Abstract: A method is provided for enhancing video images in a medical device. The method includes receiving a first image frame and a second image frame from one or more image sensors. The first image sub-blocks are generated by dividing the first image frame. At least one curve to the first image sub-blocks are associated based on one or more look-up tables. A target in at least one of the first image sub-blocks is identified. Second image sub-blocks are generated by dividing the second image frame. At least one curve is associated to the second image sub-blocks based on the one or more look-up tables. The target is identified in at least one of the second image sub-blocks. Histogram enhanced images of the target in the first image sub-blocks and the second image sub-blocks are generated. A video image stream is generated based on the histogram enhanced images of the target.

Abstract: The medical image processing apparatus includes: an image acquiring unit that acquires a medical image; a classification unit that classifies, on the basis of the medical image acquired by the image acquiring unit, the medical image or a region of interest included in the medical image; a notification information generating unit that generates, in accordance with a classification result of the classification, first notification information for display and second notification information for storage, the second notification information differing from the first notification information; and a storage that stores the medical image and the second notification information.

Abstract: We present a new method to automatically sample the tumour/stroma interface zone (IZ) from microscopy image analysis data. It first delineates the tumour edge using a set of explicit rules in grid-subsampled tissue areas; then the IZ of controlled width is sampled and ranked by the distance from the edge to compute TIL density profiles across the IZ. From this data, a set of novel Immunogradient indicators are computed to reflect TIL “gravitation” towards the tumour. We applied the method on CD8 immunohistochemistry images of surgically excised breast and colorectal cancers to predict overall patient survival. In both patient cohorts, we found strong and independent prognostic value of the Immunogradient indicators, outperforming methods currently available. We conclude that data-driven, automated, human operator-independent IZ sampling enables precise spatial immune response measurement in the tumour/host interaction frontline for prediction of disease and therapy outcomes.

Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure.

Abstract: An image processing apparatus for determining a radiation field of radiation from a radiation image comprises: a prediction unit that predicts, based on prior information, a degree that each position of the radiation image becomes a predetermined portion of the radiation field and generating a prediction result representing a distribution of the predicted degrees; an analysis unit that analyzes the radiation image to determines the degree that each position of the radiation image becomes the predetermined portion of the radiation field and generating an analysis result representing the distribution of the predicted degrees; and a recognition unit that decides the predetermined portion of the radiation field based on a combining result obtained by combining the prediction result and the analysis result.

Abstract: A method for analysing images in a computer aided diagnosis system (CADx) to provide a first image analysis score and a second image analysis score for an image is described. The method comprising; receiving an input comprising at least one input image showing all or part of the lungs of a subject; analysing the input to calculating a first image analysis value and a second image analysis value for the input and processing the calculated values to generate corresponding first image analysis and second image analysis scores and outputting at least one of the first image analysis score and the second image analysis score for the subject. A computer aided diagnosis system (CADx) and a method of training a computer aided diagnosis system are also described.

Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.

Abstract: Systems and methods are provided for imaging coronary trees via registration of angiographic projections with computed tomographic data. In one example, a method for imaging a coronary artery of interest in a patient may include acquiring computed tomography (CT) imaging data depicting a coronary tree, acquiring a single angiographic projection of the coronary artery of interest, registering the single angiographic projection to the CT imaging data, and determining a fractional flow reserve (FFR) of the coronary artery of interest based on the single angiographic projection registered to the CT imaging data.

Abstract: A method for generating a panoramic layer image of an object to be imaged, by employing a 2D panoramic X-ray device, the object being imaged by projecting X-rays generated by an X-ray source through the object in a projection direction and recording the X-rays with an X-ray detector. Several 2D X-ray projection images are continuously recorded from various imaging directions while the X-ray source and the X-ray detector move around the object. At least one panoramic layer image is calculated from the recorded 2D X-ray projection images using a reconstruction method. The panoramic layer image is calculated by selecting and employing at least one partial area from at least two 2D X-ray projection images in relation to an active sensor area.

Abstract: The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation.

