Abstract: An adaptive density correction (ADC) method and system automatically compensate for pseudo-enhancement (PEH) of voxels in computed tomography (CT) data, such as in fecal-tagged CT colonography (ftCTC), so air (or another low-contrast background) and soft tissues are represented by their usual CT attenuations. ADC estimates an amount of pseudo-enhancement energy that was received by voxels that are near tagged voxels (i.e., voxels that are tagged with a high-contrast agent), based on a first distribution scheme, such as a Gaussian distribution. ADC then iteratively distributes PEH energy received by voxels to neighboring voxels, according to another distribution scheme, which may be another Gaussian function. ADC then subtracts the total amount of PEH energy at each voxel from the CT data of the voxel.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: An adaptive density mapping (ADM) method and system automatically identify interface regions between air and material tagged with contrast agents in computed tomographic (CT) image data, then map CT attenuations of voxels outside the identified interface regions, such that voxels that represent tagged material are made to represent air or another gas.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

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 lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: An adaptive density correction (ADC) method and system automatically compensate for pseudo-enhancement (PEH) of voxels in computed tomography (CT) data, such as in fecal-tagged CT colonography (ftCTC), so air (or another low-contrast background) and soft tissues are represented by their usual CT attenuations. ADC estimates an amount of pseudo-enhancement energy that was received by voxels that are near tagged voxels (i.e., voxels that are tagged with a high-contrast agent), based on a first distribution scheme, such as a Gaussian distribution. ADC then iteratively distributes PEH energy received by voxels to neighboring voxels, according to another distribution scheme, which may be another Gaussian function. ADC then subtracts the total amount of PEH energy at each voxel from the CT data of the voxel.

Abstract: An adaptive density mapping (ADM) method and system automatically identify interface regions between air and material tagged with contrast agents in computed tomographic (CT) image data, then map CT attenuations of voxels outside the identified interface regions, such that voxels that represent tagged material are made to represent air or another gas.

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.