plaque in ultrasound imaging -- 2.2.2 Methodology for the segmentation of plaque in ultrasound video -- 2.2.3 Segmentation of the plaque in ultrasound imaging -- 2.2.4 Results of the segmentation of plaque in ultrasound video -- 2.2.5 An overview of plaque segmentation techniques -- 2.3 Discussion on despeckling of the intima media complex and the plaque in imaging and video --

3. Evaluation of despeckle filtering of carotid plaque imaging and video based on texture analysis -- 3.1 Evaluation of despeckle filtering on carotid plaque imaging based on texture analysis -- 3.1.1 Distance measures -- 3.1.2 Univariate statistical analysis -- 3.1.3 kNN classifier -- 3.1.4 Image and video quality and visual evaluation -- 3.2 Discussion of image despeckle filtering based on texture analysis -- 3.3 Discussion of image despeckle filtering based on visual quality evaluation -- 3.4 Evaluation of despeckle filtering on carotid plaque video based on texture analysis -- 3.5 Discussion of video despeckle filtering based on texture analysis and visual quality evaluation -- 3.6 Evaluation of two different ultrasound scanners based on despeckle filtering -- 3.6.1 Evaluation of despeckle filtering on an ultrasound image -- 3.6.2 Evaluation of despeckle filtering on gray-value line profiles -- 3.6.3 Evaluation of despeckle filtering based on visual perception evaluation -- 3.6.4 Evaluation of despeckle filtering based on statistical and texture features -- 3.6.5 Evaluation of despeckle filtering based on image quality evaluation metrics --

4. Wireless video communication using despeckle filtering and HVEC -- 4.1 Mobile health medical video communication systems: introduction and enabling technologies -- 4.1.1 Video compression technologies -- 4.1.2 High efficiency video coding (HEVC) -- 4.2 Wireless infrastructure -- 4.2.1 4G networks confirming to IMT-advanced requirements -- 4.2.2 Worldwide interoperability for microwave access (WiMAX) -- 4.2.3 Long term evolution (LTE) -- 4.3 Selected mHealth medical video communication systems -- 4.3.1 Diagnostically driven mHealth systems -- 4.3.2 Diagnostic region(s)-of-interest -- 4.3.3 Diagnostically relevant encoding -- 4.3.4 Diagnostically resilient encoding -- 4.3.5 Reliable wireless communication -- 4.3.6 Clinical video quality assessment -- 4.4 Ultrasound video communication using despeckle filtering and HEVC -- 4.4.1 Methodology -- 4.4.2 Video coding standards comparison -- 4.4.3 Video quality assessment -- 4.4.4 Rate-distortion comparisons -- 4.4.5 Clinical video quality assessment -- 4.5 Results and discussion -- 4.5.1 Clinical ultrasound video dataset -- 4.5.2 Video compression results after despeckle filtering -- 4.5.3 Video coding standards for ultrasound video communication -- 4.5.4 Clinical evaluation -- 4.6 Concluding remarks --

5. Summary and future directions -- 5.1 Summary findings on despeckle filtering -- 5.2 Future directions -- A. Appendices -- Despeckle filtering, texture analysis, and image quality evaluation -- Toolbox functions (IDF toolbox) -- Despeckle filtering, texture analysis and video (VDF toolbox) quality -- Evaluation toolbox functions -- Examples of running the despeckle filtering toolbox functions -- Material and recording of ultrasound images and videos -- References -- Authors' biographies.

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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 MATLAB. code 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 MATLAB. 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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Title from PDF title page (viewed on August 18, 2015).