WO1999041676A1 - Appareil de tomodensitometrie et procede de classement d'objets - Google Patents

Appareil de tomodensitometrie et procede de classement d'objets Download PDF

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Publication number
WO1999041676A1
WO1999041676A1 PCT/US1999/001514 US9901514W WO9941676A1 WO 1999041676 A1 WO1999041676 A1 WO 1999041676A1 US 9901514 W US9901514 W US 9901514W WO 9941676 A1 WO9941676 A1 WO 9941676A1
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WIPO (PCT)
Prior art keywords
data
density
region
objects
volume elements
Prior art date
Application number
PCT/US1999/001514
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English (en)
Inventor
Carl R. Crawford
Muzaffer Hiraoglu
Ibrahim M. Bechwati
Sergey Simanovsky
Original Assignee
Analogic Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US09/021,889 external-priority patent/US6078642A/en
Priority claimed from US09/022,354 external-priority patent/US6108396A/en
Priority claimed from US09/022,062 external-priority patent/US6272230B1/en
Priority claimed from US09/022,064 external-priority patent/US6026171A/en
Priority claimed from US09/022,059 external-priority patent/US6317509B1/en
Priority claimed from US09/021,781 external-priority patent/US6075871A/en
Priority claimed from US09/022,165 external-priority patent/US6026143A/en
Priority claimed from US09/022,164 external-priority patent/US6035014A/en
Priority claimed from US09/021,782 external-priority patent/US6076400A/en
Priority claimed from US09/022,060 external-priority patent/US6128365A/en
Priority claimed from US09/022,189 external-priority patent/US6111974A/en
Priority claimed from US09/022,204 external-priority patent/US6067366A/en
Application filed by Analogic Corporation filed Critical Analogic Corporation
Priority to JP2000531792A priority Critical patent/JP2002503816A/ja
Priority to EP99904247A priority patent/EP1062555A4/fr
Priority to AU24689/99A priority patent/AU2468999A/en
Publication of WO1999041676A1 publication Critical patent/WO1999041676A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30112—Baggage; Luggage; Suitcase

Definitions

  • the present invention relates generally to computed tomography (CT) scanners and more specifically to a target detection apparatus and method in a baggage scanning system which utilizes CT technology.
  • CT computed tomography
  • Various X-ray baggage scanning systems are known for detecting the presence of explosives and other prohibited items in baggage or luggage prior to loading the baggage onto a commercial aircraft. Since many explosive materials may be characterized by a range of densities differentiable from that of other items typically found in baggage, explosives are generally amenable to detection by X- ray equipment.
  • a common technique of measuring a material's density is to expose the material to X-rays and to measure the amount of radiation absorbed by the material, the absorption being indicative of the density.
  • X-ray baggage scanning systems in use today are of the "line scanner" type and include a stationary X-ray source, a stationary linear detector array, and a conveyor belt for transporting baggage between the source and detector array as the baggage passes through the scanner.
  • the X-ray source generates an X-ray beam that passes through and is partially attenuated by the baggage and is then received by the detector array.
  • the detector array During each measuring interval the detector array generates data representative of the integral of density of the planar segment of the baggage through which the X-ray beam passes, and these data are used to form one or more raster lines of a two-dimensional image.
  • the scanner As the conveyor belt transports the baggage past the stationary source and detector array, the scanner generates a two- dimensional image representative of the density of the baggage, as viewed by the stationary detector array.
  • the density image is typically displayed for analysis by a human operator, or it can be analyzed by computer.
  • detection of suspected baggage can require very attentive operators. The requirement for such attentiveness can result in greater operator fatigue, and fatigue as well as any distractions can result in a suspected bag passing through the system undetected.
  • Techniques using dual energy X-ray sources are known for providing additional information about a material's chemical characteristics, beyond solely a density measurement. Techniques using dual energy X-ray sources involve measuring the X-ray absorption characteristics of a material for two different energy levels of X-rays.
  • Certain types of explosives present a particular challenge to baggage scanning systems because, due to their moldable nature, they may be formed into geometric shapes that are difficult to detect. Many explosives capable of significantly damaging an aircraft are sufficiently large in length, width, and height so as to be readily detectable by an X-ray scanner system regardless of the explosive's orientation within the baggage. Another problem with some explosives is that they can be hidden inside an object such as a piece of electronic equipment, e.g., a lap top computer. These can be difficult to detect with traditional line scanning techniques. Also, an explosive powerful enough to damage an aircraft may be formed into a relatively thin sheet that is extremely small in one dimension and is relatively large in the other two dimensions. The detection of explosives may be difficult because it may be difficult to see the explosive material in the image, particularly when the material is disposed so that the thin sheet is perpendicular to the direction of the X-ray beam as the sheet passes through the system.
  • a system using CT technology typically includes a CT scanner of the third generation type, which typically includes an X-ray source and an X-ray detector system secured to diametrically opposite sides of an annular-shaped platform or disk.
  • the disk is rotatably mounted within a gantry support so that in operation the disk continuously rotates about a rotation axis while X-rays pass from the source
  • the detector system can include a linear array of detectors disposed as a single row in the shape of a circular arc having a center of curvature at the focal spot of the X-ray source, i.e., the point within the X-ray source from which the X- rays emanate.
  • the X-ray source generates a fan-shaped beam, or fan beam, of X- rays that emanates from the focal spot, passes through a planar imaging field, and is received by the detectors.
  • the CT scanner includes a coordinate system defined by X-, Y- and Z-axes, wherein the axes intersect and are all normal to one another at the center of rotation of the disk as the disk rotates about the rotation axis.
  • the Z-axis is defined by the rotation axis and the X- and Y-axes are defined by and lie within the planar imaging field.
  • the fan beam is thus defined as the volume of space defined between a point source, i.e., the focal spot, and the receiving surfaces of the detectors of the detector array exposed to the X-ray beam. Because the dimension of the receiving surfaces of the linear array of detectors is relatively small in the Z- axis direction the fan beam is relatively thin in that direction.
  • Each detector generates an output signal representative of the intensity of the X-rays incident on that detector. Since the X-rays are partially attenuated by all the mass in their path, the output signal generated by each detector is representative of the density of all the mass disposed in the imaging field between the X-ray source and that detector.
  • the detector array is periodically sampled, and for each measuring interval each of the detectors in the detector array generates an output signal representative of the density of a portion of the object being scanned during that interval.
  • the collection of all of the output signals generated by all the detectors in a single row of the detector array for any measuring interval is referred to as a "projection, " and the angular orientation of the disk (and the corresponding angular orientations of the X-ray source and the detector array) during generation of a projection is referred to as the "projection angle. " At each projection angle, the path of the X-rays from the focal spot to each detector, called a "ray , " increases in cross section from a point source to the receiving surface area of the detector,
  • the scanner As the disk rotates around the object being scanned, the scanner generates a plurality of projections at a corresponding plurality of projection angles.
  • a CT image of the object may be generated from all the projection data collected at each of the projection angles.
  • the CT image is representative of the density of a two dimensional "slice" of the object through which the fan beam has passed during the rotation of the disk through the various projection angles.
  • the resolution of the CT image is determined in part by the width of the receiving surface area of each detector in the plane of the fan beam, the width of the detector being defined herein as the dimension measured in the same direction as the width of the fan beam, while the length of the detector is defined herein as the dimension measured in a direction normal to the fan beam parallel to the rotation or Z-axis of the scanner.
  • the InVision Machine includes a CT scanner of the third generation type, which typically include an X-ray source and an X-ray detector system secured respectively to diametrically opposite sides of an annular-shaped platform or disk.
  • the disk is rotatably mounted within a gantry support so that in operation the disk continuously rotates about a rotation axis while X-rays pass from the source through an object positioned within the opening of the disk to the detector system.
  • CT scanners of the type described in the '764 and '552 patents take a relatively long time, e.g., from about 0.6 to about 2.0 seconds, for one revolution of the disk to
  • CT -7- generate the data for a single sliced CT image. Further, the thinner the slice of the beam through the bag for each image, the better the resolution of the image.
