A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

Systems and methods for determining modified fractional flow reserve values of vascular lesions are provided. Patient physiologic data, including coronary vascular information, is measured. According to the physiologic data, a coronary vascular model is generated. Lesions of interest within the coronary vascular system of the patient are identified for modified fractional flow reserve value determination. The coronary vascular model is modified to generate modified blood flow information for determining the modified fractional flow reserve value.

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

The present disclosure is a human body physiological parameter monitoring method based on face recognition for a workstation, and the method is based on a human physiological parameter monitoring system. The human physiological parameter monitoring system has a backstage server, at least one image acquisition device arranged in the workstation; the image acquisition device is communicatively connected with the backstage server and the method has the following steps:(1) continuous image sampling is carried out by the image acquisition device, and uploaded to the backstage server; when an image acquisition device detects the presence of a person, it proceeds to step (2); (2) the backstage server compares the person detected in step (1) with a pre-stored registered person sample on the backstage server through a face recognition algorithm. The method disclosed has the advantage of greatly improved detection efficiency and accuracy.

A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry is configured to acquire time-series medical images including blood vessels of an examination subject, the time-series medical images being fluoroscopically captured in at least one direction at a plurality of points in time, generate a blood vessel shape model including time-series variation information about the blood vessels in an analysis region of the blood vessels on the basis of the acquired time-series medical images, and perform fluid analysis of blood flowing through the blood vessels on the basis of the generated blood vessel shape model.

A medical image processing device includes circuitry configured to: analyze a blood flow flowing in an observation object based on a medical observation image obtained by capturing an image of the observation object; and generate a difference result between a simulation result of a blood flow flowing in a 3D model acquired in advance for the observation object and an analysis result of a blood flow flowing in the observation object.

Systems for obtaining an image of a target are provided including at least one multi-wavelength illumination module configured to illuminate a target using two or more different wavelengths, each penetrating the target at different depths; a multi-wavelength camera configured to detect the two or more different wavelengths illuminating the target on corresponding different channels and acquire corresponding images of the target based on the detected two or more different wavelengths illuminating the target; a control module configured synchronize illumination of the target by the at least one multi-wavelength illumination module and detection of the two or more different wavelengths by the camera; an analysis module configured to receive the acquired images of the target and analyze the acquired images to provide analysis results; and an image visualization module modify the acquired images based on the analysis results to provide a final improved image in real-time.

In one embodiment, a system for biomedical object segmentation includes one or more processors; one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored on the one or more memory modules. The machine readable instructions cause the system to perform the following when executed by the one or more processors: receive image data of one or more biological constructs; analyze the image data to generate processed image data via a data analytics module to recognize biomedical objects; and automatically annotate the processed image data to indicate a location of one or more biological objects within the one or more biological constructs.

A radiation image processing apparatus includes an image processing unit. The image processing unit is configured to create a device map superimposition image by superimposing a device fixed image on a map image.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.