Despeckle Filtering Toolbox for Medical Ultrasound Video

Despeckle Filtering Toolbox for Medical Ultrasound Video

Christos P. Loizou (Department of Computer Science, Intercollege, Limassol, Cyprus), Charoula Theofanous (Department of Electrical Engineering, Computer Engineering & Informatics, Cyprus University of Technology, Limassol, Cyprus), Marios Pantziaris (Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus), Takis Kasparis (Department of Electrical Engineering, Computer Engineering & Informatics, Cyprus University of Technology, Limassol, Cyprus), Paul Christodoulides (Department of Electrical Engineering, Computer Engineering & Informatics, Cyprus University of Technology, Limassol, Cyprus), Andrew N. Nicolaides (The Vascular Screening and Diagnostic Centre, Nicosia, Cyprus) and Constantinos S. Pattichis (Department of Computer Science, University of Cyprus, Nicosia, Cyprus)
DOI: 10.4018/ijmstr.2013100106

Abstract

Ultrasound medical video has the potential in differentiating between normal and abnormal tissue and structure. Ultrasound imaging is used in border identification and texture characterisation of the atherosclerotic carotid plaque in the common carotid artery (CCA), the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that are very important in the assessment of cardiovascular disease. However, visual perception is reduced by speckle noise affecting the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for increasing the visual quality or as a pre-processing step for further automated analysis, such as the video segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound video sequences. In order to facilitate this analysis, the authors have developed a video analysis software toolbox based on MATLAB® that uses video despeckling, texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA videos. The proposed software, which is based on a graphical user interface (GUI), incorporates video normalisation, 4 different despeckle filtering techniques (DsFlsmv, DsFhmedian, DsFkuwahara and DsFsrad), 65 texture features, 11 quantitative video quality metrics and objective video quality evaluation. The software was validated on 10 ultrasound videos of the CCA, by comparing its results with quantitative visual analysis performed by two medical experts. It was shown that the filters DsFlsmv, and DsFhmedian improved video quality perception (based on the expert’s assessment and the video quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular video analysis. However, exhaustive evaluation of the despeckle filtering toolbox has to be carried out by more experts on more videos.
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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.

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