  • the CT scanner should provide images of sufficient resolution to detect plastic explosives on the order of only a few millimeters thick. Therefore, to provide adequate resolution, many revolutions are required.
  • a conventional CT baggage scanner such as the InVision Machine can only afford to generate a few CT images per bag. Clearly, one cannot scan the entire bag within the time allotted for a reasonably fast throughput. Generating only a few CT images per baggage items leaves most of the item unscanned and therefore does not provide scanning adequate to identify all potential threat objects in the bag, such as sheets of explosive material.
  • the InVision Machine uses a pre-screening process which produces a two-dimensional projection image of the entire bag from a single angle. Regions of the projection identified as potentially containing threat items can then be subjected to a full scan or manual inspection. With this pre-screening and selective region scanning approach, the entire bag is not scanned, thus allowing potential threat items to pass through undetected. This is especially true in the case of sheet items oriented transversely to the direction of propagation of the radiation used to form the pre-screen projection and where the sheet covers a relatively large portion of the area of the bag.
  • the Eberhard et al. publication teaches that its system can identify thin objects.
  • the system sets its labeling density at a low level such that thin objects viewed edge-on which partially fill a voxel can be detected.
  • a significant drawback to the Eberhard et al. system is that it may miss thin objects such as sheet explosives that are not viewed edge-on and which cover a large area of the bag. These transversely oriented sheet objects will add only slightly to the density measured for the bag and will have only small density contrast with the background. If the density threshold used during CCL is set low enough to detect these sheets, then, because of the low contrast between the sheet and the background, the entire bag will be connected and labeled together, and no discernable object will be identified. If the threshold is set higher, then the sheet object will be missed.
  • the baggage scanning equipment prefferably analyzes the acquired density data and determine if the data indicate the presence of any contraband items, e.g., explosives.
  • This automatic explosive detection process should have a relatively high detection rate such that the chances of missing an explosive in a bag are small.
  • the false alarm rate of the system should be relatively low to substantially reduce or eliminate false alarms on innocuous items. Because of practical considerations of baggage throughput at large commercial airports, a high false alarm rate could reduce system performance speed to a prohibitively low rate.
  • the present invention is directed to an object identification apparatus and method and a computed tomography (CT) baggage scanning system and method
  • the object identification apparatus and method of the invention analyze acquired CT density data for a region to detect objects in the data.
  • the region can include at least a portion of the inside of a container such as a piece of baggage or luggage.
  • Detected objects can then be labeled according to their physical configuration. For example, in one embodiment, objects can be labeled as being bulk objects or sheet objects.
  • objects after objects are detected and labeled, they are discriminated, that is, they are classified as being threat objects or non-threat objects.
  • the invention uses a sheet detection process which identifies thin sheet-shaped objects.
  • each voxel is analyzed by comparing its density to that of its neighboring voxels.
  • the mean and standard deviation of the densities of the neighboring voxels are computed.
  • the difference between the density of the voxel being analyzed and the mean density of the neighboring voxels is compared to a predetermined threshold difference, which can be related to the standard deviation of the densities of the neighboring voxels. If the density of the voxel of interest differs from the mean density by more than the predetermined threshold difference, then it is concluded that the voxel of interest is associated with a thin object, e.g., a sheet.
  • the voxels can be analyzed one at a time and can be individually labeled according to whether they are associated with a sheet object.
  • the set of labeled voxels can be analyzed to group associated voxels into objects.
  • a standard connected components labeling (CCL) approach is used to group neighboring voxels of similar densities into sheets.
  • CCL connected components labeling
  • each voxel labeled as a sheet voxel is compared to neighboring sheet voxels to determine the difference between their densities. If the difference in density is below a predetermined density difference threshold, then it is assumed that the two neighboring voxels belong to the same object, i.e., sheet. This process
  • the apparatus and method of the invention can also classify objects such as detected sheet objects as being threat objects or non-threat objects. In one embodiment, this is done by comparing the mass of the objects to a predetermined threshold mass. If the mass of an object is above the predetermined mass threshold, then it is concluded that the object is a threat object. When a bag is identified as containing a threat object, it can be marked for further analysis. The bag can be identified for further inspection by the operator or an image of the entire interior of the bag can be produced from the density data.
  • the present invention also provides for the identification and classification of bulk objects, such as bulk explosives, in the acquired CT density data for a region such as the interior of a piece of luggage or baggage.
  • the bulk detection process of the invention uses a modified connected components labeling (CCL) process to identify bulk objects.
  • CCL connected components labeling
  • neighboring voxels having density values which differ by less than a predetermined threshold are labeled as being part of the same object.
  • Each voxel is analyzed and compared to its neighbors to combine the voxels into objects.
  • This common CCL approach has a drawback in that objects that are close together or that touch each other and have similar densities may be combined into a single object.
  • the modified CCL approach of the invention separates these objects into individually labeled objects.
  • the approach of the invention applies a "morphological" CCL method.
  • Each object is first “eroded,” by removing all of its surface voxels. This tends to separate connected objects into multiple individual objects. The separated objects are then separately labeled. Next, a “dilation” step is applied in which surface voxels are added back to identified and labeled objects.
  • this morphological approach to CCL allows objects in close proximity to each other to be separately identified and labeled. The objects can then be separately discriminated and classified as being threats or non-threats.
  • one standard erosion process identifies a surface voxel as being any voxel having at least one neighboring voxel whose density is below a predetermined threshold. This assumes that all the voxels being analyzed adjacent to a neighboring voxel below the threshold are surface voxels. These identified surface voxels are then removed from the object.
  • a drawback to this approach is that there are circumstances under which a voxel that is not at the surface of the object will be removed. For example, an object with an interior void region, such as a cylindrical, stick-shaped object with an interior, axial, thin, cylindrical hole, will have voxels around the outside of the void region removed. The undesirable result is that the interior void region is enlarged by the erosion process.
  • erosion is performed in such a way that the probability of removing a non-surface voxel is reduced.
  • a plurality of neighboring voxels is identified.
  • the neighboring voxels define a three-dimensional subregion or neighborhood which surrounds the voxel of interest.
  • the subregion can be cube- shaped.
  • Each voxel in the subregion is analyzed to determine if its density is within one or more predetermined ranges of densities.
  • the number of voxels in the associated subregion whose densities fall within the predetermined range of densities is compared to a threshold. If the number is lower than the threshold, then it is concluded that the voxel of interest is an object surface voxel, and the voxel is removed from the object.
  • the predetermined range of densities is determined based on the density of the voxel of interest.
  • the range is selected to be a range that includes the density of the voxel of interest.
  • the analysis determines the number of voxels in the subregion that are in the same density range. If that number does not exceed a threshold, then it is concluded that the voxel of interest is at a surface of an object, and the voxel is removed from the object.
  • the predetermined range of densities is selected from a plurality of ranges, each of
  • the density of the voxel of interest determines the potential threat material and, therefore, the selected density range. For example, if the density of the voxel of interest indicates that it is a bulk explosive material, the density range for a bulk explosive material is selected for analysis of the subregion surrounding the voxel of interest.
  • the voxel of interest is concluded to be a surface voxel, and it is removed from the object.
  • This approach to erosion in accordance with the invention reduces the possibility of enlarging interior voids in the object and increases the likelihood of removing only exterior surface voxels.
  • the dilation step of the morphological CCL approach is applied to produce a more accurate measure of the size and, therefore, the mass, of an object.
  • the density assigned to the added voxel is the average eroded density of the bulk object. That is, the average density of all of the voxels of an eroded object is computed.
  • each voxel added to the surface of the eroded object is assumed to have a density at the average eroded density.
  • sheet objects can be detected in the density data by using a morphology approach analogous to the morphological CCL applied in bulk object detection.
  • a morphological sheet detection approach analogous to the morphological CCL applied in bulk object detection.
