Analogic Corporation

Muzaffer Hiraoglu

Carl R Crawford

Sergey Simanovsky

Ibrahim M Bechwati

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. 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. 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.

Computed tomography apparatus and method for classifying objects

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. The overall system and/or object detection rate, and/or the false alarm rate and/or the overall false detection rate can be optimized by modifying at least one algorithm so as to adjust at least one of the object detection rates or object false alarm rate.

Apparatus and method for optimizing detection of objects in computed tomography data

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.

Architecture for CT scanning system

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. The system can also identify objects that contain liquids. The object detection rate and false alarm rate can be adjusted by adjusting individual object detection rates and/or false alarm rates.

Apparatus and method for combining related objects in computed

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.

Apparatus and method for detecting sheet objects in computed tomography

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. A merging process can be used to reconnect related items, such as multiple sticks, that were separated during an erosion step. The merging process allows multiple objects that would individually pass as non-threat items to be combined into a single item that is correctly classified as a threat. The system can also identify objects that contain liquids, if desired.

Apparatus and method for correcting object density in computed

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. 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. 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. 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. A merging process can be used to reconnect related items, such as multiple sticks, that were separated during an erosion step. The merging process allows multiple objects that would individually pass as non-threat items to be combined into a single item that is correctly classified as a threat. The system can also identify objects that contain liquids, if desired. The process of the invention can be carried out in multiple stages. The overall system performance, including overall object detection rate and false alarm rate, can be adjusted by adjusting individual object detection rates and/or false alarm rates.

Apparatus and method for classifying objects in computed tomography data

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.

Apparatus and method for density discrimination of objects in computed

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.

Apparatus and method for eroding objects in computed tomography data

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.

Apparatus and method for detecting objects in computed tomography data

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. That is, for example, a sheet object detection process and a bulk object detection process can be implemented separately and in parallel. During the sheet object detection process, the data can be analyzed to identify bulk objects. Those objects can then be removed from the data and the remaining data can then be analyzed to identify any remaining sheets. Redundant analysis of data is eliminated.

Multiple-stage apparatus and method for detecting objects in computed

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. The overall system performance, including overall object detection rate and false alarm rate, can be adjusted by adjusting individual object detection rates and/or false alarm rates.

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.

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Woburn, MA, United States of America

Muzaffer Hiraoglu