Abstract: Among other things, one or more systems and/or techniques for segmenting a representation of a sheet object from an image are provided herein. To identify elements of an image (e.g., pixels and/or voxels) representative of sheet objects, a constant false alarm rate (CFAR) score and a topological score are computed for respective elements being analyzed. The CFAR score indicates a relationship between an element and a neighborhood of elements when viewed as a collective unit. The topological score indicates a relationship between the element and a neighborhood of elements when viewed neighbor-by-neighbor. When the CFAR score is within a specified range of CFAR scores and the topological score is within a specified range of topological scores, the element is labeled as being associated with a sheet object. A connected component labeling (CCL) approach may be used to group elements labeled as being associated with a sheet object.

Abstract: Multi-energy imaging is afforded with a single detector array by generating a first data set, indicative of a first radiation energy spectrum, using a first set of cells of the array, and by generating a second data set, indicative of a second radiation energy spectrum, using a second set of cells of the array (e.g., where substantially more cells are in the first set than the second). The first data set is comprised of measured data from the first set of cells and estimated data that would have been yielded from the second set of cells had the second set been configured to detect the first energy spectrum. The second data set is comprised of measured data yielded from the second set of cells and estimated data that would have been yielded from the first set of cells had the first set been configured to detect the second energy spectrum.

Abstract: Among other things, one or more systems and/or techniques for segmenting a representation of a sheet object from an image are provided herein. To identify elements of an image (e.g., pixels and/or voxels) representative of sheet objects, a constant false alarm rate (CFAR) score and a topological score are computed for respective elements being analyzed. The CFAR score indicates a relationship between an element and a neighborhood of elements when viewed as a collective unit. The topological score indicates a relationship between the element and a neighborhood of elements when viewed neighbor-by-neighbor. When the CFAR score is within a specified range of CFAR scores and the topological score is within a specified range of topological scores, the element is labeled as being associated with a sheet object. A connected component labeling (CCL) approach may be used to group elements labeled as being associated with a sheet object.

Abstract: One or more systems and/or techniques are provided to identify objects comprised in a compound object without segmenting three-dimensional image data of the potential compound object. Two-dimensional projections of a potential compound object (e.g., Eigen projections) are examined to identify the presence of known objects. The projections are compared to signatures, such as morphological characteristics, of one or more known objects. If it is determined based upon the comparison that there is a high likelihood that the compound object comprises a known object, a portion of the projection is masked, and it is compared again to the signature to determine if this likelihood has increased. If it has, a sub-object of the compound object may be classified based upon characteristics of the known object (e.g., the compound object may be classified as a potential threat item if the known object is a threat item).

Abstract: Multi-energy imaging is afforded with a single detector array by generating a first data set, indicative of a first radiation energy spectrum, using a first set of cells of the array, and by generating a second data set, indicative of a second radiation energy spectrum, using a second set of cells of the array (e.g., where substantially more cells are in the first set than the second). The first data set is comprised of measured data from the first set of cells and estimated data that would have been yielded from the second set of cells had the second set been configured to detect the first energy spectrum. The second data set is comprised of measured data yielded from the second set of cells and estimated data that would have been yielded from the first set of cells had the first set been configured to detect the second energy spectrum.

Abstract: Potential threat items may be concealed inside objects, such as portable electronic devices, that are subject to imaging for example, at a security checkpoint. Data from an imaged object can be compared to pre-determined object data to determine a class for the imaged object. Further, an object can be identified inside a container (e.g., a laptop inside luggage). One-dimensional Eigen projections can be used to partition the imaged object into partitions, and feature vectors from the partitions and the object image data can be used to generate layout feature vectors. One or more layout feature vectors can be compared to training data for threat versus non-threat-containing items from the imaged object's class to determine if the imaged object contains a potential threat item.

