Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A mobile device to provide a medical image based distortion correction mechanism is described. An image analysis application, executed by the mobile device, captures a digital copy of the medical image with a camera component in response to a user action. Distortion(s) associated with the digital copy are identified by processing the digital copy with deep neural network (DNN) model(s). Next, a manual correction description is determined to correct the distortion(s) in relation to the camera component and the medical image. Furthermore, a notification to recapture the digital copy is provided to the user. The notification includes the manual correction description. Additionally, in response to another user action to correct the distortion(s) or a failure to detect the user execute manual correction(s) associated with the distortion(s) within a time period, the distortion(s) within the digital copy are corrected and the corrected digital copy is provided to the user.

Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Abstract: A system and method of processing an ultrasound signal may include, in response to receiving a set of complex frequency samples of the ultrasound signal inclusive of content data and noise at a first noise level and being used to image an anatomical region of a body, resampling multiple subsets of complex frequency samples from the set of complex frequency samples. The resampled subsets of complex frequency samples may be resampled from a first domain into a second domain. The transformed resampled subsets of complex frequency samples may be combined in the second domain to produce a result signal with a second noise level reduced from the first noise level. An image derived from the result signal may be displayed.

Abstract: A system for implementing a method for searching multimedia contents uses a pictorial edit distance to compare a search query consisting of an image to a database of images to determine the ranking of matches from closest match to least closest match between the search image and the images in the database or portions of the images in the database.

Abstract: A method and system is described for determining the distance between first and second images using an enhanced P-Edit distance metric which accounts for differences in the rotation or pose of objects identified in the images.

Abstract: A method and system for searching a database of graphical data are described. Embodiments of the invention use accelerated image-comparing techniques based on an adaptation of the Levenshtein algorithm for matching or searching one-dimensional data strings for use with recognizing pre-selected targets in graphical contents of 2D images.

Abstract: A system and method for searching multimedia databases using a pictorial language, input via an iconic interface and making use of trained ontologies, i.e., trained data models. An iconic graphic user interface (GUI) allows a user to specify a pictorial query that may include one or more one or more key-images and optional text input. Similarities between the query key-images and images in a multimedia database based on a pictorial edit distance are used to select the images that are the closest match to the query. The result images are returned to the user in order of their relevance to the query.

Abstract: A method and system for tracking one or more targets is described. The method includes the step of selecting a first template having a first image of a target and cyclically repeated steps of accumulating new images of the target, producing updated templates containing the new images, and tracking the target using the updated templates. Embodiments of the method use techniques directed to detection and mitigation of target occlusion events.

Abstract: A method and system for recognizing images are described. Embodiments of the invention apply techniques of the Levenshtein algorithm for matching or searching one-dimensional strings for comparing graphical contents of 2D images.

Abstract: A system and method for comparing images by calculating an edit distance between the images using the results of matching portions of one image to the other. The first image is divided into blocks of pixels. For every block of pixels, a closest match is found in the second image. Substitution is equated to the sum of the errors in the closet matches. Deletion is equated to the percentage of the second image not involved in any of the matching. Insertion is equated to the percentage of the second image simultaneously involved in matching more than one block. The image edit distance may then be calculated as the weighted sum of the insertion, deletion and substitution. Recognition may be done by finding a minimum edit distance between an image of an unknown object and a set of reference images.

Abstract: A system and method for incorporating relevance feedback into an image search. A multimedia database is first searched using a domain specific similarity matrix (DSM), resulting in a list of image results ranked by their similarity to a query key-image. The results are then reexamined using a personalized similarity matrix (PSM). The PSM includes image that have been selected by the user, either in previous searches or from lists of candidate images. Similarity scores are calculated between the domain relevant images in the PSM and the results images. The new similarity scores are combined with the original similarity scores and the images is re-ranked into a final results list. Any images selected by the user may then be used to update and/or expand the PSM.

Abstract: A system and method of machine learning that uses an inverse matrix of a reference similarity matrix as a transformation matrix. The reference similarity matrix relates a reference set of objects to themselves using a distance metric such as an image edit distance. The transformation matrix is used to improve the performance of query vectors in classifying or identifying digital representations of an unknown object. The query vector is a measure of similarity between the unknown object and the members of the reference set. Multiplying the query vector by the transformation matrix produces an improved query vector having improved similarity scores. The highest improved similarity score indicates the best match member of the reference set If the similarity score is high enough, the unknown object may either be classified as belonging to the same class, or recognized as being the same object, as the best match object.

Abstract: A method and system for searching a database of graphical data are described. Embodiments of the invention use accelerated image-comparing techniques based on an adaptation of the Levenshtein algorithm for matching or searching one-dimensional data strings for use with recognizing pre-selected targets in graphical contents of 2D images.

Abstract: A method and system for searching a database of graphical data are described. Embodiments of the invention use accelerated image-comparing techniques based on application of the Levenshtein algorithm for matching or searching one-dimensional data strings to recognizing pre-selected targets in graphical contents of 2D images.

Abstract: A method and system for searching a database of graphical data are described. Embodiments of the invention use accelerated image-comparing techniques based on application of the Levenshtein algorithm for matching or searching one-dimensional data strings to recognizing pre-selected targets in graphical contents of 2D images.

Abstract: A method and system for recognizing images are described. Embodiments of the invention apply techniques of the Levenshtein algorithm for matching or searching one-dimensional strings for comparing graphical contents of 2D images.

Abstract: A method and system for comparing images are described. Embodiments of the invention apply the Levenshtein algorithm for matching or searching one-dimensional data strings to recognize objects of interest in graphical contents of 2D images.