Embodiments of the present invention relate to discriminating between normal and abnormal left ventricles in echocardiography. In some embodiments, a first region of a first image of a heart is located by clustering. The first region depicts a chamber of the heart. The chamber has a chamber shape. A boundary of the first region is determined by contour tracing a retaining the traced contour overlapping the first region. A predetermined shape is compared to the boundary. The predetermined shape had been determined without reference to the first image. A first plurality of parameters is determined that, when applied to the predetermined shape, conforms the predetermined shape to the boundary and minimizes at least one error between the predetermined shape and the boundary. An indication of normality or abnormality is determine from the first plurality of parameters.

A method of segmenting an MR organ volume includes performing regional mapping on the MR organ volume using a spatial prior probability map of a location of the organ to create a regionally mapped MR organ volume, and performing boundary refinement on the regionally mapped MR organ volume using a level set framework that employs the spatial prior probability map and a propagated shape constraint to generate a segmented MR organ volume.

The present invention relates to a device for encoding an anatomical shape (13) of a living being, comprising a receiving unit (12) for receiving an anatomical shape (13); a shape representation generating unit (14) for generating a shape representation of the anatomical shape (13) by using one or more shape representation models and determining the value of one or more shape representation coefficients of the one or more shape representation models; and a conversion unit (16) for converting the determined value of the one or more shape representation coefficients into a human-readable code comprising one or more human-readable characters.

An image retrieval apparatus that retrieves a candidate medical image in diagnosis of diffuse lung disease based on a position of an abnormal shadow in an organ region in a target medical image, the apparatus includes a memory, and a processor coupled to the memory and configured to map the organ region in the target medical image to an image having a predetermined shape to make it identifiable whether the abnormal shadow is distributed over a first portion in the organ region or a second portion in the organ region, occurrence portions of the abnormal shadow within the first portion and the second portion are organizationally different, and calculate a position of the abnormal shadow after the mapping in the image having the predetermined shape.

An interactive image segmenting apparatus and method are provided. The image segmenting apparatus and corresponding method include a boundary detector, a condition generator, and a boundary modifier. The boundary detector is configured to detect a boundary from an image using an image segmentation process. The feedback receiver is configured to receive information about the detected boundary. The condition generator is configured to generate a constraint for the image segmentation process based on the information. The boundary modifier is configured to modify the detected boundary by applying the generated constraint to the image segmentation process.

There is provided an image registration device, method, and program that enable easy initial registration between a target object included in a video and a simulation image. A first registration unit performs first registration that is initial registration between an intraoperative video and a simulation image. At this time, a boundary image showing the boundary of the simulation image is displayed on a display so as to be superimposed on the intraoperative video. An operator performs registration between a target object included in the intraoperative video and the boundary image. After the end of the first registration, a second registration unit performs second registration between the simulation image and the target object included in the intraoperative video based on the result of the first registration.

Systems and methods are provided for contouring a set of medical images. Deformation field data is generated between a source image and a target image of the set of medical images. The deformation field data relates structures in the source image to corresponding structures in the target image and is generated in accordance with a deformable registration algorithm. The deformation field data is utilized to generate target contour data associated with the target image from source contour data, associated with the source image, that identifies one or more objects within the source image.

Systems and methods relate to encoded video streams including geometric-data streams transmitted to a receiver for rendering of a viewpoint-adaptive 3D persona. A method includes obtaining a three-dimensional (3D) mesh of a subject generated from depth-camera-captured information about the subject, obtaining a facial-mesh model, locating a facial portion of the obtained 3D mesh of the subject, computing a geometric transform based on the facial portion and the facial-mesh model, the geometric transform determined in response to one or more aggregated error differences between a plurality of feature points on the facial-mesh model and a plurality of corresponding feature points on the facial portion of the obtained 3D mesh, generating a transformed facial-mesh model using the geometric transform and generating a hybrid mesh of the subject at least in part by combining the transformed facial-mesh model and at least a portion of the obtained 3D mesh.

A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.

A system, article, and method of image segmentation refinement for image processing.