Despeckle Filtering for Ultrasound Imaging and Video, Volume I

This volume explores speckle noise in ultrasound imaging, presenting filtering techniques and evaluation frameworks to enhance image quality. Despeckle Filtering for Ultrasound Imaging and Video, Volume I is essential for clinical practices.

Speckle noise is a well-known multiplicative interference that can significantly impact the quality of images and videos, particularly in ultrasound imaging. This degradation not only affects the visual assessment by experts but also creates challenges in routine clinical practices and tele-consultations. Despeckle Filtering for Ultrasound Imaging and Video, Volume I addresses these issues by introducing robust techniques to mitigate speckle noise, thereby enhancing the overall quality of ultrasound visuals.

The primary objective of Despeckle Filtering for Ultrasound Imaging and Video, Volume I is to present a comprehensive overview of speckle noise in ultrasound images and videos. The book delves into the theoretical foundations, including the relevant equations, algorithmic procedures, and MATLABTM code for various despeckling filters. These filters encompass linear filtering, nonlinear filtering, anisotropic diffusion filtering, and wavelet filtering. A comparative evaluation framework is proposed, focusing on texture analysis, image quality metrics, and visual assessments by medical experts to determine the effectiveness of these filtering techniques.

Moreover, this volume serves as a precursor to the companion book, which will cover additional topics in detail. To support further applications, two distinct MATLABTM toolboxes have been developed for image and video despeckle filtering, along with texture analysis and quality evaluation metrics. The open-source code for these toolsets is available for download, complementing the insights provided in both volumes. This structured approach aims to improve visual observation quality and facilitate advanced automated analysis in ultrasound cardiovascular imaging.