Abstract: A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.

Abstract: A method of finding a path from a start point to a target point, in multi-dimensional space, including: (a) determining a plurality of points in a physical space, including a start point and an target point; (b) computing, using a cost function, for said points an accumulated path cost from the start point to a point; representing a minimal cost path from the start point to the point with respect to an optimization criteria; (c) computing for at least some of said points an estimated-cost-to-target from a point to the target point; and (d) after computing said costs, determining at least one of a minimal path or a minimal path cost of a path from the start point to the target point in the physical space, wherein the determination is based on said accumulated path costs, and is minimal with respect to the optimization criteria.

Abstract: A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, b) applying a second set of one or more tests to accept seeds as bone seeds, and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image density has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis; b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image density at or near said voxel, with the image density falling sufficiently steeply in both directions along at least one axis; and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, b) applying a second set of one or more tests to accept seeds as bone seeds, and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image density has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis; b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image density at or near said voxel, with the image density falling sufficiently steeply in both directions along at least one axis; and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.

Abstract: A method of finding a path from a start point to a target point, in multi-dimensional space, including: (a) determining a plurality of points in a physical space, including a start point and an target point; (b) computing, using a cost function, for said points an accumulated path cost from the start point to a point; representing a minimal cost path from the start point to the point with respect to an optimization criteria; (c) computing for at least some of said points an estimated-cost-to-target from a point to the target point; and (d) after computing said costs, determining at least one of a minimal path or a minimal path cost of a path from the start point to the target point in the physical space, wherein the determination is based on said accumulated path costs, and is minimal with respect to the optimization criteria.

Abstract: A method and system for detecting a boundary between a first tissue and a second tissue in a two-dimensional image. A cost is assigned to each pixel in a detection region indicative of the likelihood that the pixel is not located on the boundary between the first tissue and the second tissue. The boundary between the first and second tissues or organs is a path of voxels of minimal cost in the detection region from among two or more paths, the cost of a path being obtained in a calculation based upon the costs assigned to the voxels of the path. The method may be used to detect a boundary between a first tissue or organ and a second tissue or organ in a three-dimensional scan by applying the method to each of one or more two-dimensional sections and joining the two dimensional boundaries into a three dimensional boundary in the three dimensional scan. The method may also be used in segmenting a three-dimensional body scan.