Despeckle Filtering Toolbox for Medical Ultrasound Video

Introduction

Despite significant progress made in the last few years and the vast technological advancements in image and video processing, there are a number of factors that negatively influence the visual quality of images and videos, and hinder the automated analysis (Wang, 2004). These include video acquisition technologies, imperfect instruments, natural phenomena, transmission errors, and coding artifacts, which degrade the quality of video data by inducing noise (Loizou, 2008; Loizou, 2012b). Ultrasound imaging and video is a non-invasive powerful diagnostic tool in medicine, but it is degraded by a form of multiplicative noise (speckle), which makes the observation difficult (Loizou, 2005; Loizou, 2008). It is therefore of interest for the research community to investigate and apply new video despeckle filtering techniques that can increase the visual perception evaluation and further automate video analysis, thus improving the final diagnosis. These techniques are usually incorporated into integrated software medical image/video processing applications.

We propose in this study an integrated software toolbox (see also Figure 1 and Figure 2) for ultrasound video despeckling that can be utilized in video analysis. The present work is an extended version of a conference paper presented in the BIBE conference (Loizou, 2012b), where ultrasound video despeckling of the common carotid artery (CCA) was proposed. In order to quantitatively evaluate the proposed software system we applied different video despeckle filtering techniques and investigated their performance on 10 ultrasound videos of the CCA. The despeckling filtering techniques and the integrated software toolbox were also evaluated through visual perception, performed by two vascular experts, a cardiovascular surgeon, and a neurovascular specialist, before and after despeckle filtering. The video despeckle filtering techniques were further evaluated through a number of texture features and video quality metrics, which were extracted from the original and the despeckled videos.

Figure 1.

Flowchart analysis of despeckle filtering toolbox for ultrasound video

Figure 2.

The graphical user interface (GUI) of the proposed despeckle filtering toolbox for ultrasound video. The following components are shown: original video display, original video settings, despeckled video display, despeckled video settings (filter settings), number of frames for video despeckling, texture analysis and quality analysis.