Abstract: A method, system, and computer program product for identifying at least one three-dimensionally extended lesion within a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ. The method includes: (1) generating a set of voxels representing a total scanned volume from a set of cross-sectional images of the target organ; (2) performing segmentation to extract a set of voxels representing the volumetric region from the set of voxels representing the total scanned volume; (3) detecting a set of candidate lesions based on geometric feature values of each voxel in the set of voxels representing the volumetric region; and (4) selecting the at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric, morphologic, and texture feature values of each lesion in the set of candidate lesions.

Abstract: A visualization method and system for virtual endoscopic examination of CT colonographic data by use of shape-scale analysis. The method provides each colonic structure of interest with a unique color, thereby facilitating rapid diagnosis of the colon. Two shape features, called the local shape index and curvedness, are used for defining the shape-scale spectrum. The shape index and curvedness values within CT colonographic data are mapped to the shape-scale spectrum in which specific types of colonic structures are represented by unique characteristic signatures in the spectrum. The characteristic signatures of specific types of lesions can be determined by use of computer-simulated lesions or by use of clinical data sets subjected to a computerized detection scheme. The signatures are used for defining a 2-D color map by assignment of a unique color to each signature region.

Abstract: A method, system, and computer program product for identifying at least one three-dimensionally extended lesion within a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ. The method includes: (1) generating a set of voxels representing a total scanned volume from a set of cross-sectional images of the target organ; (2) performing segmentation to extract a set of voxels representing the volumetric region from the set of voxels representing the total scanned volume; (3) detecting a set of candidate lesions based on geometric feature values of each voxel in the set of voxels representing the volumetric region; and (4) selecting the at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric, morphologic, and texture feature values of each lesion in the set of candidate lesions.