A joint image unfolding apparatus, a joint image unfolding method, and a non-transitory computer readable recording medium storing a joint image unfolding program are provided to make it possible to check information regarding the entire cartilage in a joint with high accuracy. An image obtaining unit (21) obtains a three-dimensional image of a joint having cartilage. An unfolding unit (23) unfolds the cartilage included in the three-dimensional image with reference to a specific reference axis in the joint to generate an unfolded image.

In some examples, a method includes receiving a test image from a user or client, the test image depicting an anatomical region or structure of a human body, obtaining a reference image corresponding to the anatomical region or structure depicted in the test image, and analyzing the test image and the reference image to obtain a set of differences between the two images. In some examples, the method further includes, based at least in part on the set of differences, detecting a possible abnormality in the test image and outputting a result to the user or client. In some examples, a fast R-CNN is used to detecting the possible abnormality in the test image.

A console includes a CPU as at least one processor. The CPU acquires a radiographic image obtained by imaging an imaging region where a patient is present, with a radioscopy apparatus. The CPU specifies a structure image that is included in the radiographic image and represents a structure of a specific shape having transmittance of radiation lower than the patient, based on the specific shape. The CPU executes image processing corresponding to the structure image to the radiographic image.

Image data including a target object is acquired. The target object includes at least one sub-object. Target image data is acquired by processing the image data based on a fully convolutional neural network. The target image data include at least a center point of each sub-object in the target object.

Embodiments of the present invention relate to methods of predicting fracture risk, which improve fracture risk prediction by developing a bone radiomics score model based on machine learning. As an embodiment of the present invention, the method of predicting the fracture risk is configured to perform the steps of designing a development set, processing bone images for a plurality of subjects included in the development set, extracting texture features from the bone images, selecting optimal texture features required to predict the fracture risk from the extracted texture features, performing machine learning for the optimal texture features using a training set of the development set, and designing a bone radiomics score model to predict the fracture risk.

A movable photographing system is provided. The movable photographing system includes a carrier, an image capturing device, a storage device and a processing device. The image capturing device is carried by the carrier and configured to generate a first image. The storage device stores a plurality of image data. The processing device obtains the feature information of a target object in the first image, and according to the feature information, compares the first image with the plurality of image data to select a reference image from the plurality of image data. In addition, the processing device generates motion information using the first image and the reference image and the carrier moves according to the motion information to adjust the shot position of the image capturing device to generate a second image.

A skeleton recognition method for a computer to execute a process includes acquiring distance images from each of a plurality of sensors that sense a subject from a plurality of directions; acquiring joint information that includes joint positions of the subject for each of the plurality of sensors by using a machine learning model that estimates the joint positions from the distance images; generating skeleton information that represents three-dimensional coordinates by integrating the joint information; and outputting the skeleton information of the subject.

Methods, systems and apparatuses may provide for technology that detects an individual in a real-time multi-camera video feed and generates three-dimensional (3D) skeletal data based on the real-time multi-camera video feed. The technology may also automatically identify a frontal body orientation of an individual based on the 3D skeletal data and one or more anthropometric constraints.

An image processing apparatus divides a radiographic image obtained through radiography into a plurality of areas, extracts, as a target area, at least one area to serve as a reference, from the plurality of areas divided, determines a rotation angle from the target area extracted, and rotates the radiographic image on the basis of the rotation angle determined.

A method according to one embodiment of the present disclosure comprises receiving a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements; receiving a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements; determining a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; calculating a homography for each transformation in the set of transformations to yield a set of homographies; and identifying, using the set of homographies, a common portion of each transformation attributable to a change in camera pose, and an individual portion of each transformation attributable to a change in rigid element pose.