A device for acquiring first and subsequent images of a suspect area on a patient and methods for monitoring or detecting changes of the suspect area over time and providing notification when the changes exceed a threshold. The device may be an imaging device, such as a digital camera, possibly augmented with physical or optical devices for arranging the orientation and/or distance of the imaging device with respect to the suspect area. In addition, methods for identifying, relocating, acquiring a first and/or subsequent image of the suspect area, and performing a comparative analysis of respective images are also described. Results of the comparative analysis can be used to notify and/or assist a medical professional in treating or counseling the patient.

The current subject matter provides a tool for evaluating the risk of failure or the likelihood of success of surgery of healing ligaments and tendons in the body. In some embodiments, a region of a scan comprising one or more of an anterior cruciate ligament (ACL) or an ACL graft can be defined. A magnetic resonance (MR) imaging data set can be obtained. MR parameters characterizing a size and a quality of the ACL or ACL graft can be derived using the MR data. The MR parameters can be used as inputs to a predictive model. A score characterizing a likelihood of failure of the ACL or ACL graft in a human patient can be generated using the predictive model.

Evaluating a pressure ulcer includes: detecting in a color input image a boundary of the pressure ulcer to get a RGB region of interest (ROI) image; converting the RGB ROI image to a grayscale ROI image; processing the grayscale ROI image using a Linear Combination of Discrete Gaussians (LCDG) process to estimate three main class probabilities of the grayscale ROI image; processing the RGB ROI image using predetermined RGB values to estimate the three main class probabilities of the RGB ROI image; and combining the probabilities of the grayscale ROI image and the probabilities of the RGB ROI image to determine an estimated labeled image; and normalizing and refining the estimated labeled image using a Generalized Gauss-Markov Random Field (GGMRF) process to produce a final segmentation image.

A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.

A frame storage stores an image obtained by imaging a region of at least part of the body of a user. A vital sign signal detector detects a signal sequence of a vital sign that cyclically varies from plural imaged regions of the body of the user by using captured images of a predetermined number of frames stored in the frame storage. A correlation calculator obtains the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body. An identity determining section determines whether or not the respective imaged regions of the body belong to the same user based on the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body.

According to an aspect, video data taken from an endoscopic imaging device can be used to automatically control a surgical pump for purposes of regulating fluid pressure in an internal area of a patient during an endoscopic procedure. Control of the pump can be based in part on one or more features extracted from video data received from an endoscopic imaging device. The features can be extracted from the video data using a combination of machine learning classifiers and other processes configured to determine the presence of various conditions within the images of the internal area of the patient. Using the one or more extracted features, the system can adjust the inflow and outflow settings of the surgical pump to regulate the fluid pressure of the internal area of the patient commensurate with the needs of the surgery and the patient at any given moment in time during the surgical procedure.

Devices and methods are provided for analyzing images from a magnetic resonance (MR) system. The device includes at least one hardware processor coupled with a storage system accessible to the at least one hardware processor. The device further includes a display in communication with the at least one hardware processor. The device receives a plurality of non-contrast MR images in a region of interest (ROI). The device obtains blood flow signals from the plurality of non-contrast MR images. The device identifies an abnormal segment by analyzing the blood flow signals. The device displays the non-contrast MR images by a highlighted segment in at least one of the non-contrast MR images to indicate the abnormal segment on the display.

A voxel-based technique is provided for performing quantitative imaging and analysis of tissue image data. Serial image data is collected for tissue of interest at different states of the issue. The collected image data may be deformably registered, after which the registered image data is analyzed on a voxel-by-voxel basis, thereby retaining spatial information for the analysis. Various thresholds are applied to the registered tissue data to identify a tissue condition or state, such as classifying chronic obstructive pulmonary disease by disease phenotype in lung tissue, for example.

A dynamic analysis system includes an imaging device and an analysis device. The imaging device performs dynamic imaging by emitting radiation to a chest part of a human body, thereby obtaining a series of frame images showing a dynamic state of the chest part. The analysis device includes a controller. The controller (i) selects a first plurality of frame images to be analyzed from the series of frame images obtained by the imaging device, (ii) calculates, based on the first plurality of frame images, a ventilation amount index value that indicates an amount of ventilation of a lung field and a perfusion amount index value that indicates an amount of perfusion of the lung field, and (iii) calculates a ratio of the ventilation amount index value to the perfusion amount index value.

An image registration method includes acquiring first image data for a target object that includes first coordinate information; acquiring second image data for the target object that includes second coordinate information, by using a probe; and registering the first image data with the second image data, using the first coordinate information and the second coordinate information. According to the image registration method, image registration between a plurality of pieces of volume data adjusted so that their coordinate axes correspond to each other is performed, whereby a high-quality registered image may be quickly and simply obtained.