Abstract: A method for producing three-dimensional images of a blood vessel. A first set of seed points is placed along a first estimate of a centerline of the vessel. A cyclic graph is constructed around a first one of the seed points in a plane passing through the seed points. The graph comprises a plurality of nodes, with edges connecting the nodes. The nodes are disposed at equally spaced intervals about each one of a circumference of plurality of concentric circles centered at the seed point The method applies filtering such as multi-scale mean shift intensity detection orthogonal to the edges of the cyclic graph to thereby estimate a boundary of the vessel. A new center of the estimated boundary is determined to thereby generate a new seed point. The process is repeated using the new seed point to thereby generate a final boundary of the vessel in the plane.

Abstract: A method for segmenting and separating arteries and veins in blood pool contrast agents (MRA). Specifically, arteries and veins are accurately segmented by an algorithm that combines local vessel models, discrete centerline models and ordered statistical front propagation to produce accurate segmentation results with the minimum amount of non-vascular inclusion. Separation of arteries and veins is obtained by incorporating centerline models to the distance based watershed transforms.

Abstract: Disclosed is a method and system for detecting a boundary of a vessel in an image. Edges in the image are detected. Edge detection is based on the change in intensity over some distance while varying the scale of the distance. A set of edges is then selected from the detected edges. An initial vessel boundary is determined based on the selected set, and a shape descriptor (e.g., one or more elliptical shape descriptors) is applied to the initial vessel boundary to determine a final vessel boundary.

Abstract: An exemplary method of segmenting an object from a structure of interest is provided. A user-selected point in an image is received. A watershed transformation is performed on the user-selected point to determine a primary object. A neighboring watershed region is added to the primary object based on region competition and a smoothness constraint to form an updated object.

Abstract: A method for segmentation of 3D structures in CT and MR images is provided. The method is based on 3D ray propagation by mean-shift analysis with a smoothness constraint. Ray propagation is used to guide an evolving surface due to its computational efficiency and shape priors are incorporated for robust convergence. The method includes the steps of receiving 3D image data; visualizing the 3D image data on a display device; selecting a structure in the 3D image data by placing a seed in the structure; initializing a plurality of rays from the seed to form a surface; determining a speed function of each of the rays; evolving the surface by propagating the rays based on the speed function of each of the rays; converging the rays on a boundary of the structure; and segmenting the structure when all of the rays have converged on the structure's boundary.

Abstract: A system and method for artery-vein separation and vessel modeling are provided, the system including an adapter unit, a centerline locating unit in signal communication with the adapter unit, and a deformable modeling unit in signal communication with the centerline locating unit; and the method including receiving three-dimensional image data indicative of vessels, visualizing the received image data, selecting seed points on the vessels relative to the visualization, smoothing the image data with a non-linear filter responsive to curvature information of the data, initiating a deformable model for each selected seed point, modulating the propagation of each deformable model in accordance with at least one of local and global statistics, and growing an interconnected region for each model, where each model competes for classifying new points in its own region until convergence.

Abstract: A method for segmentation of 2D structures in CT and MR images is provided. The method is based on 2D ray propagation by mean-shift analysis with a smoothness constraint. Ray propagation is used to guide an evolving curve due to its computational efficiency and shape priors are incorporated for robust convergence. The method includes the steps of receiving 2D image data; visualizing the 2D image data on a display device; selecting a structure in the 2D image data by placing a seed in the structure; initializing a plurality of rays from the seed to form a curve; determining a speed function of each of the rays; evolving the curve by propagating the rays based on the speed function of each of the rays; converging the rays on a boundary of the structure; and segmenting the structure when all of the rays have converged on the structure's boundary.

Abstract: We propose local watershed operators for the segmentation of structures, such as medical structures, from an image. A watershed transform is a technique for partitioning an image into many regions while substantially retaining edge information. The proposed watershed operators are a computationally efficient local implementation of watersheds, which can be used as an operator in other segmentation techniques, such as seeded region growing, region competition and markers-based watershed segmentation.

Abstract: A method and apparatus for representing a multi-dimensional shape includes deriving a shock scaffold from an unorganized point cloud. The shock scaffold is derived by shocks recovered from collisions of logical wavefronts. The logical wavefronts are initialized from selected points in the unorganized point cloud. Shocks hold topology information including speed accelerations and direction from boundaries of the multi-dimensional shape. The shocks recovered from wavefront collisions define shock sheets of the multi-dimensional shape. Representative shock points of the shock sheets are paired to find shock curve representatives as another set of special shock points. The latter are also paired to find shock vertices and the remaining nodes and links defining the full shock scaffold representation of the multi-dimensional shape.

Abstract: A method for segmentation of 3D structures in CT and MR images is provided. The method is based on 3D ray propagation by mean-shift analysis with a smoothness constraint. Ray propagation is used to guide an evolving surface due to its computational efficiency and shape priors are incorporated for robust convergence. The method includes the steps of receiving 3D image data; visualizing the 3D image data on a display device; selecting a structure in the 3D image data by placing a seed in the structure; initializing a plurality of rays from the seed to form a surface; determining a speed function of each of the rays; evolving the surface by propagating the rays based on the speed function of each of the rays; converging the rays on a boundary of the structure; and segmenting the structure when all of the rays have converged on the structure's boundary.