Abstract: The present invention concerns a device for producing a “digital video classifier” configured to determine the quality of cleanliness of one or more segments of the digestive tube in a video capsule endoscopy (VCE) of a subject, comprising: a VCE allowing the acquisition of videos of segments of the digestive tube, video storage means, coupled with the VCE, an “image database” with images extracted from VCE exams, a “video database” with videos extracted from VCE exams, calculating means connected to the video storage means, and to the databases, and configured for performing a statistical learning for the generation of a “digital image classifier” from the “image database” and which classifies the images in adequate cleanliness or non-adequate cleanliness; generating the “digital video classifier” capable of classifying a video of a subject as being of adequate cleanliness or of non-adequate cleanliness of one or more segments of the digestive tube.

Abstract: The invention relates a method (100) and a device (200) for analyzing a video sequence captured by Small Bowel (SB) Capsule Endoscopy (CE) devices, when placed in a patient's body during a SBCE video examination, the clinical setting of the patient being chosen from overt Obscure GastroIntestinal Bleeding (OGIB), occult OGIB, or suspected Crohn's Diseases (CD), by calculating a level of “clinical relevance” of the findings detected by the SBCE devices according to the chosen clinical setting, comprising: (a) collecting (110), by a memory, at least one video sequence captured during a SBCE video examination; (b) automatically detecting (120), by a processor, from the video sequence, at least one image comprising at least one abnormal finding in the Small Bowel detected among sixteen types of SB findings; (c) counting (130), by the processor, the abnormal detected findings; (d) classifying (140), by the processor the abnormal detected findings according to predetermined rules; and (e) outputting (150), by a di

Abstract: A method for performing real-time detection and displaying of polyps in optical colonoscopy, includes a) acquiring and displaying a plurality of real-time images within colon regions to a video stream frame rate, each real-time image comprising a plurality of color channels; b) selecting one single color channel per real-time image for obtaining single color pixels; c) scanning the single color pixels across each the real-time image with a sliding sub-window; d) for each position of the sliding sub-window, extracting a plurality of single color pixels local features of the real-time image; e) passing the extracted single color pixels local features of the real-time image through a classifier to determine if a polyp is present within the sliding sub-window; f) real-time framing on display of colon regions corresponding to positions of the sliding sub-window wherein polyps are detected. A system for carrying out such a method is also provided.

Abstract: The invention relates to a method for producing an image numerical classifier, to automatically determine the visualization quality of endoscopy videocapsule images of segments of the digestive tract, comprising a step of acquiring a video in the digestive tract by a videocapsule; a step of extracting images from the video; a so-called “ground truth” step of clinically evaluating the visualization quality of the images based on medical criteria, a step of selecting an initial set of “adequate” visualization images and “inadequate” visualization images, a step of calculating at least one numerical parameter relating to at least one of the medical criteria, a statistical machine learning step to produce the numerical classifier.

Abstract: The invention relates to a method for producing an image numerical classifier, to automatically determine the visualization quality of endoscopy videocapsule images of segments of the digestive tract, comprising a step of acquiring a video in the digestive tract by a videocapsule; a step of extracting images from the video; a so-called “ground truth” step of clinically evaluating the visualization quality of the images based on medical criteria, a step of selecting an initial set of “adequate” visualization images and “inadequate” visualization images, a step of calculating at least one numerical parameter relating to at least one of the medical criteria, a statistical machine learning step to produce the numerical classifier.

Abstract: A method for performing real-time detection and displaying of polyps in optical colonoscopy, includes a) acquiring and displaying a plurality of real-time images within colon regions to a video stream frame rate, each real-time image comprising a plurality of color channels; b) selecting one single color channel per real-time image for obtaining single color pixels; c) scanning the single color pixels across each the real-time image with a sliding sub-window; d) for each position of the sliding sub-window, extracting a plurality of single color pixels local features of the real-time image; e) passing the extracted single color pixels local features of the real-time image through a classifier to determine if a polyp is present within the sliding sub-window; f) real-time framing on display of colon regions corresponding to positions of the sliding sub-window wherein polyps are detected. A system for carrying out such a method is also provided.