Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporate Research, Inc.

Shmuel Aharon

Leo John Grady

Thomas Schiwietz

Oliver Fluck

Mikael Rousson

Daniel Cremers

Mehmet Akif Gulsun

Ali Khamene

Huseyin Tek

Ozcan Oksuz

Christophe Lenglet

A method for deformable registration including determining a vector field from a two-dimensional matching of a volume of an object of interest and a two-dimensional image of the object of interest, providing a deformation profile, and finding a volume deformation that maps to a state of the two-dimensional image, wherein the deformation is parameterized by the vector field and control points of the deformation profile to find a control point configuration of the volume deformation.

Deformable 2D-3D registration

A GPU accelerated volume rendering method from flat textures (also known as texture atlas) is disclosed. The method is not restricted to a specific rendering technique. The method is fast as it requires no reordering or copying passes. An image processing system is also provided. Addressing of two dimensional flat textures is accomplished based on the viewing direction. A first addressing scheme is used for the x direction, a second addressing scheme is used for the y direction and a third addressing scheme is used for the z direction, where x, y, and z refer to the axes of Cartesian coordinate system.

Flat texture volume rendering

A method of coronary vessel segmentation and visualization includes providing a digitized coronary image, placing a plurality of seed points along an estimated centerline of a coronary vessel, selecting a seed point and constructing a cyclic graph around the seed point in a plane perpendicular to the centerline at the seed point, performing a multi-scale-mean shift filtering in the perpendicular plane to estimate image gradient values, detecting a vessel boundary using a minimum-mean-cycle optimization that minimizes a ratio of a cost of a cycle to a length of a cycle, constructing a sub-voxel accurate vessel boundary about a point on the centerline, and refining the location of the centerline point from the sub-voxel accurate boundary, where the steps of constructing a sub-voxel accurate vessel boundary and refining the centerline point location are repeated until convergence.

System and method for coronary segmentation and visualization

A manifold learning technique is applied to the problem of discriminating an object boundary between neighboring pixels/voxels in an image. The manifold learning technique is referred to as locality preserving projections. The application is for multi-channel images, which may include registered images/volumes, a time series of images/volumes, images obtained using different pulse sequences or contrast factors, radar and color photographs.

Manifold learning for discriminating pixels in multi-channel images, with application to image/volume/video segmentation and clustering

System and methods of displaying anatomical structures and their surrounding area, are disclosed. For a viewing point the anatomical structures are rendered separate from their surrounding and saved. The surrounding area of the anatomical structure within a viewing frustum is extracted, interpolated and rendered. The rendered anatomical structures and calculated image of the surrounding are combined into a complete rendering of the anatomical structures with its nearby surrounding areas.

Rendering anatomical structures with their nearby surrounding area

A method for histogram calculation using a graphics processing unit (GPU), comprises storing image data in a two-dimensional (2D) texture domain; subdividing the domain into independent regions or tiles; calculating in parallel, in a GPU, a plurality of tile histograms, one for each tile; and summing up in parallel, in the GPU, the tile histograms so as to derive a final image histogram.

Method and system for histogram calculation using a graphics processing unit

A method for segmentation of an image interactively with a user utilizes level set segmentation and includes selecting by user input respective areas of object and of background; initializing an embedding function implementing a segmentation boundary according to the selecting; computing intensity distributions and for the respective areas of object and of background; and performing repeatedly the steps below until convergence is reached: (a) evolving the embedding function, (b) recomputing the intensity distributions, and (c) checking for new user input and, if so: (d) updating labeling of the areas of object and background.

Method and system for interactive image segmentation

A method for image segmentation includes specifying seed points in an image of interest, the seed points corresponding to a node in a seed texture, each seed point having a different color. The method includes determining a matrix for each node, including neighboring edge weights of each node, and determining a probability that a node can be characterized as each seed point. The method includes assigning the node the color of a most probable seed point, and outputting a segmentation of the image of interest according to node assignments, wherein the segmentation differentiates portions of the image of interest.

GPU accelerated multi-label image segmentation (MLS)

A method and system for segmenting an object in a digital image are disclosed. A user selects at least one foreground pixel or node located within the object and at least one background pixel or node located outside of the object. A random walk algorithm is performed to determine the boundaries of the object in the image. In a first step of the algorithm, a plurality of coefficients is determined. Next, a system of linear equations that include the plurality of coefficients are solved to determine a boundary of the object. The processing is performed by a graphics processing unit. The processing can be performed using the near-Euclidean LUV color space or a Lab color space. It is also preferred to use a Z-buffer in the graphics processing unit during processing. The object, once identified, can be further processed, for example, by being extracted from the image based on the determined boundary.

GPU accelerated multi-label digital photo and video editing

System and methods of displaying one or more vessels, particularly blood vessels and more particularly coronary arteries, are disclosed. Each of the one or more vessels is associated with an outline of a tube generated from a set of image data. For a viewing point, a depth image is calculated by projecting a plurality of rays from the viewing point and, for each of the plurality of rays, determining distance between the viewing point and the point of intersection of ray and any of the tubes. The surrounding area of the one or more vessels within a bounding viewing box is extracted, interpolated and rendered, and the depth image of the surrounding area is calculated. Then, an image of the one or more vessels and their background information concerning data surroundings the vessels generated based on the depth image is displayed.

Simultaneous projection of multi-branched vessels and their context on a single image

A system and method for segmenting an object in an image using an isoperimetric ratio in a graphics processing unit is disclosed. The object is identified by one or more selected pixels located within the object. The system includes a graphics processing unit and software loadable on the graphics processing unit. The software is operable to find weights for edges, to build a Laplacian matrix and a d vector, to eliminate the row and column corresponding to the one or more selected pixels to determine Land d, to solve xin the equation Lx=d; and to threshold the potentials x at the value that selects the object having the lowest isoperimetric ratio.

GPU accelerated isoperimetric algorithm for image segmentation, digital photo and video editing

A collision detection method for polygonal objects using OpenGL is provided, comprising dividing a surface of each object into a set of axis aligned bounding boxes (AABB) to build an AABB tree, selecting an object among the plurality of polygonal objects as a reference object, defining a global bounding box of the reference object, detecting first bounding boxes of the plurality of polygonal objects that intersect the global bounding box of the reference object, and determining intersections for each polygon of the reference object with all polygons of the plurality of polygonal objects.

Growing community of inventors

West Windsor, NJ, United States of America

Shmuel Aharon