Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems

Spatial Hand Segmentation Using Skin Colour and Background Subtraction

Despite advances in hand detection and hand tracking, robust hand segmentation remains a challenging task in many gesture recognition systems. Problems can be caused by a variety of factors, such as changing illumination and background clutter. We compare the most commonly used visual cues for hand segmentation, namely skin colour and background subtraction, applied both separately and combined. All three approaches are evaluated on video-data recorded with different backgrounds and under varying lighting conditions using a standard evaluation scheme based on overlapping masks. Additionally, we introduce a new evaluation scheme based on global histograms of oriented gradient

Pilot evaluation of a ward-based automated hand hygiene training system

A novel artificial intelligence (AI) system (SureWash; GLANTA, Dublin, Ireland) was placed on a ward with 45 staff members for two 6-day periods to automatically assess hand hygiene technique and the potential effectiveness of the automated training system. Two human reviewers assessed videos from 50 hand hygiene events with an interrater reliability (IIR) of 88% (44/50). The IIR was 88% (44/50) for the human reviewers and 80% (40/50) for the software. This study also investigated the poses missed and the impact of feedback on participation (+113%), duration (+11%), and technique (+2.23%). Our findings showed significant correlation between the human raters and the computer, demonstrating for the first time in a clinical setting the potential use of this type of AI technology in hand hygiene training

Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems

Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems

Spatial Hand Segmentation Using Skin Colour and Background Subtraction

Despite advances in hand detection and hand tracking, robust hand segmentation remains a challenging task in many gesture recognition systems. Problems can be caused by a variety of factors, such as changing illumination and background clutter. We compare the most commonly used visual cues for hand segmentation, namely skin colour and background subtraction, applied both separately and combined. All three approaches are evaluated on video-data recorded with different backgrounds and under varying lighting conditions using a standard evaluation scheme based on overlapping masks. Additionally, we introduce a new evaluation scheme based on global histograms of oriented gradient

Spatial Hand Segmentation Using Skin Colour and Background Subtraction

Despite advances in hand detection and hand tracking, robust hand segmentation remains a challenging task in many gesture recognition systems. Problems can be caused by a variety of factors, such as changing illumination and background clutter. We compare the most commonly used visual cues for hand segmentation, namely skin colour and background subtraction, applied both separately and combined. All three approaches are evaluated on video-data recorded with different backgrounds and under varying lighting conditions using a standard evaluation scheme based on overlapping masks. Additionally, we introduce a new evaluation scheme based on global histograms of oriented gradient