Abstract: A technique is provided for segmenting a structure of interest from a volume dataset. The technique identifies regions of the structure using templates having characteristics of the structure of interest. The identified regions may then undergo a constrained growth process using dynamic constraints that may vary based on local statistics associated with the identified structure regions. Edges within the volume may be determined using gradient data determined by evaluating the strongest gradient between each pixel and all adjacent pixels. The edge data may be used to prevent the constrained growing process from exceeding the boundaries of the structure of interest.

Abstract: A method for improving a resolution of an image is provided. The method includes reconstructing an image of an initial portion of an object at an initial resolution, and reconstructing an additional portion of the object at an additional resolution.

Abstract: A technique is provided for partitioning an imaged volume into two or more sub-volumes. The technique identifies partition lines which separate the sub-volumes by generating a profile, such as a bone profile, which is then analyzed to determine the placement of the partition lines. In one embodiment, placement of the partition lines is determined automatically by applying one or more sets of hierarchical rules to the profile. After separation of the imaged volume into sub-volumes, each sub-volume may be differentially segmented such that segmentation is customized for the sub-volume. Likewise, after separation of the imaged volume into sub-volumes, the acquisition parameters for each sub-volume may be customized for subsequent acquisitions.

Abstract: A technique is provided for modeling a network relationship via providing a graphical representation of the network relationship and labeling the graphical representation based on a symbol sequence. Further, one or more tools is provided by the present technique for interactively visualizing and/or analyzing the graphical representation of the network relationship based on the symbol sequence.

Abstract: A technique is provided for segmenting a structure of interest from a volume dataset. The technique identifies regions of the structure using templates having characteristics of the structure of interest. The identified regions may then undergo a constrained growth process using dynamic constraints that may vary based on local statistics associated with the identified structure regions. Edges within the volume may be determined using gradient data determined by evaluating the strongest gradient between each pixel and all adjacent pixels. The edge data may be used to prevent the constrained growing process from exceeding the boundaries of the structure of interest.

Abstract: A technique is provided for automatically generating a bone mask in CTA angiography. In accordance with the technique, an image data set may be pre-processed to accomplish a variety of function, such as removal of image data associated with the table, partitioning the volume into regionally consistent sub-volumes, computing structures edges based on gradients, and/or calculating seed points for subsequent region growing. The pre-processed data may then be automatically segmented for bone and vascular structure. The automatic vascular segmentation may be accomplished using constrained region growing in which the constraints are dynamically updated based upon local statistics of the image data. The vascular structure may be subtracted from the bone structure to generate a bone mask. The bone mask may in turn be subtracted from the image data set to generate a bone-free CTA volume for reconstruction of volume renderings.

Abstract: A technique is provided for partitioning an imaged volume into two or more sub-volumes. The technique identifies partition lines which separate the sub-volumes by generating a profile, such as a bone profile, which is then analyzed to determine the placement of the partition lines. In one embodiment, placement of the partition lines is determined automatically by applying one or more sets of hierarchical rules to the profile. After separation of the imaged volume into sub-volumes, each sub-volume may be differentially segmented such that segmentation is customized for the sub-volume. Likewise, after separation of the imaged volume into sub-volumes, the acquisition parameters for each sub-volume may be customized for subsequent acquisitions.