In a medical image processing apparatus, a medical image processing method, and a medical image processing program, in a case where there are a plurality of past brain images, it is possible to select a past brain image with which the atrophy rate of the brain can be accurately calculated. An image acquisition unit acquires a target brain image Bt as a diagnostic target and a plurality of past brain images Bpi, which have earlier imaging dates and times than the target brain image Bt, for the same subject. A similarity calculation unit calculates the similarity between each of the plurality of past brain images Bpi and a standard brain image Bs. A selection unit selects a reference brain image B0 serving as a reference for calculating the amount of change of the brain from the plurality of past brain images Bpi.

Systems and methods for tracking healing progress of multiple adjacent wounds are provided. In one embodiment, a system may include a processor configured to receive a first image of a plurality of adjacent wounds near a form of colorized surface having colored reference elements, determine colors of the plurality of wounds, correct for local illumination conditions, receive a second image of the plurality of wounds near the form of colorized surface, to determine second colors of the plurality of wounds in the second image, match each of the plurality of wounds in the second image to a wound of the plurality of wounds in the first image, and determine an indicator of the healing progress for each of the plurality of wounds based on changes between the first image and the second image.

Methods, systems, workstations, and computer program products that provide automated implant analysis of batches of image data sets of a plurality of different patients having an implant coupled to bone using a first data set of a first patient from the batch of image data sets, the first data set comprising a first image stack and a second image stack and allowing a user to select parameter settings for implant movement analysis of the implant including selecting a first object of interest and a second reference object. Measurements of movement of the implant and/or coupled bone can be automatically calculated and selected parameter settings can be automatically propagated to other image data sets of other patients of the batch of image data sets and measurements for the batch of image data sets of others of the different patients can be automatically calculated.

Disclosed is a method for analysing cells, in which cells are separated and the individual cells pass via a measurement region of a unit for spatially resolved radiation intensity measurement, wherein, for at least one of the separated cells, when passing via the measurement region, a time sequence of spatial intensity patterns of an electromagnetic radiation emitted from and/or influenced by the cell is created, the optical flow of a respective two of the spatial intensity patterns is calculated for at least one portion of the sequence of intensity patterns using a computer unit, and an evaluation of the calculated optical flows occurs. Also disclosed is a device for analysing cells, comprising a device for separating cells, a unit for spatially resolved radiation intensity measurement, and a computer unit for calculating the optical flow of a respective two of the created intensity patterns, and for evaluating the calculated optical flows.

A method of grouping detection events in an imaging apparatus is described herein. The detection events can include primary detection events and secondary scattered events, which are frequently discarded due to the secondary scattered events, thus reducing sensitivity of the dataset for eventual image reconstruction. The method includes cell modules cascaded with identical parametrized cells, in a pipeline fashion, having the last cell in the chain circle back to the first cell. A rotating data pointer indicates the location of the first entry in the cell pipeline. The described method enables the grouping of multiple samples of detector data in real time with no loss of information, based on a time and location of the detected event. The method can be implemented in an FPGA as a hardware-based real time process.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for contrast enhanced ultrasound quantification imaging are provided. One of the methods includes: obtaining, for each location in a region of interest, a time-dependent ultrasound signal with respect to the region of interest for a time period; setting, for the each location, a global threshold for the obtained time-dependent ultrasound signal; determining, for the each location, a relative time instant that the time-dependent ultrasound signal reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time instant of the each location, wherein the generated structural image displays different time instants that ultrasound signals corresponding to different locations in the region of interest reach the global threshold.

A method and system for rapid scanning, surveillance and detection of an infectious disease in an individual is disclosed. The method and system operate a processor to receive a set of training data comprising training data to train one or more analytical models using artificial intelligence algorithms. The method and system then use one or more input devices to receive parameters related to the individual such as images, body temperature, clinical data, CT scan data, voice data, video data and thermal image. Each parameter is associated to one or more features. An analytical model is selected from one or more analytical models to detect the presence of the infectious disease in an individual. The method and system for rapid scanning, surveillance and detection can be trained on a wide dataset received from clinical data, CT-scan, X-ray, temperature sensor and other data.

Described herein is a computer implemented method that includes receiving at a data processor two or more digital data files representing medical images of a same modality; performing group-wise 3D registration of the digital data files representing medical images of a same modality; and parallel lesion detection and analysis on the digital data files representing the medical images.

An automatic system and method for non-invasive imaging and identification of specific ocular structures of the eye and adnexa tissues by synchronous segmentation of visual and infrared images; that can produce spatial temperature profiles within each segmented area of the eye and adnexa; that can track eye and head movement and eye-blinks during the period of measurement to remove artefacts and maintain synchronicity; that can track ocular surface and eye adnexa temperature profiles over time; that can assist in diagnosis of eye disease; that can produce diagnostic indicators for ocular disease diagnosis and study of the eye. The system comprises infrared and visible light cameras for imaging the ocular structures, and a digital signal processing unit for processing the acquired infrared and visible images to output segmentations of the images for identification of different areas of the eye surface, including pupil, cornea, conjunctiva, and eyelids. The system further captures synchronous infrared and visible images from each segmented area of the ocular surface over the time of measurement. A digital signal processing unit processes and analyzes the infrared and visible images to generate descriptive outputs on temporal and spatial changes in the infrared and visible images over the time of measurement, as well as produce diagnostic indicators for ocular disease diagnosis and study of the eye.

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.