Abstract: Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring, with an x-ray detector, an x-ray image of a subject, determining a transformation that minimizes anti-scatter grid artifacts in the x-ray image, correcting the x-ray image according to the transformation to generate a corrected image, and outputting the corrected image. In this way, artifacts arising from a misalignment of an anti-scatter grid between the calibration and the acquisition may be reduced.

Abstract: A diagnosis assisting system, a diagnosis assisting method, and a diagnosis assisting program provide information that contributes to an objective diagnosis, with a server of a diagnosis assisting system including a learned model acquisition unit that acquires a plurality of learned models, each of which is generated by machine learning, inputs information based on an image obtained by imaging a diagnostic target, and outputs information that indicates a diagnosis result of the diagnostic target; an image acquisition unit that acquires an image to be analyzed; a calculation unit that performs calculation based on the plurality of learned models acquired on the image acquired, and calculates one piece of information about a diagnosis using the plurality of learned models; and an output unit that outputs the information calculated by the calculation unit.

Abstract: According to one embodiment, a medical information processing system includes processing circuitry. The processing circuitry acquires an ultrasound image of a subject. The processing circuitry acquires a first modality image of the subject, the first modality image differing from the ultrasound image. The processing circuitry acquires an imaging position for the ultrasound image. The processing circuitry generates diagnosis support information for the subject based on the ultrasound image, the imaging position for the ultrasound image, and the first modality image.

Abstract: The present invention makes it possible to estimate, with high precision, a candidate region indicating each of multiple target objects included in an image. A parameter determination unit 11 determines parameters to be used when detecting a boundary line of an image 101 based on a ratio between a density of boundary lines included in an image 101 and a density of boundary lines in a region indicated by region information 102 indicating the region including at least one of the multiple target objects included in the image 101. A boundary line detection unit 12 detects the boundary line in the image 101 using the parameter. For each of the multiple target objects included in the image 101, the region estimation unit 13 estimates the candidate region of the target object based on the detected boundary line.

Abstract: Various examples are directed to apparatus and methods for enhancing an X-ray medical image. In one example, a method for X-ray dental images enhancement is provided. The method includes receiving an input image and applying the noise reduction trough Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on the sliding window, which is calculated following a recursive two-dimensional scheme; contrast enhancement to the filtered image, including using recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (SW-CLAHE) utilizing a Truncation Threshold Surface (TTS) for calculation of the truncation threshold for the local histogram in each sliding window. The method also includes applying sharpness enhancement to the input image, including using Fast Recursive Adaptive Unsharp Masking (FRA-UM) utilizing a sliding window with two or one-dimensional recursion, to obtain a sharpened image.

Abstract: One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

Abstract: A medical image region screening method and apparatus and a storage medium are provided. The method includes: obtaining a medical image of biological tissue, segmenting tissue regions of a plurality of tissue types from the medical image, selecting, from the tissue regions of the plurality of tissue types based on types of capturing positions of the medical image, a reserved region, obtaining a positional relationship between the reserved region and a predicted lesion region in the medical image; and screening for the predicted lesion region in the medical image based on the positional relationship, to obtain a target lesion region.

Abstract: Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.

Abstract: Technology described herein can verify readiness of a customer tenant/customer for cloud provisioning based on the customer tenant. An example method comprises collecting, by a system comprising a processor, data based on user input from a customer tenant via a user interface, the data comprising information of a configuration setting that is to be used for cloud provisioning of a device based on the configuration setting. The method comprises, in response to collecting the configuration setting, analyzing, by the system, the configuration setting by comparing the configuration setting with respect to a selected rules check. The method comprises, in response to the analyzing of the configuration setting, determining, by the system, whether the configuration setting is acceptable for use in the cloud provisioning based on the configuration setting.

Abstract: Provided is a method for acquiring labeled data, including acquiring a 3D-3D registration sample, wherein the 3D-3D registration sample includes a sample 3D image, a reference 3D image and 3D-3D registration data, and the 3D-3D registration data includes correspondence data between the sample 3D image and the reference 3D image; generating at least one dual-2D-image based on the sample 3D image, wherein each dual-2D-image is a combination of 2D projection images of the sample 3D image in two different directions; and generating 2D-3D registration data corresponding to each dual-2D-image based on the 3D-3D registration data, wherein each 2D-3D registration data includes correspondence data between the dual-2D-image and the reference 3D image.

Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.

Abstract: An X-ray imaging apparatus includes an imaging device configured to capture a camera image of a target; a controller configured to stitch a plurality of X-ray images of respective divided regions of the target to generate one X-ray image of the target; and a display configured to display a settings window that provides a GUI for receiving a setting of an X-ray irradiation condition for the respective divided regions, and display the camera image in which positions of the respective divided regions are displayed.

Abstract: A radiographic imaging system having a number of useable digital radiographic detectors selectively activates one of the detectors to capture a radiographic image. The DR detectors each include a movement detector configured to transmit a movement signal when a movement of the corresponding detector is sensed. A portion of the system is disabled in response to receiving a movement signal transmitted from a motion sensor in a detector other than the active detector.

Abstract: An analysis method is for automatically determining radiological result data from radiology data sets. In an embodiment, the method includes provisioning a first radiology data set at least based on X-ray data of a first X-ray energy spectrum; provisioning at least one second radiology data set at least based on X-ray data of a second X-ray energy spectrum; provisioning an analysis unit including a neural network, to analyze radiology data sets including an input layer with a plurality of cells, a number of intermediate layers and an output layer representing a radiological result; analyzing the first radiology data set and the at least one second radiology data set, at least subsets of the first radiology data set and the at least one second radiology data set being assigned to different cells of the input layer for joint processing in the neural network; and acquiring result data on the output layer.

Abstract: Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes memory to store instructions and a processor. The example processor can detect a lesion from an image of a prostate gland and generate a mapping of the lesion from the image to a sector map, the generating the mapping of the lesion comprising identifying a depth region of the lesion, wherein the depth region indicates a location of the lesion along a depth axis. The processor can also provide the sector map comprising a representation of the lesion within the prostate gland mapped from the image to the sector map.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: Provided is a dental image registration device comprising: an outermost boundary detection unit for detecting, from first teeth image data, a first outermost boundary region which is the outermost boundary region of dentition, and detecting, from second teeth image data, a second outermost boundary region which is the outermost boundary region of the dentition; and an image registration unit which registers the first and second teeth image data on the basis of a first inscribed circle inscribed within the first outermost boundary region and a second inscribed circle inscribed within the outermost boundary region, or registers the first and second teeth image data on the basis of a first central point of the first outermost boundary region and a second central point of the second outermost boundary region.

Abstract: Systems and methods for determining a target region of interest (ROI) for image registration is provided. The methods may include obtaining a target image of a subject including a target portion of the subject and obtaining feature information related to a movement of one or more feature portions of the subject. The methods may further include identifying, from the one or more feature portions, one or more reference portions of the subject based on the feature information. A position of the target portion of the subject is unrelated to a movement of the one or more reference portions. The methods may further include determining, in the target image, the target ROI based on corresponding feature information related to the one or more reference portions.

Abstract: Radiation beam alignment for a LINAC including (1) for each beam alignment parameter value of a set: (a) with a beam alignment parameter of a LINAC set to the beam alignment parameter value, using a gantry to generate a radiation beam; (b) using an imaging device to acquire a radiation transmission image indicative of a radiation field of the radiation beam after passing by a radiation opaque marker; (c) determining a location of a beam axis of the radiation beam and a center of a shadow of the marker based on the radiation transmission image; and (d) determining a target-to-beam-axis distance between the location of the beam axis and the center of the shadow of the radiation opaque marker; and (2) determining an optimum beam alignment parameter value based on the beam alignment parameter values and the target-to-beam-axis distances determined with the LINAC set to the beam alignment parameter values.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: Computer systems and computer-implemented analysis methods may be used for assistance in planning and/or performing a medical procedure, such as percutaneous nephrolithotomy or percutaneous nephrolithotripsy. The method may include receiving one or more radiographic images of an anatomical structure of a patient, generating a display of the radiographic image(s), generating at least one request for user input to identify features of the anatomical structure, receiving user input identifying the features of the anatomical structure, identifying at least one access plan based on the received user input, and generating a display of the identified access plan(s) associated with the radiographic image(s). The method may include generating a patient template that indicates an insertion site according to the identified access plan(s).