  • all objects in the data are eroded a predetermined number of times such that all thin sheet shaped objects are eliminated from the data.
  • the number of erosions performed is based on the number of erosions needed to eliminate sheet objects from the data, which is related to the thickness of a sheet.
  • Each erosion can remove one layer of surface voxels. Therefore, the number of erosions is related to the expected thickness of a sheet and the size of a voxel.
  • -13- erosion steps are performed, the voxels remaining in the data are assumed to be associated with bulk objects. Then, dilation can be performed to restore the bulk objects to their original size. The data associated with these objects can then be eliminated from further processing.
  • the original data, with the bulk objects removed, are then analyzed to label the sheet objects.
  • the remaining voxels are analyzed one at a time such as by the CCL process to combine voxels into sheet objects and then label the sheet objects.
  • discrimination is performed on the sheet objects to classify them as threats or non-threats, such as by comparing the objects mass to a predetermined mass threshold. Sheets with masses above the threshold can be classified as threats.
  • An optional CCL step can be performed between the erosion steps and the dilation step to identify objects in the eroded data. Then, the subsequent dilation and subtraction steps may be performed only on objects which exceed a predetermined size or mass.
  • at least two sheet detection processes can be applied to the data for a region to identify voxels associated with sheet-shaped objects. These two approaches include the CFAR method and the morphological erosion-dilation method described above. Either approach can produce a set of binary data associated with the voxels, which binary data define each voxel as either being part of a sheet or not being part of a sheet.
  • a voxel connection approach such as the morphological CCL of the invention, standard CCL, or other connectivity method, is performed to connect the voxels into objects.
  • the object connection process does not eliminate sheets from the data and thereby make them impossible to detect.
  • the connection approach can be applied to the binary data generated by the sheet detection method, or it can be applied to the product of the binary data and the density data, i.e., the density data for voxels identified as being sheet voxels.
  • separate objects which should be considered as a single threat are combined or merged.
  • a merging process of the invention identifies such separated objects and combines them such that they can be identified as a threat.
  • the merging process of the invention identifies objects that are close to each other and also have similar or equal densities and combines them into a single object.
  • a bounding box is computed for each object. The objects are compared for similar densities. If the difference in object densities is below a predetermined threshold and the absolute density of one or both of the objects is within a predetermined density range defining multiple- object threats, then the distance between the bounding boxes is determined. If the distance between bounding boxes is below a predetermined threshold and the objects are considered to be in close enough proximity to be considered a single object, then it is concluded that the objects should be combined into a single object. A total mass of all of the individual objects is computed and compared to the threat mass threshold. If the total mass exceeds the threshold, then the combined object is concluded to be a threat.
  • the invention can merge multiple small sheet objects into a single sheet object.
  • analysis of three- dimensional CT images of actual bags has identified the effect that a high density object such as a metallic bar can obscure and/or interrupt the image of a large sheet, making it appear as multiple images of separate individual sheets.
  • the single large sheet object can be identified as multiple smaller objects.
  • the multiple smaller objects may be small enough, i.e., have low enough mass, such that all of them will be classified as non-threat items. This is especially a problem where the object should be classified as a threat and would be so classified if the system
  • each sheet object is associated with a plane.
  • the planes for each sheet are examined in three-dimensional space. If the planes intersect and their intersection is close to the sheets, then it is concluded that the individual sheets are actually part of a larger sheet.
  • the masses of the individual sheets are combined into a single value which is compared to the mass threshold during discrimination. If the mass of the combined sheet exceeds the mass threshold, then it is concluded that the sheet is a threat.
  • mass discrimination is used to classify the objects.
  • the mass of each identified object is computed by multiplying the density of each voxel by its volume and then totaling all of the individual voxel masses. The total object mass is then compared to a mass threshold. If the mass of the object exceeds the threshold, then it is concluded to be a threat object.
  • the mass threshold used for an object can be determined based on the type of object. That is, different mass thresholds are used for different types of objects. For example, a sheet object may be compared to one threshold while a powder explosive may be compared to a different mass threshold. This is due to the fact that different explosives pose different threats depending upon their masses. A large amount of one type of explosive may not pose as serious a threat as a smaller amount of a different type of explosive. Hence, in the present invention, mass thresholds can be selected based on the type of explosive. In one embodiment, the selection of mass threshold is determined by the density of the identified object, since it is the density that is closely related to the type of object identified.
  • the density of one type of explosive is in general different from the density of another type of explosive.
  • These individual densities are used to identify the type of explosive and, therefore, determine the mass threshold to be used in classifying an object as a threat.
  • This density-dependent mass thresholding of the invention provides a much more accurate threat
  • calculation of the total mass of an object is enhanced to improve the threat classification accuracy of the system.
  • surface voxels of an object can be eroded from the object.
  • an erosion step can be performed to eliminate the effects of partial volume voxels located at the surface of the object. These voxels introduce inaccuracies because their density values contain density contributions from both the object and the background at the boundary of the object.
  • erosion is performed to remove the surface voxels.
  • an average eroded density for the remaining object voxels is computed.
  • the average eroded density is the average of the voxel densities remaining in the object after the erosion step.
  • the eroded surface voxels are replaced with voxels having density values equal to the average eroded density.
  • the total mass can then be computed for the object using the surface voxels having the average eroded density value. This corrected total object mass provides more accurate classification of objects during subsequent mass discrimination.
  • accepted densities are defined in multiple density ranges with gaps between them in which densities would not be accepted to associate the voxel with an object of interest. That is, voxels having densities in the gaps are rejected and voxels within one of the density ranges are accepted as belonging to objects of interest.
  • the accepted density ranges can be selected according to densities of known threat objects. For example, a density range may be selected for each of several different types of known explosives.
  • a gap between density ranges is selected to coincide with the expected density of typical surface voxels. By rejecting these surface voxels, multiple adjacent objects which would otherwise be combined and labeled as a single object are separated and labeled as individual objects. As separate objects, they can be independently analyzed and classified
  • the object identification and classification system of the invention can recognize and identify liquids in containers such that they can be eliminated as threats. This provides a method of discriminating detected objects beyond the mass and density discrimination approaches of the invention described above.
  • the invention determines whether an object is a contained liquid by first creating a bounding box which surrounds the object. The numbers of voxels close to each of the surfaces of the bounding box are computed. The top surface of the liquid is then identified by identifying a horizontal surface of the bounding box. The ratio of voxels close to the top surface to the total number of surface voxels can then be calculated. If the fraction of top surface voxels exceeds a predetermined threshold ratio, and if the density of voxels above the top surface indicates that air is located above the top surface, then it is concluded that the object is a contained liquid. In one embodiment, it can then be concluded that the object does not pose a threat.
  • the invention applies a statistical approach to determining whether an object in the bounding box is a contained liquid.
  • a histogram of the top- surface voxels and a histogram of the bottom-surface voxels are computed.
  • the peak in the top-surface histogram indicates the vertical position of the top-surface voxels
  • the peak in the bottom-surface histogram indicates the vertical position of the bottom-surface voxels.
  • the ratio of the number of top-surface voxels to the top-surface area in the bounding box exceeds a threshold and the ratio of the number of top-surface voxels to bottom-surface voxels exceeds another threshold, then it can be concluded that the object is a contained liquid.
  • detection is carried out in multiple paths or stages such that the overall detection process is more efficient.
  • Each item that can be identified by the method of the invention is, in general, associated with
  • this multiple-path method of the invention where one specific detection path has been applied to a set of data and has classified a portion of the data, the classified portion of data are removed from further processing. This eliminates inefficiencies introduced by unnecessary re-analysis of data that has already been classified.
  • the present invention also allows for optimization of overall system detection rate (probability of detection) and false alarm rate.
  • Each item that can be detected by the system of the invention is associated with an individual detection rate and false alarm rate.
  • sheet explosive detection has a unique probability of detection and false alarm rate.
  • each individual explosive material type has its own unique probability of detection and false alarm rate.
