Abstract: A computer-assisted detection method is provided for detecting suspicious locations of lesions in the volumetric medical images. The method includes steps of features extraction and fusion. The first step is computing gradient feature for extraction of the layer of Partial Volume Effect (LPVE) between different tissues that related to specific organs. The LPVE will combine with the result of voxel classification to fulfill the task of tissue classification. After tissue classification, the contour of tissue boundary is determined. The gradient feature is also used to determine the direction that intensity changes. This direction that intensity changes most dramatically serves as the normal vector for voxel on the contour of the tissue boundary. The second step is to determine a local surface patch on the contour for each voxel on the contour. A local landmark system is then created on that patch and the so-called Euclidean Distance Transform Vector (EDTV) is computed based on those landmarks.

Abstract: Different applications for multiple imaging technologies are provided to advance patient care. In one aspect, a 3-D imaging device is used to identify the location of target region in a patient, such as a tumor or aneurysm, for instance. The location information is then used to control a device used in performing a surgical or other intervention, further imaging or a diagnostic or therapeutic procedure (920). The device can operate fully automatically, as a robot, or can assist a manual procedure performed by a physician. In another aspect, the invention provides a technique for obtaining an improved image of the vasculature in a patient (1020). In another aspect, images from multiple imaging technologies are combined or fused (1120) to achieve synergistic benefits.

Abstract: Methods for generating a centerline or skeleton structure within a 3D virtual object are provided. A first method defines a centerline based on distance from boundary values along points of the defined centerline. A second method uses a distance from boundary field to assign costs to voxels in the virtual object and defines a minimum cost spanning tree based on assigned costs. The centerline is defined along the minimum cost spanning tree. Branches along the centerline are identified and added to the centerline to define a skeleton.