Abstract: Various methods and systems are provided for mammogram imaging. In one example, a method for an x-ray mammography system includes determining, based on a pre-exposure image of a subject acquired with the x-ray mammography system before acquisition of one or more main images, that the subject has an implant; automatically adjusting one or more imaging parameters in response to determining that the subject has the implant; and acquiring the one or more main images with the adjusted one or more imaging parameters.

Abstract: A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

Abstract: In acquisition of spatial transcriptomic information, a plurality of images representing a common field of view of a sample are obtained and registered. Each pixel of the registered images is decoded by identifying a code word from a plurality of code words in a code book that provides a best match to data values in the plurality of registered images for the pixel. For each code word identified as a best match and each pixel, whether a bit ratio for an image word for the pixel meets a threshold for the code word is determined. The image word is formed from the data values in the plurality of registered images for the pixel. For at least one pixel that is determined to meet the threshold, a gene associated with the code word is determined. Pixels for which the bit ratio does not meet the threshold are screened.

Abstract: A medical care support device includes an acquisition unit that acquires medical information including medical image data representing a medical image obtained by imaging a bone of a subject and age information representing an age of the subject in a case where the medical image is imaged, a derivation unit that derives presence or absence of a fracture in a growth plate of the subject, based on the medical information acquired by the acquisition unit and a learned model learned in advance using a plurality of pieces of learning medical information including the medical image data and the age information, and a warning information output unit that outputs warning information representing a warning in a case where the growth plate of the subject is fractured.

Abstract: A compression plate of a mammography apparatus has an upper surface which is irradiated with radiation, a lower surface which is opposite to the upper surface and comes into contact with the breast, and a surface which is parallel to the upper surface between the upper surface and the lower surface. In the compression plate, a compression plate scale for identifying an in-plane position of each surface is given to any one of the surfaces. A first display control unit of a console performs control to display a radiographic image captured by the mammography apparatus on a display unit. A second display control unit of the console performs control to display an image scale which indicates a position on the radiographic image corresponding to the in-plane position of the compression plate on the radiographic image displayed on the display unit.

Abstract: A system for determining accuracy of a surgical procedure to implant an implant on a patient bone. The system including at least one computing device configured to perform the following steps. Receive preoperative patient data including preoperative images of the patient bone and planned implant position and orientation data. Receive postoperative patient data including postoperative images of the patient bone and an implant implanted on the patient bone. Segment the patient bone and the implant from the postoperative images of the patient bone and the implant. Register separately the patient bone and the implant from the postoperative images to the patient bone from the preoperative images. And compare an implanted position and orientation of the implant from the postoperative images relative to the patient bone from the preoperative images to the planned implant position and orientation data relative to the patient bone from the preoperative images.

Abstract: Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and/or a list of lesion candidates generated by a lesion identification network.

Abstract: A method for calibrating an image sensor begins by illuminating a portion of the image sensor with an input light spectrum, where the input light spectrum includes light of known wavelength and intensity. The method continues by sampling an output for each optical sensor of the image sensor, where each optical sensor is associated with one or more optical filters and where each optical filter being associated with a group of optical filters of a plurality of groups of optical filters. Each optical filter of a group of optical filters is configured to pass light in a different wavelength range and at least some optical filters in different groups of the plurality of groups of optical filters are configured to pass light in substantially a same wavelength range.

Abstract: A computer-implemented image analysis method and system. The method comprises: quantifying one or more features segmented and identified from a medical image of a subject; extracting clinically relevant features from non-image data pertaining to the subject; assessing the features segmented from the medical image and the features extracted from the non-image data with a trained machine learning model; and outputting one or more results of the assessing of the features.