Abstract: Representations of an object in an image generated by an imaging apparatus can comprise two or more separate sub-objects, producing a compound object. Compound objects can negatively affect the quality of object visualization and threat identification performance. As provided herein, a compound object can be separated into sub-objects. Topology score map data, representing topological differences in the potential compound object, may be computed and used in a statistical distribution to identify modes that may be indicative of the sub-objects. The identified modes may be assigned a label and a voxel of the image data indicative of the potential compound object may be relabeled based on the label assigned to a mode that represents data corresponding to properties of a portion of the object that the voxel represents to create image data indicative of one or more sub-objects.

Abstract: One or more systems and/or techniques are provided to identify objects comprised in a compound object without segmenting three-dimensional image data of the potential compound object. Two-dimensional projections of a potential compound object (e.g., Eigen projections) are examined to identify the presence of known objects. The projections are compared to signatures, such as morphological characteristics, of one or more known objects. If it is determined based upon the comparison that there is a high likelihood that the compound object comprises a known object, a portion of the projection is masked, and it is compared again to the signature to determine if this likelihood has increased. If it has, a sub-object of the compound object may be classified based upon characteristics of the known object (e.g., the compound object may be classified as a potential threat item if the known object is a threat item).

Abstract: The techniques described herein provide for correcting projection data that comprises contamination due to source switching in a multi energy scanner. The correction is a multi-neighbor correction. That is, it uses data from at least two other views of an object (e.g., generally a previous view and a subsequent view) to correct a current view of the object. The multi-neighbor correction may use one or more correction factors to determine how much data from the other two views to use to correct the current view. The correction factor(s) are determined based upon a calibration that utilizes image space data and/or projection space data of a phantom. In this way, the correction factor(s) account for source leakage that occurs in multi energy scanners.

Abstract: Representations of an object in an image generated by an imaging apparatus can comprise two or more separate sub-objects, producing a compound object. Compound objects can negatively affect the quality of object visualization and threat identification performance. As provided herein, a compound object can be separated into sub-objects. Topology score map data, representing topological differences in the potential compound object, may be computed and used in a statistical distribution to identify modes that may be indicative of the sub-objects. The identified modes may be assigned a label and a voxel of the image data indicative of the potential compound object may be relabeled based on the label assigned to a mode that represents data corresponding to properties of a portion of the object that the voxel represents to create image data indicative of one or more sub-objects.

Abstract: The techniques described herein provide for correcting projection data that comprises contamination due to source switching in a multi energy scanner. The correction is a multi-neighbor correction. That is, it uses data from at least two other views of an object (e.g., generally a previous view and a subsequent view) to correct a current view of the object. The multi-neighbor correction may use one or more correction factors to determine how much data from the other two views to use to correct the current view. The correction factor(s) are determined based upon a calibration that utilizes image space data and/or projection space data of a phantom. In this way, the correction factor(s) account for source leakage that occurs in multi energy scanners.

Abstract: Potential threat items may be concealed inside objects, such as portable electronic devices, that are subject to imaging for example, at a security checkpoint. Data from an imaged object can be compared to pre-determined object data to determine a class for the imaged object. Further, an object can be identified inside a container (e.g., a laptop inside luggage). One-dimensional Eigen projections can be used to partition the imaged object into partitions, and feature vectors from the partitions and the object image data can be used to generate layout feature vectors. One or more layout feature vectors can be compared to training data for threat versus non-threat-containing items from the imaged object's class to determine if the imaged object contains a potential threat item.

Abstract: The disclosed CT scanner comprises at least one source of X-rays; a detector array comprising a plurality of detectors; and an X-ray filter mask arrangement disposed between the source of X-rays and detector array so as to modify the spectra of the X-rays transmitted from the source through the mask to at least some of the detectors so that the X-ray spectra detected by at least one set of detectors is different from the X-ray spectra detected by at least one other set of detectors.

Abstract: The disclosed CT scanner comprises at least one source of X-rays; a detector array comprising a plurality of detectors; and an X-ray filter mask arrangement disposed between the source of X-rays and detector array so as to modify the spectra of the X-rays transmitted from the source through the mask to at least some of the detectors so that the X-ray spectra detected by at least one set of detectors is different from the X-ray spectra detected by at least one other set of detectors.