A method of determining respiratory phase of a living body from a sequence of digitized fluoroscopic images of a living-body region exhibiting respiratory displacement, the method employing programmable computing apparatus and comprising the steps of: (a) in each living-body-region image in the sequence, defining one or more zones with each image having identical image-to-image zone locations, sizes, and shapes; (b) for each image, computing an average pixel intensity for each zone to form a sequence thereof for each zone; (c) for each zone, modifying the average pixel intensities by (i) computing the mean value of the sequence of average pixel intensities for such zone, and (ii) subtracting the mean from each average pixel intensity in the zone; (d) for each zone sequence, computing a figure-of-merit; (e) selecting the zone having the highest figure-of-merit; and (f) using the sequence of pixel intensities of the selected zone to determine respiratory phase.

Video processing methods and the associated system architecture for measuring transformation in objects, including pupils, entail the following steps: 1. Motion correction; 2. Object (eye) detection; 3. Image correction; and 4. Fourier-based analysis for item (in some embodiments the item is a pupil) motion estimation.

An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

The moving object tracking apparatus emphasizes an image with specific size in each of fluoroscopic images derived from two or more paired fluoroscopic radiographic devices, obtains a value indicating certainty degree of detecting a candidate position of the object to be tracked on the image subjected to the emphasizing process, extracts the candidate position based on the value indicating the certainty degree of detection, calculates a value indicating a correlation between the candidate position extracted from images picked up from two or more directions, and a position of the fluoroscopic radiation generator, detects the position of the object to be tracked based on the value indicating the certainty degree of detection, and the value indicating the correlation, and controls irradiation of radiation to an irradiation target based on the detected position of the object to be tracked.

An image acquisition unit acquires a CT image and one or more MRI images of the brain of a subject that has developed a cerebral infarction. An infarction region extraction unit extracts an infarction region corresponding to the time elapsed since the development from the MRI image. A registration unit performs registration between the CT image and the MRI image. An infarction region specification unit specifies the infarction region corresponding to the time elapsed since the development in the CT image on the basis of the result of the registration. A learning unit learns a discriminator which discriminates an infarction region corresponding to the time elapsed since the development in the CT image to be discriminated, using the infarction region corresponding to the time elapsed since the development, which has been specified in the CT image, as teacher data.

Methods and systems are provided for dynamic collimation adjustment during various x-ray imaging and image-guided procedures. In one example, collimation for an x-ray mammography system is adjusted based on a volume of interest, and further based on a workflow step of an imaging procedure. As an example, prior to a target selection, collimation may be adjusted to irradiate a larger volume of interest and x-ray system acquisition parameters, and hence, a greater area of a detector is irradiated; and after target coordinates are selected (e.g., for an interventional procedure), collimation may be adjusted to irradiate a reduced volume of interest based on the selected target and x-ray system acquisition parameters, and hence, a smaller area of detector is irradiated.

Methods and systems for crop grading and crop management. One or more images of crops are obtained and one or more crop related features are at least one of identified or extracted from the one or more images. A crop health status is determined based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis. One or more actions are at least one of recommended, triggered, and performed.

The present disclosure provides an operating method of an automatic brain infarction detection system on magnetic resonance imaging (MRI), which includes steps as follows. Images corresponding to different slices of a brain of a subject are received from the MRI machine. The image mask process is performed on first and second images of the images. It is determined whether the cerebellum image intensity and the brain image intensity in the first image are matched. When the cerebellum image intensity and the brain image intensity are not matched, the cerebellar image intensity in the first image is adjusted. The first image is processed through a nonlinear regression to obtain a third image. A neural network identify an infarct region by using the first, second and third images that are cut.

A microscope system includes an eyepiece, an objective, a tube lens that is disposed between the eyepiece and the objective, a projection apparatus that projects a projection image onto an image plane on which an optical image is formed by the tube lens, and a processor that performs processes. The processes include performing for digital image data of the sample at least one analysis process selected from a plurality of analysis processes, and generating projection image data representing the projection image on the basis of the analysis result and the at least one analysis process. The projection image data indicates the analysis result in a display format including an image color corresponding to the at least one analysis process. The generating the projection image data includes determining a color for the projection image in accordance with the at least one analysis process selected from the plurality of analysis processes.

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).