Abstract: A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Abstract: A method, applied to an apparatus for mammographic multi-view mass identification, includes receiving a main image, a first auxiliary image, and a second auxiliary image. The main image and the first auxiliary image are images of a breast of a person, and the second auxiliary image is an image of another breast of the person. The method further includes detecting the nipple location based on the main image and the first auxiliary image; generating a first probability map of the main image based on the main image, the first auxiliary image, and the nipple location; generating a second probability map of the main image based on the main image, the second auxiliary image, and the nipple location; and generating and outputting a fused probability map based on the first probability map and the second probability map.

Abstract: A robotic catheter procedure system includes a bedside system and a workstation. The bedside system includes an actuating mechanism configured to engage and to impart movement to a percutaneous device. The workstation includes a user interface and a control system configured to be operatively coupled to the user interface, the bedside system, and a medical imaging system. The control system is responsive to a first input and to a second input, and the user interface receives the second input from a user. The control system is configured to generate a first control signal to the medical imaging system based on the first input, and the medical imaging system captures at least one image in response to the first control signal. The control system is configured to generate a second control signal to the actuating mechanism based on the second input, and the actuating mechanism causes movement of the percutaneous device in response to the second control signal.

Abstract: An apparatus for analyzing coronary vessels and a corresponding method are provided in which simulated pullback data obtained from (non-invasively) acquired diagnostic images is co-registered with invasively acquired intravascular pullback data and the co-registration is used to identify disparities in the pullback data obtained using the two modalities. These disparities allow for deriving further information about the vessel geometry and/or the blood flow through the vessel. They may therefore be used to improve the physiological model.

Abstract: A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices.

Abstract: Embodiments of the present application provide a method, an apparatus, a computer device for convolution operation and a computer readable storage medium. The method includes: obtaining input data of a network layer in a convolutional neural work; extracting each time a plurality of data points from the input data according to a preset step size; mapping the plurality of data points extracted each time to the same position at different depth levels of three-dimensional data to obtain rearranged data; and performing convolution operation on the rearranged data with a convolution kernel of a preset size to obtain a convolution result. Through the present solution, the operation efficiency of the convolutional neural network can be improved.

Abstract: An electronic device includes a processor configured to generate a position movement prediction field indicating prediction of a potential positional change of a branch path by a patient's biological activity for one or more branch paths based on a blood vessel image of a reference frame, correct guidewire information extracted from a blood vessel image of a target frame with respect to a catheter position of the reference frame, and select a branch path to dispose the guidewire information, among one or more branch paths of a blood vessel region based on the position movement prediction field and the corrected guidewire information; and a display configured to visualize the guidewire information on the selected branch path.

Abstract: Embodiments described herein provide a platform, device and process for digital pathology that enable multi-level annotation and visualization of histopathologic slides using a modular arrangement of deep convolutional neural networks (CNNs). The CNNs can be trained using pathology images (e.g., in some cases increasing the base of data by breaking larger fields of view into smaller ones) to learn features consistent with certain pathologies. The platform can use the CNNs to visually annotate pathology slides at an interface tool of a display device. The platform can automate the process of selection, as well as provide an opportunity for the pathologist to see a depiction of predicted results. The platform can use the CNNs to identify regions of interest on pathology slides. The interface tool can enable a predicted region of interest (ROI) type to be visually presented on a surface map showing the basis of the prediction.

Abstract: A non-transitory computer-readable storage medium storing a set of instructions that are executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations comprising receiving an imaging study including a medical image of a patient; extracting image context from the imaging study; generating a search query based on the image context; sending the search query to a data source; receiving results of the query from a data source; and providing the results to a user.

Abstract: A methods and apparatuses for image processing are provided. The method may include acquiring initial image data from an image sensor. The method may further include deconstructing the initial image data in a plurality of image pyramid layers. The method may further include collapsing a first image pyramid layer with a second image pyramid layer in order to create an intermediate layer. The method may further include collapsing the intermediate layer with the subsequent image pyramid layer to create a new intermediate layer and generating the final image based on the new intermediate layer and a last image pyramid layer. A tone-mapping operator is applied to at least one of the intermediate layers.