  • the overall system probability of detection is an accumulation of each individual detection rate; in one embodiment, it is the average of the individual detection rates.
  • the overall false alarm rate of the system is an accumulation of all of the individual false alarm rates; in one embodiment, it is the sum of the individual false alarm rates.
  • the overall detection rate can be optimized by adjusting one or more or the individual detection rates.
  • the overall false alarm rate can be optimized by adjusting one or more of the individual false alarm rates.
  • overall system performance can be adjusted as required to attain desired overall detection rate and/or false alarm rate by making adjustments to individual detection rates and/or false alarm rates.
  • one or more individual detection rates can be lower than a
  • the system can provide the flexibility of adjusting one or more individual detection rates to a lower level while maintaining the overall rate within specified limits. Reducing one detection rate can also reduce the associated false alarm rate. Thus, the overall system false alarm rate can be reduced while maintaining the overall system detection rate within the specified limits. Also, the overall detection rate can be maintained at a particular value while individual and/or overall system false alarm rates can be adjusted to desired levels.
  • the system of the invention can provide a complete CT scan of a bag such that complete three-dimensional image data for the bag can be analyzed. This results in the system's ability to detect objects such as thin sheets in the bag regardless of orientation and size.
  • the InVision Machine only regions identified as suspect by the 2D pre-screen are subjected to 3D scanning.
  • voxels are not connected and identified as objects until voxels belonging to thin sheet objects are first identified. This eliminates the problems of identifying sheets found in systems such as the Eberhard et al. system.
  • FIG. 1 contains a perspective view of a baggage scanning system in accordance with the present invention.
  • FIG. 2 contains a cross-sectional end view of the system shown in FIG. 1.
  • FIG. 3 contains a cross-sectional radial view of the system shown in FIG.
  • FIG. 4 contains a schematic electrical and mechanical block diagram of one embodiment of the baggage scanner of the invention.
  • FIG. 5 contains a top-level flow diagram which illustrates the logical flow of one embodiment of the object identification method of the present invention.
  • FIG. 6 contains a flow diagram of the logical flow of one embodiment of the region of interest calculation of the present invention.
  • FIG. 7 contains a flow diagram of the logical flow of one embodiment of a sheet detection method in accordance with the present invention.
  • FIGS. 8A and 8B schematically illustrate the sheet object detection method of
  • FIG. 9 contains a flow diagram of the logical flow of one embodiment of a bulk object detection method in accordance with the present invention.
  • FIG. 10 contains pseudocode which describes one embodiment of a modified connected component labeling method in accordance with the present invention.
  • FIG. 11 is a schematic illustration of the partial volume effect.
  • FIG. 12 is a schematic plot of mass threshold versus density illustrating three different density dependent mass thresholds in accordance with the present invention.
  • the present invention provides an apparatus and method which detect, identify and/or classify objects in CT data for a region.
  • the region can include the interior of a piece of baggage or luggage being carried or checked onto a commercial aircraft.
  • the invention can therefore be implemented in a CT baggage scanning system.
  • the objects identified by the invention can be objects known to pose threats to persons at an airport or on board an aircraft. These objects can include explosive objects and materials. It should be noted that the explosive objects and materials that can be detected by the invention can be of various shapes and materials. The explosives
  • -21- can be commercial, military or improvised, i.e. , home made.
  • explosive objects can be in various shapes including, but not limited to, sheets, single cylindrical containers or other such shapes, multiple cylinders or other stick shapes, and other bulk shapes.
  • Various types of explosive materials formed or contained in these shapes can be detected in accordance with the invention.
  • thresholds such as density thresholds, mass thresholds, density-dependent mass thresholds, and difference thresholds as well as process parameters are used to carry out the various methods of the invention.
  • These thresholds and parameters are determined based on extensive analysis of the CT data, such as actual three-dimensional CT density data, for many actual threat and non-threat objects.
  • This analysis included statistical analysis of the data employing statistical methods such as simulated annealing and genetic algorithms. In accordance with the invention, this analysis allows for threshold and/or parameter selection based on a particular objective to be met, e.g. , false alarm and/or detection rate setting/optimization, discrimination of explosive type, etc. , as described below.
  • FIGS. 1, 2 and 3 contain perspective, end cross-sectional and radial cross- sectional views, respectively, of a baggage scanning system 100 constructed in accordance with the invention, which provides object detection, identification and classification in accordance with the invention.
  • the baggage scanning system 100 generates CT data for a region which can include a piece of baggage.
  • the system can use the CT data to generate image volume elements or "voxels" for the region.
  • the baggage scanning system can be of the type described in copending U.S. patent application serial numbers 08/831,558, 08/948,930, 08/948,937, 08/948,928, 08/948,491,
  • the system 100 includes a conveyor system 110 for continuously conveying baggage or luggage 112 in a direction indicated by arrow 114 through a central aperture of a CT scanning system 120.
  • the conveyor system includes motor driven belts for supporting the baggage. Conveyor system 110 is illustrated
  • the CT scanning system 120 includes an annular shaped rotating platform or disk 124 disposed within a gantry support 125 for rotation about a rotation axis 127 (shown in FIG. 3) that is preferably parallel to the direction of travel 114 of the baggage 112.
  • Disk 124 is driven about rotation axis 127 by any suitable drive mechanism, such as a belt 116 and motor drive system 118, or other suitable drive mechanism, such as the one described in U.S. Patent No. 5,473,657 issued December 5, 1995 to Gilbert McKenna, entitled "X-ray Tomographic Scanning System," (Attorney Docket No. ANA- 30CON) which is assigned to the assignee of the present application and which is incorporated herein in its entirety by reference.
  • Rotating platform 124 defines a central aperture 126 through which conveyor system 110 transports the baggage 112.
  • the system 120 includes an X-ray tube 128 and a detector array 130 which are disposed on diametrically opposite sides of the platform 124.
  • the detector array 130 can be a two-dimensional array such as the array described in a copending U.S. Patent Application serial no. 08/948,450 entitled, "Area Detector Array for Computed Tomography Scanning System," (Attorney Docket No. ANA- 137) filed on October 10, 1997.
  • the system 120 further includes a data acquisition system (DAS) 134 for receiving and processing CT data signals generated by detector array 130, and an X-ray tube control system 136 for supplying power to, and otherwise controlling the operation of, X-ray tube 128.
  • DAS data acquisition system
  • the system 120 is also preferably provided with a computer processing system for processing the output of the data acquisition system 134 and for generating the necessary signals for operating and controlling the system 120.
  • the computer system can also include a monitor for displaying information including generated images.
  • the X-ray tube control system 136 can be a dual-energy X-ray tube control system such as the dual-energy X-ray tube control system described in the copending U.S. Patent Application Serial No. 08/671,202 entitled, "Improved Dual Energy Power Supply, " (Attorney Docket No. ANA-094), which is assigned to the same assignee as the present application and which is incorporated herein in its entirety by
  • Dual energy X-ray techniques for energy-selective reconstruction of X- ray CT images are particularly useful in indicating a material's atomic number in addition to indicating the material's density, although it is not intended that the present invention be limited to this type of control system. It is noted that the detailed description herein of the object identification and classification system and method of the invention describes the details in connection with single-energy data. It will be understood that the description is applicable to multiple-energy techniques.
  • System 120 also includes shields 138, which may be fabricated from lead, for example, for preventing radiation from propagating beyond gantry 125.
  • the X-ray tube 128 generates a pyramidically shaped beam, often referred to as a "cone beam," 132 of X-rays that pass through a three- dimensional imaging field, through which baggage 112 is transported by conveying system 110. After passing through the baggage disposed in the imaging field, cone beam 132 is received by detector array 130 which in turn generates signals representative of the densities of exposed portions of baggage 112. The beam therefore defines a scanning volume of space.
