Abstract: A method of performing bone correction on a computerized tomography (CT) image includes the steps of: A. generating a reconstructed CT image of a bone-containing portion of a patient's body, the reconstructed CT image including a bone region corresponding to the bone-containing portion; B. determining characteristics of the bone region of the image; and C. performing a bone correction procedure on the image, the bone correction procedure being performed at a gain that is determined based on the bone region characteristics.

Abstract: A system for correcting for scatter in a computed tomography scanner includes a tunnel having a platform disposed therein for receiving an object to be scanned, an x-ray source for directing x-rays at the object to be scanned, a detector array including a plurality of primary detectors for receiving the x-rays and at least one secondary detector for receiving portion of the x-ray beam scattered within the tunnel. The system further includes processing means for reducing the effects of scatter in images of the object reconstructed from the x-rays detected by method of estimating an amount of scatter caused only by the presence of the object corrected by an amount of scattered x-ray when presence or absence of object.

Abstract: An apparatus and method for identifying and classifying objects represented in CT data for a region using the shape of the objects are disclosed. An object represented by CT data for a region is identified. Next, a two-dimensional projection of the object along a principal axis of the object is generated. The principal axis can be identified by computing eigenvectors of a covariance matrix of spatial locations of voxels in the CT data that are associated with the object. The smallest eigenvector can be selected as the principal axis of the object along which the two-dimensional projection is generated. The identification of the object can be used in the classification of the object such as by altering one or more discrimination parameters.

Abstract: A method and apparatus detects sheet explosives in computed tomography (CT) data. In particular, sheet-shaped objects such as sheet explosives can be discriminated from other object shapes and detected. The detection includes analyzing a neighborhood of voxels surrounding a test voxel. If the density of the test voxel is sufficiently different from the mean density of the neighboring voxels, then it is concluded that the test voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data so as to eliminate voxels associated with thin objects. Remaining objects are then subtracted from the original data, leaving only thin sheet-shaped objects. Erosion of the data can be performed by identifying a neighborhood of voxels surrounding a voxel of interest. If the number of voxels having densities below a predetermined threshold exceeds a predetermined number, then it is assumed that the test voxel is a surface voxel and is removed from the object.

Abstract: A method of and apparatus for detecting objects in computed tomography (CT) data includes the ability to define the types of objects to be detected, and at least one algorithm related to the detection of each type of object. Multiple types of objects can be detected and distinguished from one another. Each type of object exhibits an object detection rate related to the probability of the system detecting the corresponding object type, and a false detection rate related to the false identification of objects, different from the target objects, as the target objects. An overall system detection rate is related to a combination of the object detection rates. Each type of object can also be associated with a unique object false alarm rate, with a overall false detection rate being related to the combination of object false alarm rates.

Abstract: A method and apparatus for detecting concealed objects in computed tomography data are disclosed. Sheet-shaped objects such as sheet explosives can be detected by a CT scanning system, in particular, a CT baggage scanning system. The invention analyzes CT voxels in a subregion in proximity to the sheet object to determine if the sheet object is concealed in an electronic device or is “sandwiched” within an item such as a book or magazine. To detect electronic concealment, the number of voxels in a subregion that contains the object having a density above a predetermined threshold is counted and the ratio of that number of voxels to the number of object voxels is computed. If the ratio exceeds a threshold, then it is concluded that the object is concealed in electronics. In response, the CT scanning system can alter discrimination parameters to allow the object to be classified as a threat. For “sandwich” concealment, layers on opposite sides of a sheet object are examined.

Abstract: A system for and method of detecting and classifying objects contained within or concealed by items scanned by an X-ray scanner is described. Greater throughput and relatively reduced cost is achieved by using a shared bulk memory for entering imaging data received from the scanner into slots of a bulk shared memory; and storing detection and classification data in slots of the bulk memory after processing imaging data so as to provide detection and classification data relating to absence or suspected presence of predetermined target objects. Preferably, the scanner is a CT scanner and the imaging data is CT data.

Abstract: Sheet-shaped objects can be detected by analyzing a neighborhood of voxels surrounding a test voxel. If the density is sufficiently different, then the voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data so as to eliminate voxels associated with thin objects. Remaining objects are then subtracted from the original data, leaving only thin sheet-shaped objects. If the number of voxels having densities below a predetermined threshold exceeds a predetermined number, then it is assumed that the test voxel is a surface voxel and is removed from the object. A connectivity process can be applied to voxels to combine them into objects after sheets are detected. A dilation function can then be performed to replace surface voxels. A corrected mass can be compared to mass thresholds. Bulk objects can be detected by a modified morphological connected components labeling (CCL) approach. A merging process can be used to reconnect related items.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data, including sheet-shaped objects such as sheet explosives can be detected by analyzing a neighborhood of voxels surrounding a test voxel. If the density of the test voxel is sufficiently different from the mean density of the neighboring voxels, then it is concluded that the test voxel is associated with a sheet object. A connectivity process can be applied to voxels to combine them into objects after sheets are detected to prevent sheets from being inadvertently removed from the data by erosion.

