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.
Speaker recognition in a multi-speaker environment is a complex listening task that requires effort to be solved. Especially people with hearing loss show an increased listening effort in demanding listening situations compared to normal hearing people. However, a standardized method to quantify listening effort does not exist yet. Recently we have shown a possible way to determine listening effort objectively. The aim of this study was to validate the proposed objective measure in a challenging, true-to-life listening situation, and to get an insight on the influence of different hearing aid (HA) settings on the listening effort using the proposed measure. To achieve this we investigated the influence of four different HA settings and two different listening task difficulties (LTD) on the listening effort of people with hearing loss in a selective, real-speech listening task. HA setting A, B and C all had an adaptive compression with static characteristic, but differed in the gain and compression settings (more and less gain and more and less linear). Setting D had an adaptive compression whose characteristic was situation-dependent. To quantify the listening effort the ongoing oscillatory EEG activity was recorded as the basis to calculate the objective measure (OLEosc). By way of comparison a subjective listening effort score was determined on an individual basis (SLEscr). The results show that the OLEosc maps the SLEscr well in every of the tested conditions. Furthermore, the results also suggest that OLEosc might be more sensitive to small variances in listening effort than the employed subjective rating scale.
We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices-according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.
We address two quality issues in endoscopy videos originating from: (a) Colour Channel Misalignment: In current endoscope systems, the colour channel images are most commonly acquired sequentially at different time instances. Whenever the camera moves significantly between acquisition instances, the colour channels get misaligned. This misalignment degrades the quality of the video due to the appearance of stroboscopic artefacts. (b) Specular Highlights: The surfaces of human organs, visualised in endoscopy videos, usually have a glossy appearance which causes specular highlights in the images. For many image analysis algorithms, these distinct and bright artefacts can become a significant source of error. In this paper, we propose two novel algorithms to remove the aforementioned artefacts. The colour channel misalignment artefacts are removed by estimating the camera motion in the time interval between the acquisition of two colour channel images. The issue of specular highlights is addressed using: (i) a segmentation method based on nonlinear filtering and colour image thresholding and (ii) a fast inpainting method. Both algorithms are evaluated using image sets extracted from colonoscopy videos. The colour channel realignment algorithm achieved a success rate of 86% and 78% in image sets with artificial and real misalignment, respectively. When specular highlights are a priori removed by the proposed algorithm, these success rates increase to 92% and 84%, respectively. The specular highlight removal algorithm achieved an accuracy of 91.99%.
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