A method may include identifying a simulated three-dimensional representation corresponding to an internal anatomy of a subject based on a match between a computed two-dimensional image corresponding to the simulated three-dimensional representation and a two-dimensional image depicting the internal anatomy of the subject. Simulations of the electrical activities measured by a recording device with standard lead placement and nonstandard lead placement may be computed based on the simulated three-dimensional representation. A clinical electrogram and/or a clinical vectorgram for the subject may be corrected based on a difference between the simulations of electrical activities to account for deviations arising from patient-specific lead placement as well as variations in subject anatomy and pathophysiology.

A method of determining volumetric data of a predetermined anatomical feature is described. The method comprising determining volumetric data of one or more anatomical features present in a field of view of a depth sensing camera apparatus, identifying a predetermined anatomical feature as being present in the field of view of the depth sensing camera apparatus, associating the volumetric data of one of the one or more anatomical features with the identified predetermined anatomical feature, and outputting the volumetric data of the predetermined anatomical feature. An apparatus is also described.

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

A medical image display apparatus includes an image acquisition unit that receives an input of a three-dimensional brain image of a subject, a brain area division unit that divides the three-dimensional brain image of the subject into a plurality of brain areas, an image analysis unit that calculates an analysis value for each brain area from the three-dimensional brain image of the subject, a data acquisition unit that acquires information indicating a correspondence between the brain area and a function of the brain, a display unit, and a display controller that displays an image showing the brain image of the subject divided into the brain areas, a function of the brain corresponding to each of the brain areas, and the analysis value on the display unit in association with each other.

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.

There is provided a system and method of assessing the quality of the extraction of the signal and the reliability of the physiological measurement by providing a system for producing a quality metric for physiological information extraction from a sequence of image frames, the system comprising a signal extraction unit configured to extract a signal representative of a physiological characteristic from a plurality of the image frames, a signal analyzer configured to calculate a plurality of physiological information results from the signal using a plurality of calculation functions, the plurality of calculation functions being comprised within a list of calculation functions, each physiological information result being calculated using a different calculation function, and a quality metric calculator for calculating a quality metric value based on a signal analysis metric derived from a comparison between the physiological information results of the plurality of physiological information results.

An image processing apparatus includes: an image acquisition unit that acquires a plurality of endoscope images obtained by imaging an observation target at different times with an endoscope; a blood vessel extraction unit that extracts blood vessels of the observation target from the plurality of endoscope images; a blood vessel information calculation unit that calculates a plurality of pieces of blood vessel information for each of the blood vessels extracted from the endoscope images; a blood vessel parameter calculation unit that calculates a blood vessel parameter, which is relevant to the blood vessel extracted from each of the endoscope images, by calculation using the blood vessel information; and a blood vessel change index calculation unit that calculates a blood vessel change index, which indicates a temporal change of the blood vessel, using the blood vessel parameter.

An image processing apparatus includes at least one processor operatively coupled to a memory, serving as an obtaining unit configured to obtain a contrast material-enhanced image of an object, a first region extraction unit configured to extract a first region representing a first anatomical portion of the object from the image, with the first anatomical portion in the object being contrast material-enhanced, and an estimation unit configured to estimate a phase of the image based on a comparison result between a feature amount concerning a gray level in the first region and statistical data concerning a gray level in a plurality of phases of the first anatomical portion.

Systems and methods are provided for acquiring a series of angiographic images and identifying the anatomical structures represented in the series of images using a machine learnt classifier. Additional series of images that would yield the optimal visualization of the structure of interest may be suggested.

An ultrasound device performs an ultrasound scan to acquire a video clip having a plurality of images in a selected zone of a set of lungs. The ultrasound device detects B lines in each of the images of the video clip. The ultrasound device assigns a score to each of the images of the video clip based at least in part on the detected number of B lines. The ultrasound device highlights the detected B lines in each of the images of the video clip. The ultrasound device identifies a representative image from the highlighted images of the video clip in the selected zone. The identification of the representative image is based at least in part on the assigned score of each of the images. The ultrasound device displays the identified representative image.