Abstract: Sheet explosives can be detected by analyzing voxels surrounding a test voxel. If the density is sufficiently different, it is concluded that the test voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data then subtracting from the original data, leaving only thin sheet-shaped objects. A connectivity process can be applied to voxels to combine them into objects after sheets are detected. A dilation function can then be performed to replace surface voxels. A corrected mass can be computed and compared to mass thresholds to classify the object as to whether it poses a threat. Multiple mass thresholds can be used. Bulk objects can be detected by a modified morphological connected components labeling (CCL) approach which performs a series of erosion and dilation steps to separate adjacent objects in the data such that they can be individually labeled and analyzed.

Abstract: A method and apparatus for detecting and classifying objects in computed tomography (CT) data are disclosed. A connectivity process can be applied to voxels in the data to combine them into objects. A dilation function can then be performed on the eroded object to replace surface voxels removed by erosion. A corrected mass using the mean eroded density of the object can be computed and compared to mass thresholds to classify the object as to whether it poses a threat. Multiple mass thresholds can be used, each of which is associated with a particular density range based on the density of an expected threat object.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data are disclosed. Sheet-shaped objects such as sheet explosives can be detected by analyzing a neighborhood of voxels surrounding a test voxel. If the density of the test voxel is sufficiently different from the mean density of the neighboring voxels, then it is concluded that the test voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data so as to eliminate voxels associated with thin objects. Remaining objects are then subtracted from the original data, leaving only thin sheet-shaped objects. Erosion of the data can be performed by identifying a neighborhood of voxels surrounding a voxel of interest. If the number of voxels having densities below a predetermined threshold exceeds a predetermined number, then it is assumed that the test voxel is a surface voxel and is removed from the object.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data, including sheet-shaped objects such as sheet explosives, analyze a neighborhood of voxels surrounding a test voxel. Erosion of the data can be performed by identifying a neighborhood of voxels surrounding a voxel of interest. If the number of voxels having densities below a predetermined threshold exceeds a predetermined number, then it is assumed that the test voxel is a surface voxel and is removed from the object. A connectivity process can be applied to voxels to combine them into objects after sheets are detected to prevent sheets from being inadvertently removed from the data by erosion. A dilation function can then be performed on the eroded object to replace surface voxels removed by erosion.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data are disclosed. Erosion of the data can be performed by identifying a neighborhood of voxels surrounding a voxel of interest. If the number of voxels having densities below a predetermined threshold exceeds a predetermined number, then it is assumed that the voxel of interest is a surface voxel of an object and is removed from the object. A connectivity process is then applied to remaining voxels to combine them into objects. A dilation function can then be performed on the eroded object to replace surface voxels removed by erosion. This morphological connected components labeling (CCL) approach separates adjacent objects in the data such that they can be individually labeled and analyzed.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data are disclosed. Sheet-shaped objects such as sheet explosives can be detected by analyzing a neighborhood of voxels surrounding a test voxel. If the density of the test voxel is sufficiently different from the mean density of the neighboring voxels, then it is concluded that the test voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data so as to eliminate voxels associated with thin objects. Remaining objects are then subtracted from the original data, leaving only thin sheet-shaped objects. Bulk objects can be detected by a modified morphological connected components labeling (CCL) approach which performs a series of erosion and dilation steps to separate adjacent objects in the data such that they can be individually labeled and analyzed. The process of the invention can be carried out in multiple paths.

Abstract: A method and apparatus for detecting objects in computed tomography (CT) data are shown. Specifically, the system can identify objects that contain liquids, if desired. A subregion that encloses an object is defined, and a top surface of the subregion is identified. Volume elements at the surfaces of the object are identified. If a ratio of top surface volume elements to the total number of surface volume elements is determined. If that ratio exceeds a predetermined threshold, then the object can be identified as including a liquid.

Abstract: Sheet-shaped objects can be detected by analyzing voxels surrounding a test voxel. If the density different from the mean density then the test voxel is associated with a sheet object. Sheet objects can also be detected by eroding the CT data. Remaining objects are then subtracted from the original data removing with surface voxels, being removed. A connectivity process combines voxels into objects after sheets are detected A dilation function replaces surface voxels removed by erosion. A corrected mass can be compared to mass thresholds to classify the object. Multiple mass thresholds can be used, each of which is associated with a particular density range based on the density of an expected threat object. Bulk objects can be detected by a modified morphological connected components labeling (CCL) approach. A merging process can be used to reconnect related items, The system can also identify objects that contain liquids.