Abstract: A system according to the present disclosure may include a display unit, a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a bodily structure and fluid flowing within the bodily structure. The processor may be configured to generate vector field data including axial and lateral (or transverse) velocity components of the fluid flowing within the bodily structure, calculate velocity profiles for a plurality of locations along a wall of the bodily structure based on the axial and lateral velocity components, generate wall shear stress (WSS) visualization data based, at least in part, on the velocity profiles, and cause the display unit to concurrently display the image including the bodily structure overlaid with the WSS visualization data.

Abstract: Embodiments herein disclose systems, methods, and computer-readable media for integrating facial recognition technology into clinical workflows. An application can manage receipt of a source image, including a face of an unidentified individual, and communicate the source image for facial recognition processing. The facial recognition processing can identify an individual and demographic information associated thereto, as well as provide information to access an electronic health record server housing one or more electronic health records. The application can access health-related data for the individual from the individual's EHR and rank the accessed information to communicate to a source device in a custom, prioritized communication.

Abstract: The present invention is related to improved methods for analysis of images of the vitreous and/or retina and/or choroid obtained by optical coherence tomography and to methods for making diagnoses of retinal disease based on the reflectivity profiles of various vitreous and/or retinal and/or choroidal layers of the retina.

Abstract: A system and for determining the presence or absence of myocardial ischemia in a subject, based upon analysis of medical images of at least one region of the heart of a subject of interest, the plurality of medical images being acquired in a consecutive manner by a medical imaging modality and being a plurality of myocardial layers in a direction which is generally perpendicular to the wall of the left ventricular myocardium.

Abstract: The present disclosure relates to a method and system for registering multi-modality images. The method may include: acquiring a first image relating to one or more reference objects; acquiring a second image relating to the one or more reference objects; determining a set of reference points based on the one or more reference objects; determining a set of mapping data corresponding to the set of reference points in the first image and the second image; and determining one or more registration parameters by comparing a plurality of back-projection errors generated in a plurality of iterations that are performed based on the set of mapping data.

Abstract: Methods and systems for analyzing images are disclosed. An example method may comprise inputting one or more of a first image or a second image into a fully convolutional network, and determining an updated fully convolutional network by optimizing a similarity metric associated with spatially transforming the first image to match the second image. The one or more values of the fully convolutional network may be adjusted to optimize the similarity metric. The method may comprise registering one or more of the first image or the second image based on the updated fully convolutional network.

Abstract: There is provided an image processing apparatus. An obtaining unit obtains image data having a first dynamic range, the image data having been applied with a predetermined tone curve, the predetermined tone curve being a tone curve representing visual characteristics based on a relationship between the first dynamic range and an absolute luminance that is output. A first compression unit compresses the image data from the first dynamic range to a second dynamic range narrower than the first dynamic range while maintaining the predetermined tone curve.

Abstract: The invention relates to a method for reducing image artifacts in images of a sample captured by scanning, wherein intensity values of at least two detection regions, denoted as pixels (Pxn), are captured along respectively one row (j) in a first scanning direction. A reconstructed image is produced on the basis of the captured intensity values. According to the invention, the intensity values of the reconstructed image are summed along the rows (j) respectively scanned by a certain pixel (Pxn) and a row sum is formed in each case. A correction value of the pixel (Pxn) is ascertained on the basis of the row sums formed thus and the correction value is applied to the intensity values, captured by means of the pixel (Pxn), of the reconstructed image, as a result of which a corrected image is obtained.

Abstract: A method is executed by an electronic device in a video streaming system including a macroblock detector. The method includes receiving a video stream from a video source, selecting a frame from the video stream, detecting edges in the frame of the video stream, generating straight lines from detected edges in the frame of the video stream, identifying squares in the straight lines in the frame with polygon approximation, and outputting an indication of a detected macroblock in the image in response to identifying at least one square with characteristics of a macroblock in the frame.

Abstract: An image processing apparatus, a method for image processing, and an electronic device are provided. The image processing apparatus includes a Hardware Abstract Layer (HAL), an application (APP) coupled with the HAL, and an Algo Process Service (APS). The HAL is configured to receive a RAW image, to convert the RAW image into a YUV image, and to transmit the RAW image and/or the YUV image to the APP. The APP is configured to transmit the RAW image and/or the YUV image to the APS. The APS is coupled with the HAL via the APP and stores at least one image processing algorithm for image post-processing, and the APS is configured to process, with the at least one image processing algorithm, the RAW image and/or the YUV image.

