According to some aspects, an image processing apparatus is provided. The image processing apparatus includes circuitry configured to receive at least two images of a biological sample and determine motion information for a plurality of regions of the at least two images. The motion information corresponds to motion of the biological sample. The circuitry is further configured to generate a graphical representation of at least two characteristic amounts. The at least two characteristic amounts correspond to a region of the plurality of regions and one characteristic amount of the at least two characteristic amounts is indicative of the motion information.

Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. The method comprises, while the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

A dynamic analysis system includes a hardware processor. The hardware processor: analyzes a dynamic image for a dynamic state of a living body; generates an analysis result image showing the analysis result; determines, for each pixel of the dynamic image or the analysis result image, whether a pixel value is within a predetermined range of values; classifies the pixels into groups according to the determination result; extracts, as each border pixel, a pixel in a group adjacent to a pixel classified into a different group; generates a border between the groups based on the extracted border pixels; superimposes the border on, between the dynamic image and the analysis result image, an image not subjected to the classification, thereby generating a combined image; and causes an output device to output the combined image.

A method for image analysis comprises receiving one or more images of a plurality of lesions captured from a body of a person, extracting one or more features of the plurality of lesions from the one or more images, analyzing the extracted one or more features, wherein the analyzing comprises determining a distance between at least two lesions with respect to the extracted one or more features, and determining whether any of the plurality of lesions is an outlier based on the analyzing.

A medical system for assisting in the planning and/or performance of the reconstruction of normally symmetrical body parts of a patient comprises a measuring unit for measuring a first surface and a second surface for determining a topography of the first and second surfaces, a storage unit for storing the determined topographies of the first and second surfaces, an evaluation unit for establishing a mirror image of the stored topography of the first surface and for calculating deviations of the stored topography of the second surface from the first surface mirror image, and a control unit for outputting guidance information for at least one medical instrument on the basis of the calculated deviations of the topography of the second surface, in order to reconstruct the topography of the second surface according to the mirror image of the topography of the first surface.

A system for monitoring the movement of objects, structures, models of structures, cables and the like provides for the acquisition of images with an optical sensing device such as a video camera fixedly mounted at a selected distance from the item studied, in which the images are arranged into frames divided into pixels which are characterized by an intensity reflected or emitted over a selected time interval, and a data processing system to calculate a physical displacement as function of time of the item being studied or a portion of the item being studied based on an output from the video camera, and in some embodiments the system visually distinguishes one or more locations in the frame to indicate a difference in the phase of motion for multiple objects appearing in the frame.

The present invention relates to a method and system that automatically classifies tissue type/patterns and density categories in mammograms. More particularly, the present invention relates to improving the quality of assessing density and tissue pattern distribution in mammography.

According to a first aspect, there is provided a computer-aided method of analysing mammographic images, the method comprising the steps of: receiving a mammogram; segmenting one or more anatomical regions of the mammogram; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammogram.

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points.

According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.