  • Platform 124 rotates about its rotation axis 127, thereby transporting X-ray source 128 and detector array 130 in circular trajectories about baggage 112 as the baggage is continuously transported through central aperture 126 by conveyor system 110 so as to generate a plurality of projections at a corresponding plurality of projection angles.
  • signals from the detector array 130 can be initially acquired by data acquisition system 134, and subsequently processed by a computerized processing system using CT scanning signal processing techniques.
  • the processed data can be displayed on a monitor, and/or can also be further analyzed by the processing system as described in detail below to determine the presence of a suspected material.
  • the CT data can be analyzed to determine whether the data suggest the presence of material having the density (and when a dual energy system is used, molecular weight) of explosives. If such data are present, suitable means can be provided for indicating the detection of such material to the operator or monitor of the system, for example, by providing an indication on the screen of the monitor by sounding an audible or visual alarm,
  • detector array 130 can be a two-dimensional array of detectors capable of providing scan data in both the directions of the X- and Y- axes, as well as in the Z-axis direction.
  • the plurality of detector rows of the array 130 generate data from a corresponding plurality of projections and thereby simultaneously scan a volumetric region of baggage 112.
  • the dimension and number of the detector rows are preferably selected as a function of the desired resolution and throughput of the scanner, which in turn are a function of the rotation rate of rotating platform 124 and the speed of conveying system 110.
  • conveying system 110 advances the baggage 112 just enough so that the volumetric region scanned by detector array 130 during one revolution of the platform is contiguous and non-overlapping with (or partially overlapping with) the volumetric region scanned by detector array 130 during the next revolution of the platform.
  • Conveying system 110 continuously transports a baggage item 112 through CT scanning system 120, preferably at constant speed, while platform 124 continuously rotates at a constant rotational rate around the baggage items as they pass through.
  • system 120 performs a helical volumetric CT scan of the entire baggage item.
  • Baggage scanning assembly 100 preferably uses at least some of the data provided by the array 130 and a helical reconstruction algorithm to generate a volumetric CT representation of the entire baggage item as it passes through the system.
  • the system 100 performs a nutating slice reconstruction (NSR) on the data as described in copending U.S. Patent Application Serial No.
  • NSR nutating slice reconstruction
  • FIG. 4 contains a mechanical/electrical block diagram of one embodiment of the baggage scanning system 100 of the invention.
  • the mechanical gantry of the scanner 100 includes two major components, the disk 124 and the frame (not shown).
  • the disk 124 is the rotational element which carries the X-ray assembly, the detector assembly 130, the data acquisition system (DAS) 134, a high-voltage power supply and portions of the monitor/control assembly, the power supply assembly and the data link assembly.
  • the frame supports the entire system 100, including the baggage handling conveyor system 110.
  • the disk 124 is mechanically connected to the frame via a duplex angular contact ball bearing cartridge.
  • the disk 124 can be rotated at a constant rate by a belt which can be driven by a DC servomotor 505.
  • the gantry also contains X-ray shielding on the disk and frame assemblies.
  • the baggage conveyor system 110 includes a single belt driven at a constant rate to meet specified throughput requirements.
  • the belt can be driven by a high-torque, low-speed assembly to provide a constant speed under changing load conditions.
  • a low-attenuation carbon graphite epoxy material can be used for the portion of the conveyor bed in the X-ray.
  • the total length of the conveyor is designed to accommodate three average length bags.
  • a tunnel is used around the conveyor to meet the appropriate safety requirements of a cabinet X-ray system.
  • the low-voltage power supply 501 on the disk 124 provides power for the DAS 134, the X-ray cooling system and the various monitor/control computers and electronics.
  • a low-voltage power supply 501 on the disk 124 provides power for the DAS 134, the X-ray cooling system and the various monitor/control computers and electronics.
  • the -26- voltage power supply on the frame provides power for the reconstruction computer and the various monitor/control electronics.
  • the conveyor motor 503, the gantry motor 505, the high-voltage power supply and the X-ray coolant pump can all be supplied power directly from the main supply.
  • the high- voltage power supply provides power to the X-ray tube 128.
  • the supply can provide a dual voltage across the cathode/anode.
  • the driving waveform can be any desirable shape, and preferably is in the form of a sine wave. This supply can also provide X-ray filament power.
  • the supply current can be held approximately constant for both voltages.
  • the dual-energy X-rays strike the baggage, and some portion of the X-rays pass through and strike the detector assembly 130.
  • the detector assembly 130 performs an analog conversion from X-ray to visible photons and then to electrical current.
  • the DAS 134 can sample the detector currents, multiplex the amplified voltages to a set of 16-bit analog-to-digital converters and multiplex the digital outputs to the computerized processing system 515, which generates CT data and processes the data in accordance with the invention as described below to detect, identify and classify objects in the piece of baggage 112.
  • the digital data from the DAS 134 are transferred to the processing system 515 via a non-contact serial data link 511.
  • the DAS 134 can be triggered by the angular position of the disk 124.
  • the non-contact links 511 and 513 can transfer the high-speed digital DAS data to the processing system 515 and the low-speed monitor/control signals back and forth between the disk and frame control computers.
  • the data link 511 can be based upon an RF transmitter and receiver.
  • the image reconstructor portion of the processing system 515 converts the digital line integrals from the DAS 134 into a set of two- dimensional images of bag slices for both the high and low energies.
  • the CT reconstruction can be performed via a helical-cone-beam solution, such as the nutating slice reconstruction method described in copending U.S. Patent Application serial no. 08/831,558, incorporated by reference above.
  • the reconstructor can include embedded software, a high-speed DAS port, an array
  • the array processor can perform data corrections and interpolation.
  • the reconstructor can be self- hosted and can tag images based upon the baggage information received over the UART interface to the frame computer.
  • the processing system 515 can include a PC -based embedded control system. All subsystems can be monitored for key health and status information. This system can also control both motion systems, can sense baggage information, can control the environment, e.g., temperature, humidity, etc., can sense angular position of the disk 124 and can trigger the DAS and HVPS. This system can also have a video and keyboard interface for engineering diagnostics and control. Additionally, a control panel can be included for field service.
  • categories can include sheets, sticks, bulks and other categories based on shapes.
  • the invention can include multiple separate detection paths, which can include a separate path for each type.
  • the method can include a sheet explosive path and a path for the rest of the explosive objects which are referred to as "bulks" throughout this application.
  • the process begins by first performing a partial discrimination on the data to identify sheet-shaped objects.
  • a connection step such as some form of CCL is performed to connect objects.
  • further discrimination is performed to classify identified objects according to potential threats. This is in contrast to prior systems such as the Eberhard et al. system which perform connection first and then discrimination, resulting in the loss of thin sheet-shaped objects.
  • sheet explosive detection is based on a process known as a constant false alarm rate method (CFAR) and modified in accordance with the invention, which statistically decides whether a volume element or voxel belongs to a sheet explosive.
  • CFAR constant false alarm rate method
  • Sheet voxels can also be identified by a morphological sheet detection approach in accordance with the invention described below in detail.
  • the voxels identified as sheet voxels by CFAR or the morphological sheet detection of the invention are then connected and labeled using a standard connected component labeling (CCL) process.
  • CCL connected component labeling
  • the voxels are connected and labeled using the morphological CCL of the invention described herein.
  • the labeled objects can then be discriminated by their mass. If the mass of an object is greater than a predetermined threshold, the object is declared a sheet explosive.
  • CCL connected component labeling
  • bulk explosive detection uses a modified connected component labeling (CCL) process that can include morphological operations (erosion and dilation) to prevent objects growing together.
  • CCL connected component labeling
  • morphological operations or dilation
  • Bulk detection can also involve controlled object merging for closely spaced threat objects, e.g. , individual stick-shaped objects which should be considered as a single object.
  • Discrimination is based on the density and mass of a detected object.
  • the mass thresholds for discrimination are density dependent. Lower density objects can be assigned higher mass thresholds for several reasons. For example, data indicate that the amount of low-density explosive required to cause a particular amount of damage is greater than the amount of high-density explosive. Therefore, at lower densities, a higher amount, i.e., higher mass, of material is required to trigger an alarm condition. Also, at low densities, a higher rate of false alarms may result. Accordingly, a higher mass threshold can reduce the number of false alarms at low densities.
