A method generates an image sequence using a tomosynthesis system. The image sequence represents an object under examination in rotating fashion. In a first step at least two projection data sets for the object under examination are captured. These have been acquired using different X-ray spectra in each case and from a plurality of acquisition angles in each case. In a further step at least one combination data set is calculated on the basis of the projection data sets. Subsequently in a further step the image sequence is calculated on the basis of the combination data set. An image sequence generating apparatus and a tomosynthesis system perform this described method.

A medical information processing system according to an embodiment includes processing circuitry. The processing circuitry is configured to identify the position of a tissue from first medical image data represented by an image of a target site acquired before the tissue in the target site was collected and to obtain an image feature value of the tissue. The processing circuitry is configured to obtain an examination result of a pathological examination performed on the tissue. The processing circuitry is configured to bring the image feature value of the tissue into association with the examination result of the pathological examination.

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Apparatus and methods are described for use with an endoluminal data-acquisition device configured to be moved through a lumen of a subject's body, and a two-dimensional angiographic image of the lumen. A value of a luminal-flow-related index of the subject is determined non-invasively at a plurality of locations along the lumen, at least partially by performing image processing on the angiographic image. While the endoluminal data-acquisition device is being moved through the lumen, a set of endoluminal data points of the lumen at a plurality of locations within the lumen is acquired, using the endoluminal data-acquisition device. It is determined that respective endoluminal data points correspond to respective locations along the lumen, and, in response thereto, it is determined that respective endoluminal data points correspond to respective values of the luminal flow-related index. Other applications are also described.

A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

In a method of video monitoring of a subject, for example a driver of a vehicle, the video image is motion compensated by image registration techniques so that the subject's position in each frame of the video image is stable. A region of interest is defined on the skin of the subject and used to obtain a PPG signal. To compensate for variations in illumination of the subject caused by the subject's movement in the vehicle, the parameters of the calculated motion transformation used in the image registration are used to form an illumination model representing how the illumination of the subject would have changed because of the subject's motion. The illumination model is a linear or quadratic function fitted to the image intensity in the region of interest. Residuals between the fitted model and the image intensity form an illumination-compensated signal in which the photoplethysmographic signal is more clearly present. The illumination-compensated signal is analysed to obtain a PPG signal and from this estimates of one or more vital signs such as heart rate or breathing rate are obtained.

An analysis device includes an acquisition unit configured to acquire an image of a cell and an identification unit configured to identify elements that are identifiable on the basis of the image of cell acquired by the acquisition unit. Characteristic quantities of the elements identified by the identification unit are calculated, a correlation between the characteristic quantities is calculated on the basis of the calculated characteristic quantities of the elements, and a correlation between the elements is calculated on the basis of the calculated correlation between the characteristic quantities.

An electronic device and method for recognizing a real face are provided. The electronic device includes a face image acquisition device and a processor connected to the face image acquisition device. The face image acquisition device is configured to acquire a plurality of face images of a face at a plurality of focal lengths within a first focal length range. The plurality of face images correspond to a plurality of focus planes respectively. The processor is configured to determine at least one face images with a higher definition out of the plurality of the face images, and form a second focal length range with focal lengths of the at least one face images. The processor is further configured to determine that the face is a real face according to the second focal length range.

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images can include receiving cross-sectional images of digital medical image data and identifying target lesions within the cross-sectional images. For each of the target lesions, a target lesion type and anatomical location is identified, a segmenting tool is activated for segmenting the target lesions into regions of interest, lesion metrics are automatically extracted from the regions of interest according to tumor response criteria, and conformity of target lesion identification is monitored using rules associated with the tumor response criteria, prompting a user to address any nonconforming target lesion. The method also includes receiving a presence/absence of metastases, determining changes in lesions metrics, and deriving an objective tumor response based on the tumor response criteria.

A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.