Abstract. Probe-based Confocal Laser Endomicroscopy (pCLE) provides physicians with real-time access to histological information during standard endoscopy procedures, through high-resolution cellular imaging of internal tissues. Earlier work on mosaicing has enhanced the potential of this imaging modality by meeting the need to get a complete representation of the imaged region. However, with approaches, the dynamic information, which may be of clinical interest, is lost. In this study, we propose a new mosaic construction algorithm for pCLE sequences based on a min-cut optimization and gradient-domain composition. Its main advantage is that the motion of some structures within the tissue such as blood cells in capillaries, is taken into account. This allows physicians to get both a sharper static representation and a dynamic representation of the imaged tissue. Results on 16 sequences acquired in vivo on six different organs demonstrate the clinical relevance of our approach.
Abstract:In this paper, a fast and accurate algorithm is proposed to recognize open and closed eye states. In the proposed algorithm, first a hierarchical preprocessing stage is used to detect eye areas. This stage employs Haar features to detect face area, color, and intensity mappings to extract eye candidate areas, and some simple geometrical relations for a final decision of the eye regions. In the second stage of the algorithm for detecting eye state, a new proposed descriptor based on a histogram of local maximum vertical derivative patterns in eye areas is extracted and applied to a support vector machine classifier. The proposed descriptor, while having low computational complexity, is defined well enough to describe eye features and hence can distinguish well between open and closed eyes. Experimental results from test images show that the proposed algorithm can correctly detect the eye state by the rate of 98.2%, which is higher than other similar algorithms.
Abstract. Content-based video retrieval has shown promising results to help physicians in their interpretation of medical videos in general and endomicroscopic ones in particular. Defining a relevant query for CBVR can however be a complex and time-consuming task for non-expert and even expert users. Indeed, uncut endomicroscopy videos may very well contain images corresponding to a variety of different tissue types. Using such uncut videos as queries may lead to drastic performance degradations for the system. In this study, we propose a semi-automated methodology that allows the physician to create meaningful and relevant queries in a simple and efficient manner. We believe that this will lead to more reproducible and more consistent results. The validation of our method is divided into two approaches. The first one is an indirect validation based on per video classification results with histopathological ground-truth. The second one is more direct and relies on perceived inter-video visual similarity ground-truth. We demonstrate that our proposed method significantly outperforms the approach with uncut videos and approaches the performance of a tedious manual query construction by an expert. Finally, we show that the similarity perceived between videos by experts is significantly correlated with the inter-video similarity distance computed by our retrieval system.
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