  • FIG. 5 contains a top-level flow diagram which illustrates the logical flow
  • a first step 301 reconstructed CT image data are received and analyzed to define a region of interest (ROI) or bounding box for the region.
  • ROI region of interest
  • This process eliminates voxels outside a bag and therefore reduces the size of the data set considerably.
  • the method can then proceed along parallel paths including a sheet object detection path and a bulk object detection path.
  • sheet-shaped objects are detected in the sheet detection step 302.
  • detected objects are analyzed to determine if they are threats. In one embodiment, this is done by comparing the mass of an object to a mass threshold.
  • the discrimination step 306 produces label image data for the bag, which mark the voxels belonging to each sheet object and identify physical properties of each sheet object (preferably density and mass) and their position in the bag.
  • the label image data for each voxel also include a number identifying the voxel according to an object with which it is identified or identifying the voxel as being background.
  • the discrimination step 308 produces label image data for the bag, which marks the voxels belonging to each bulk object and identifies physical properties of each bulk object (preferably density and mass) and their position in the bag.
  • the decision - data fusion step 310 of the method takes the label image data produced by sheet and bulk detection steps and computes a single label image that corresponds to detected explosives. It will be understood that the method described in connection with FIG. 5 can include more than two separate detection paths, depending on the number of types of objects to be identified.
  • the term "3-D image" and the symbol C(i,j,k) are used to represent a set of CT slice images.
  • the size of each CT slice is / columns by / rows.
  • the symbol / in C(i,j,k) represents the column index and runs from 0 to / - 1.
  • the symbol j represents the row index and runs from 0 to / - 1.
  • the symbol k represents one of these
  • the function C (i,j,k) is used to refer to or represent a particular CT density in this set, meaning that it is the CT density value at the z ' th column and they ' th row of the kt slice.
  • the CT densities are represented by positive integers with 0 (Hounsfield units) corresponding to the density of air and 1000 (Hounsfield units) corresponding to the density of water, although if desired other integer values can be used.
  • the function C (i,j,k) can be considered a 3-D image being / pixels in width, J pixels in height, and K pixels in depth. Each element in the 3-D image is a voxel.
  • the value C (i,j,k) for a particular voxel denoted by the (i,j,k) triplet is the CT density of the material occupying that voxel.
  • the size of a voxel is determined by the resolution of the CT equipment.
  • the scanner has a nominal voxel size of 3.5mm in width (x), 3.5mm in height (y), and 3.33 mm in depth (z), which is a relatively small voxel and therefore produces higher resolution when compared to the Eberhard et al. system, although the nominal size can vary depending on several design factors.
  • CT densities approximately correspond to physical densities of material. Since the CT density of 1000 is made to correspond to the density of water (i.e., 1 gram/cc), in order to find the mass of a given voxel in grams, the CT density value of that voxel is divided by 1000 and multiplied by the volume of the voxel (0.35 x ⁇ .35 x ⁇ .333 cc). The method described in this application utilizes this conversion (as the constant c 0 ) to compute the bag mass and the mass of each identified object in the bag. The main steps in the method of the invention listed above and shown in
  • FIG. 6 contains a flow diagram of the logical flow of one embodiment of the region-of-interest calculation 301 of the present invention.
  • the goal of the region of interest calculation is to eliminate parts of the image that lie outside the bag so that other parts of the process will have less data to analyze and therefore speed up the process and decrease the memory requirements.
  • a rectangular subset that contains all of
  • the inputs to the region-of-interest calculation include C (i,j,k), which is the three-dimensional CT-image for a bag.
  • the outputs include C roi (i,j,k), which represents the CT image of a bag region of interest and (x min , x max , y min , y max , z min ,
  • Step 301 begins by receiving the data representing the 3-D image of a bag, C (ij,k) and the value for the air threshold t 0 .
  • step 312 the voxels identified as containing data representing air are identified, and, in step 314, the coordinates for the region of interest are computed so as to exclude most if not all of those voxels.
  • Steps 312 and 314 proceed as follows so as to define the region of interest:
  • FIG. 7 is a flow diagram which illustrates the logical flow of one embodiment of a sheet detection method in accordance with the present invention.
  • Sheet explosives are characterized as being much thinner in one dimension (height, width, or depth) than in the other two. This dimension is referred to as the thickness of a sheet explosive.
  • One sheet explosive detection method described herein is tunable to the sheet thickness and uses a constant false alarm rate (CFAR) method.
  • CFAR constant false alarm rate
  • CFAR sheet voxel analysis step 318 is performed on the CT image data for the region of interest to identify which voxels are associated with sheet objects.
  • a connected components labeling (CCL) method can be applied in step 320 to sheet voxels to connect them within individual objects.
  • CCL connected components labeling
  • each voxel in the bag is examined to determine whether it is part of a sheet explosive.
  • a voxel should have a density value within a certain range of CT density values and should be statistically distant from its background.
  • the background is defined as the voxels on the surface of a cube of size comparable to the sheet thickness that is centered around the test voxel as shown in FIGS. 8 A and 8B, which are schematic diagrams of the preferred CFAR method of the present invention.
  • FIG. 8 A shows in two dimensions the background cube 321 including a test voxel 319 being applied to CT data voxels that include a sheet object 317.
  • the mean and standard deviation of the background voxels around the test voxel are computed.
  • the value of the test voxel is compared against the mean and standard deviation of the background. If the statistical distance of the test voxel to its background is larger than a predetermined threshold, then the test voxel is said to belong to a sheet explosive.
  • all of the voxels on the surfaces of the cube are not used to compute the mean and standard deviation.
  • the voxels at a surface can be sampled, and only the sampled voxels can be used in the computation of the mean and standard deviation. In one embodiment, only every other voxel is sampled, resulting in savings of half the processing time required to generate the mean and standard deviation.
  • three separate two-dimensional CFAR calculations can be performed in the three orthogonal Cartesian planes, x-y, x-z, y-z.
  • a voxel mean and standard deviation of the background are computed for each plane, the background being defined as the voxels on the perimeters of a square in the respective plane.
  • statistical distances are computed for each plane and are compared to a predetermined threshold. Different coordinate planes may have different thresholds.
  • the number of planes in which the threshold is exceeded is used to determine whether the voxel is a sheet voxel. For example, if one or more thresholds are exceeded, then it can be concluded that the voxel is a sheet voxel.
  • the voxel is labeled a sheet voxel if two or more thresholds are exceeded.
  • an upper threshold in addition to or instead of the lower threshold can be employed. This will eliminate sheet-shaped objects which have very high contrast with background. An example of such a sheet would be the outer surface of the bag.
  • the background voxels cover more of the object itself.
  • the background becomes statistically close to the test voxel which is chosen to be in the test object. Therefore, the CFAR distance is large for thin sheet-like objects and small for thick bulk-like objects. This property is used to detect all voxels that belong to sheet-like objects and eliminate all voxels that belong to bulk objects in the bag.
  • a three-dimensional CFAR method is applied to the CT data to detect sheet objects.
  • selecting the size of the void region between the target voxel and the background voxels was considered.
  • CFAR process such as the processes described in the literature mentioned above, requires that the background samples be taken from an area that does not include any part of the target.
  • the background samples In the application of the present invention in which sheet objects are detected, in the case of a sheet, only one dimension of the target object is known and the orientation of that dimension is not known. So, if the prior art CFAR approach were applied, it would be difficult to sample the background as the prior art CFAR process requires.
  • -34- object are sampled as background also.
  • including some of the target samples in the background samples changes the mean and the standard deviation of the background samples. But the change is different for different target coverage of the CFAR sampling region. This fact helps the invention distinguish between sheet-like objects and bulk objects. This difference between sheet coverage and bulk coverage is illustrated in Figs. 8A and 8B.