Abstract: An image processing apparatus is disclosed. The present image processing apparatus comprises: a memory for storing a low dynamic range (LDR) image and a processor for adjusting the brightness of the LDR image by means of a pixel-specific brightness ratio identified using a first parameter, and acquiring a high dynamic range (HDR) image by adding or subtracting a pixel-specific correction value identified using a second parameter in the brightness-adjusted LDR image.

Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model, which may also be known as a machine learning system, to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

Abstract: The subject invention is a system of apparatus interconnected by communication with a computer system. The primary detect transceiver detects entities involved in a change of location event. The detection data is analyzed to provide current location of entities. That data may also be analyzed to create/confirm a digital identity of each entity. The current location is compared their prior location in relation to a designated event area and outputting a location and vector. Identity, location and vector are compared to a rule data set to determine whether the event should proceed under normal rules or modified rules. The output of the computer system includes a representation of the event region, event area, entities and current event operation condition to a human perceivable interface which may also accept commands altering the output. That output is further transmitted to event enunciators which in turn signal to the event entities whether to proceed under normal or modified rules.

Abstract: A fingerprint and proximity sensing apparatus for fingerprint recognizing and proximity sensing is provided. The fingerprint and proximity sensing apparatus includes a display panel, a fingerprint sensor, and at least one proximity sensing light emitting diode. The fingerprint sensor has an optical sensing array. The optical sensing array is configured to receive a first light emitted from the display panel and a second light emitted from the at least one proximity sensing light emitting diode at different time periods. The first light and the second light have different ranges of wave length. A sensing process is also provided.

Abstract: A computer implemented method comprises receiving one or more images of a computing environment comprising a plurality of interconnected components, analyzing the or each received image to identify each component shown in the image(s) and the connection(s) of each identified component, by identifying a set of attributes for each component from the image(s) and matching the identified attributes to attributes of known components stored in a database, obtaining a specification for each identified component, and generating a document comprising each identified component, its respective specification and the connection(s) of each identified component.

Abstract: Provided are methods for deep learning-based camera calibration, which can include receiving first and second images captured by a camera, processing the first image using a first neural network to determine a depth of the first image, processing the first image and the second image using a second neural network to determine a transformation between a pose of the camera for the first image and a pose of the camera for the second image, generating a projection image based on the depth of the first image, the transformation of the pose of the camera, and intrinsic parameters of the camera, comparing the second image and the projection image to determine a reprojection error, and adjusting at least one of the intrinsic parameters of the camera based on the reprojection error. Systems and computer program products are also provided.

Abstract: A method for operating a robotic system includes obtaining and processing first data representative of an object at a start location. An event may be detected while implementing an operation based on the image data. A gripper height that corresponds to the event may be determined. Accordingly, the method may include calculating an object height that represents a height estimate of the object.

Abstract: A method includes, identifying, in a three-dimensional (3D) anatomical image of a patient organ, multiple anatomical points corresponding to respective predefined locations on a skin of the patient organ in a first coordinate system. Multiple positions in a second coordinate system, measured by a position sensor of a position-tracking system at the respective predefined locations on the skin of the patient organ, are received. At each predefined location, a distance is calculated between a respective anatomical point and closest bone tissue of the patient organ. Weights are assigned to the predefined locations based on respective distances between the anatomical points and the closest bone tissue. The first and second coordinate systems are registered, by correlating between the positions and the respective anatomical points using the assigned weights.

Abstract: Systems and methods for use in identifying a brain condition of a subject are provided. In some aspects, a provided method includes constructing a classifier to identify a brain condition of a subject comprising steps of receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer. The method also includes defining a plurality of brain condition classes using the image data associated with one or more time frames during the acquisition period, and processing the image data to generate signatures corresponding to each of the plurality brain condition classes. The method further includes constructing the classifier using the signatures. The classifier can then be applied to determine a degree to which the subject expresses one or more disease states in order to determine a brain condition of the subject.

Abstract: Disclosed is a medical image data processing method for determining a clinical target volume for a medical treatment, wherein the method comprises executing, on at least one processor (3) of at least one computer (2), steps of: a) acquiring (S1) first image data describing at least one image of an anatomical structure of a patient; b) acquiring (S2) second image data describing an indicator for a preferred spreading direction or probability distribution of at least one target cell; c) determining (S3) registration data describing a registration of the first image data to the second image data by performing a co-registration between the first image data and the second image data using a registration algorithm; d) determining (S4) gross target region data describing a target region in the at least one image of the anatomical structure based on the first image data; e) determining (S5) margin region data describing a margin around the target region based on the gross target region data; f) determining (S6) clini