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 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 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: 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: 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.

Abstract: A system and method for generating a sequential display of medical image data, such as magnetic resonance (MR) image data, computerized tomography (CT) image data, ultrasound image data, etc. are provided. The method includes the steps of acquiring a plurality of medical image data, each data including an image and associated header information; sorting each of the plurality of medical image data into a plurality of groups based on at least one common attribute identified in the associated header information; and sequentially displaying at least one group of images in a continuous movie loop. Additionally, the method provides for generating multiple images in a single movie loop allowing for comparison between data sets having different properties.

Abstract: 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.