  • the CCL analysis 320 is performed on the sheet voxels to combine the voxels into a sheet object.
  • the mass of each connected component thus obtained is compared against a predetermined mass threshold to decide the presence of a sheet explosive.
  • the present invention uses a CFAR approach that is extended to three dimensions and is substantially modified and improved over the two-dimensional techniques described in the literature.
  • the modified CFAR is used as one step in a process to identify a threat.
  • the modified CFAR of the invention first classifies individual voxels according to whether they are part of sheet objects.
  • the process of the invention continues with additional steps such as CCL to combine the voxels into objects and discrimination steps to determine whether the objects pose threats.
  • the prior two-dimensional CFAR approaches were used as stand-alone detection algorithms whose outputs consisted of a final classification of an object based on two-dimensional CFAR analysis of a pixel in the object.
  • the goal of the sheet explosive detection method is to detect sheet-like objects.
  • a separate sheet explosive detection step is used to solve the problem of sheets being inadvertently removed from the data during morphology steps such as erosion performed during the bulk detection process.
  • the inputs to the sheet detection method include C rm (i,j,k), which is the 3-D image of the region of interest (size I rm x J ro ,x K ro .
  • the outputs of sheet explosive detection include the following:
  • L s (i,j,k), Label image for sheet explosives (same size as C ro ⁇ ); N, Number of detected sheet explosives; p concentrate, Density of each detected object; Mford, Mass of each detected object; and
  • Bounding box for each detected object is defined as the smallest rectangular region which contains the object that it bounds.
  • the parameters for sheet detection include the following: (P * mm > P" max ) > CT-density range of interest for sheets; g, Size of the CFAR cube around the test pixel in voxels; t habit CFAR decision threshold;
  • the steps in the sheet explosive detection method include the following:
  • the thickness of sheets to be detected can be set to be slightly thicker than the thinnest sheet that can be detected by bulk detection.
  • sheet objects can be detected in the density data by using a morphology approach analogous to the morphological CCL applied in bulk object detection.
  • a morphological sheet detection approach analogous to the morphological CCL applied in bulk object detection.
  • all objects in the data are eroded a predetermined number of times such that all thin sheet shaped objects are eliminated from the data.
  • the number of erosions performed is based on the number of erosions needed to eliminate sheet objects from the data, which is related to the thickness of a sheet. Each erosion can remove one layer of surface voxels. Therefore, the number of erosions is related to the expected thickness of a sheet and the size of a voxel.
  • the objects remaining in the data are assumed to be bulk objects. The data associated with these objects are then eliminated from further processing.
  • the original data, with the bulk objects removed, are then analyzed to label the sheet objects.
  • the remaining voxels are analyzed one at a time such as by the CCL process to combine voxels into sheet objects and then label the sheet objects.
  • discrimination is performed on the sheet objects to classify them as threats or non- threats, such as by comparing the objects mass to a predetermined mass threshold. Sheets with masses above the threshold can be classified as threats.
  • connection process e.g., CCL
  • CCL connection process
  • the bulk object detection process of the invention searches the bag image for clusters of voxels in the density range of interest, can label them as bulk objects, and can use mass-dependent density thresholds to determine if an object is a threat.
  • the bulk detection process uses the CCL method to identify objects in the three-dimensional bag image.
  • the image is preprocessed before the application of CCL to split compound objects.
  • This preprocessing can be done using an erosion operator as described below in detail, which effectively removes the surface layer of voxels from objects to prevent CCL from growing multiple objects together.
  • a dilation operation as described below in detail is applied after an eroded image is segmented into objects by CCL. This operation adds the surface voxels back to the objects after the objects have been determined to be separate objects.
  • the bulk detection method uses one or more separate density ranges, one for each type of bulk material of interest having density values falling within the range.
  • the density ranges are chosen according to the objects sought to be identified.
  • one of the ranges covers a first specific type of explosive (referred to herein as "type A"); the other one includes all solid bulk explosives exclusive of type A. Since explosive types and typical false alarms in these two density ranges differ, separate detection paths with different erosions and dilations can be used to optimize performance for each density range.
  • the detection process for a given density range has the following steps.
  • -40- Density dependent mass thresholds are used to discriminate between threat and non- threat objects.
  • the number of potential explosive objects, their properties and their coordinates in the bag image are returned by the bulk detection process.
  • the inputs to the bulk detection process include C ⁇ i,j,k), the 3-D CT image of a bag.
  • the outputs include the following:
  • N b Number of detected bulk objects
  • V n Eroded number of voxels in each object
  • Parameters used by the process include: iff mm , ff m Density range for type A explosive;
  • FIG. 9 is a schematic flow diagram which illustrates the logical flow of one embodiment of the bulk object detection method of the invention.
  • the bag image voxels are received for preprocessing steps 330 and 332.
  • the preprocessing steps including object erosion are performed separately in parallel.
  • preprocessing for type A materials is performed in step 330 and preprocessing for bulks is performed in step 332. It will be understood that these preprocessing steps need not be performed in separate parallel steps.
  • the erosion operator is applied recursively image-by-image to the original CT image e p times for the type A erosion density range (/f e m ⁇ réelle 3 ff e max ) in step 330, and/or e b times for the bulk erosion density range ⁇ /7 e mm /7 e ma 7) in step 332.
  • Standard morphological erosion keeps a voxel only if the voxel neighborhood fits a certain pattern mask. Typically, only voxels with all 27 neighbors in the range of interest are kept. Standard erosion is described in, for example, Serra, J., Image Analysis and Mathematical Morphology, Academic Press. London. 1982. It should be noted that the erosion operator employed in the morphological CCL of the invention can be the voting or counting operator described above. Other erosion operators, such as the one described by Serra, can also be used.
  • the purpose of the erosion operation is to separate objects that are in close proximity to each other and, as a result, can be merged together by standard CCL.
  • the outside surfaces of objects are eroded.
  • some objects have internal holes (such as thin cylindrical axial cavities in cylindrical stick-like objects) or voxels that fall outside the density range of interest due to noise or artifacts in the image. Eroding these internal surfaces can split the objects into several parts, or it can eliminate the object completely in the case of thin objects with axial cavities.
  • the outer surfaces of objects are usually convex, while the surfaces of internal cavities are usually concave. Therefore, voxels on outside surfaces are likely to have fewer object voxels in the 3 3 3 neighborhood than voxels on the inside surfaces.
  • the count threshold can be used to selectively erode only the outer surfaces of objects while preserving the object interior.
  • each preprocessing step 330 and 332 generates a unique set of preprocessed data from the original bag image data.
  • the combine step 334 these individual sets of data are combined into a single set of preprocessed data.
  • CCL can be used to identify and label objects in the eroded CT image data. Neighboring voxels in the same density range, either bulk iff mm, ft max) or fyP e A iff mm, ff max), ⁇ Q connected and assigned an object label.
  • Neighboring voxels are defined as a combination of "face”, “edge”, or “vertex” neighbors, determined by the CCL connectivity parameter c b .
  • a single run of the CCL process can find objects in both density ranges, as long as the gap between them is greater than the maximum density difference for connecting voxels in CCL, f7 mm - ff max > ⁇ ⁇ .
  • Using only a single CCL run to compare voxels to multiple density ranges is much more efficient and less time consuming than performing a separate CCL run for each range, since CCL in general utilizes a large amount of processing resources.
  • the threshold difference ⁇ A is selected to be smaller that the gap between the density ranges, then the chance of mislabeling a voxel of a material of one of the ranges as belonging to another range is eliminated, thus allowing the process to be run for multiple ranges simultaneously. It will be understood that this approach can be extended to any number of ranges separated by gaps at least as large as the threshold ⁇ ⁇ .
  • the label image is created, where each voxel (i,j,k) is assigned a value
  • voxel belongs to nth object
  • N b is the total number of objects found in a bag image.
