Abstract:
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.
Abstract:
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.
Abstract:
Exemplary methods and systems are provided for performing the Finite Element Method. An exemplary method includes the steps of transferring a set of nodes and elements (i.e., a mesh) from a memory to a graphics processing unit (GPU); and performing the Finite Element Method on the set of nodes and elements using only the GPU. An exemplary system includes a central processing unit (CPU); a memory operatively connected to the CPU; and a graphics processing unit (GPU) operatively connected to the CPU; wherein the CPU transfers a set of nodes and elements from the memory to the GPU; and wherein the GPU performs the Finite Element Method on the set of nodes and elements.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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 L0 and d0, to solve x0 in the equation L0x0=d0; and to threshold the potentials x at the value that selects the object having the lowest isoperimetric ratio.
Abstract:
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.