Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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

Includes bibliographical references (pages 139-151).

1. Introduction to speckle noise in ultrasound imaging and video -- 1.1 A brief review of ultrasound imaging and video -- 1.1.1 Basic principles of ultrasound imaging and video -- 1.1.2 Ultrasound modes -- 1.1.3 Image and video quality and resolution -- 1.1.4 Limitations of ultrasound imaging and video -- 1.2 Speckle noise -- 1.2.1 Physical properties and pattern of speckle noise -- 1.2.2 Speckle noise modeling -- 1.2.3 Early attempts of despeckle filtering in different modalities and ultrasound imaging and video -- 1.2.4 Speckle noise tracking -- 1.3 An overview of despeckle filtering techniques -- 1.4 Limitations of despeckle filtering techniques -- 1.5 Guide to book contents --

2. Basics of evaluation methodology -- 2.1 Use of phantom and artificial ultrasound images and videos -- 2.2 Image and video despeckle filtering toolboxes -- 2.3 Image and video quality evaluation metrics --

3. Linear despeckle filtering -- 3.1 First-order statistics filtering (DsFlsmv, DsFwiener) -- 3.2 Local statistics filtering with higher moments (DsFlsminv1d, DsFlsmvsk2d) -- 3.3 Homogeneous mask area filtering (DsFlsminsc) --

4. Nonlinear despeckle filtering -- 4.1 Median filtering (DsFmedian) -- 4.2 Linear scaling filter (DsFca, DsFlecasort, DsFls) -- 4.3 Maximum homogeneity over a pixel neighborhood filtering (DsFhomog) -- 4.4 Geometric filtering (DsFgf4d) -- 4.5 Homomorphic filtering (DsFhomo) -- 4.6 Hybrid median filtering (DsFhmedian) -- 4.7 Kuwahara filtering (DsFKuwahara) -- 4.8 Nonlocal filtering (DsFnlocal) --

5. Diffusion despeckle filtering -- 5.1 Anisotropic diffusion filtering (DsFad) -- 5.2 Speckle-reducing anisotropic diffusion filtering (DsFsrad) -- 5.3 Nonlinear anisotropic diffusion filtering (DsFnldif ) -- 5.4 Nonlinear complex diffusion filtering (DsFncdif ) --

6. Wavelet despeckle filtering --

7. Evaluation of despeckle filtering -- 7.1 Despeckle filtering evaluation on an artificial carotid artery image -- 7.2 Despeckle filtering evaluation on a phantom image -- 7.3 Despeckle filtering evaluation on real ultrasound images and video -- 7.4 Summary findings on despeckle filtering evaluation --

8. Summary and future directions -- 8.1 Summary -- 8.2 Future directions --

A. Appendices -- A.1 Despeckle filtering, texture analysis, and image quality evaluation toolbox functions -- A.2 Despeckle filtering, texture analysis and video (VDF toolbox) quality evaluation toolbox functions -- A.3 Examples of running the despeckle filtering toolbox functions -- References -- Authors' biographies.

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It is well known that speckle is a multiplicative noise that degrades image and video quality and the visual expert's evaluation in ultrasound imaging and video. This necessitates the need for robust despeckling image and video techniques for both routine clinical practice and tele-consultation. The goal for this book (book 1 of 2 books) is to introduce the problem of speckle occurring in ultrasound image and video as well as the theoretical background (equations), the algorithmic steps, and the MATLAB code for the following group of despeckle filters: linear filtering, nonlinear filtering, anisotropic diffusion filtering, and wavelet filtering. This book proposes a comparative evaluation framework of these despeckle filters based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts. Despeckle noise reduction through the application of these filters will improve the visual observation quality or it may be used as a pre-processing step for further automated analysis, such as image and video segmentation, and texture characterization in ultrasound cardiovascular imaging, as well as in bandwidth reduction in ultrasound video transmission for telemedicine applications. The aforementioned topics will be covered in detail in the companion book to this one. 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 books.

Also available in print.

Title from PDF title page (viewed on April 26, 2015).