  • Eroded number of voxels, V s whisk , and eroded mass, M réelle are computed for each object n at the relabeling pass of the CCL.
  • L b (i l , j', k J ) l, (16) and the total number of voxels, V, , and the total mass, M, are incremented. Dilation can be performed recursively e p times for the type A density range, and e b times for the bulk density range.
  • a second combine step 342 can then be performed on the dilated data.
  • each dilation step 338 and 340 generates a unique set of dilated data from the eroded,
  • a partial volume correction step 344 can then be performed.
  • the mass of each object is enhanced by replacing the measured CT density of voxels added by dilation with the average density of the object.
  • Partial volume correction 344 is described below in detail.
  • An object merging step 346 can then be performed. If bounding boxes of objects n and m overlap and their eroded densities are close, i.e.,
  • Discrimination 308 can then be performed. For each object n, l ⁇ n ⁇ N b ,the decision is made whether this object is a potential threat based on the object mass and eroded density:
  • object is a threat if ⁇ M n ⁇ m l for p m ⁇ carbon ⁇ L> n ⁇ p t ⁇
  • CCL connected component labeling
  • the 3D image is represented by the C (i,j,k) array.
  • Index 0 ⁇ k ⁇ K is the slice number (index along the Z axis), where the value of K is determined by the length of the bag.
  • Voxels (i,j,k) and (/particularly, j n , k n ) share a common face, 1 1 -i n ⁇ + ⁇ j -j n ⁇ + ⁇ k - k till ⁇ 1.
  • Voxels (i,j,k) and (although, j n , k share a common edge, 1 1- i n ⁇ + ⁇ j -j n ⁇ + ⁇ k - k n ⁇ 2.
  • Voxels (i,j,k) and (idonating,j n , k n ) share a common vertex 1 1- i hinge ⁇ + ⁇ j-j practice ⁇ +
  • k - k n ⁇ 3.
  • a combination of these connectivity types can be specified as an input parameter for the CCL process. It should be noted that when the current voxel (i,j,k) is on the surface of the bag image (either I,j, or k equal to 0 or their respective maximum values), not all possible neighboring voxels exist.
  • FIG. 10 contains pseudocode describing one embodiment of the CCL method of the invention.
  • the first step of the CCL method is a raster scan through the image data. It assigns preliminary labels to the voxels. If the current voxel is within the density range of interest, then it is tested against its neighboring voxels. If the
  • the second step of the method is to resolve all label equivalencies in the 1(1) array, count the number of separate objects N b and assign each label an equivalent value ranging from 1 to N b .
  • Partial volume correction or mass enhancement of an object addresses the problem of density degradation of the surface voxels of the scanned objects.
  • the CT value of a given voxel represents the average density of the object within that voxel. As a result, an accurate measure of the object density can only be obtained if the voxel is totally contained in the object. On the other hand, if the voxel is partially occupied by the object, its density will be degraded based on the occupied portion of the voxel.
  • FIG. 11 shows the partial volume effect on a scanned object 351. In FIG. 11 non-shaded voxels (squares) are affected by the partial volume effect.
  • the partial volume correction of the invention is based on substituting the CT values of the surface voxels of the scanned object using its mean CT density or the mean of its eroded CT density.
  • the actual density and mass of a scanned object are larger than their measured values.
  • the difference in values depends on the shape of the object; more specifically, it depends on the surface-to-volume ratio of the scanned object.
  • the measured CT density of a scanned object is defined as the averaged density of all its voxels:
  • the object voxels can be divided into surface and core voxels.
  • Surface voxels are voxels having less than 26 neighbors of the scanned object voxels.
  • a more accurate density measure can be computed using only the core voxels of the scanned object.
  • the new density is known as the eroded density:
  • the corrected mass can be computed using one of the following equations:
  • the eroded or the averaged density can be chosen to replace the CT values of the surface voxels. However, the best substitution can be determined based on a controlled experiment. Using both densities, the corrected masses of scanned objects can be compared to their actual masses, and the density that produces the least error can be chosen. An iterative algorithm can also be used to compute a combination of eroded and average densities that produces the mass correction least dependent on object shape or size.
  • the merging process 346 mentioned above in connection with FIG. 9 will now be described in detail. To avoid combining different objects in a scanned bag, an erosion stage can precede the CCL process. As a result, physically separate objects can be assigned different labels.
  • certain explosive devices such as certain stick-shaped explosive objects
  • the use of erosion can cause each object to be considered separately, thus failing the mass threshold criterion and reducing the overall detection rate.
  • Each of these objects by itself satisfies the density criterion, however, its mass is below the threshold value.
  • a merging process based on the eroded densities of segmented objects can be implemented to recover the objects.
  • the data show that these objects have almost identical eroded densities as found by the CCL process.
  • their bounding boxes lay in the same XYZ region. In one embodiment, this information leads to the conclusion that these objects must have been components of the same single object.
  • the merging approach deals only with objects within a certain density range that fail the mass threshold criterion. As a result, for every such object the following steps can be performed:
  • the first step in the explosive detection process of the invention is to detect objects within a given density range.
  • This density range covers most of the objects that can or may be found in luggage.
  • the second step is to eliminate most of these objects based on additional information such as mass. For instance, it may be the case that an explosive will be considered a threat only if its mass exceeds a certain mass threshold.
  • the mass threshold can be used to have the effect of reducing the false alarm rate.
  • a higher mass threshold yields a lower false alarm and lower detection rate.
  • a lower mass threshold increases both the detection and the false alarm rates.
  • objects of different densities are subjected to different threshold masses. The mass threshold can then be described by
  • -51- mass threshold based on a density range can be used to adjust the false alarm rate within that particular density range, which results in an adjustment of the overall system false alarm rate.
  • a density range may be associated with a relatively high false alarm rate.
  • a relatively high mass threshold can be selected for that density range to reduce the false alarm rate for that range. As a result, the overall system false alarm rate is reduced.
  • density dependent threshold masses can be used.
  • the density space can be divided into more than two nonoverlapping regions.
  • the step function can be replaced with a gradual change in mass between two density regions:
  • FIG. 12 is a schematic plot of mass threshold versus density illustrating three different density dependent mass thresholds.
  • the decision made by the two detection methods for sheets and bulks is reconciled in order to reach a unanimous decision.
  • the two label images produced by two parts of the process are fused to obtain a single label file.
  • the inputs to the decision-data fusion method include:
  • N Total number of detected sheet explosives
  • L s (i,j,k) Sheet explosive label image
  • N b Total number of detected bulk explosives
  • L b (i,j,k) Bulk explosive label image
  • the outputs include:
  • N Total number of detected explosives
  • the decision-data fusion process includes the following steps.
  • a voxel has two conflicting labels, one of them designating it as part of a sheet object and the other labeling it as a bulk object, then an arbitrary decision is made to use the bulk object label for the voxels. Also, if output specifications for the detection method change, the data fusion part of the method can be switched off, resulting in two separate output label images for sheet and bulk objects.
  • Running bulk detection first and subtracting the detected objects from the image can be performed to reduce processing time. Bulk detection takes less time
  • Sheet explosive detection then works only on the remaining voxels, speeding up the overall detection process. That is, in one embodiment, detection is carried out in stages such that the overall detection process is more efficient.
  • Each item that can be identified by the method of the invention is, in general, associated with a unique set of detection steps.
  • a detection approach is applied to eliminate inefficiencies introduced by repetitive processing of the data by multiple detection methods. Where one specific detection procedure has been applied to a set of data and has been used to classify that data, the data are removed from further processing. Terminating the detection process after detecting the first explosive can also increase the throughput of the system, since the operator inspects the bag images if a potentially explosive object is found.
  • Execution time limits can be applied to the process.
  • the execution time of the detection process increases significantly if the bag has a large total volume occupied by dense objects.
  • Such a luggage item may be declared suspicious and forwarded for further inspection by an operator without completing all of the detection process steps.
  • Sheet explosives may be detected as smaller pieces if they are located near a relatively opaque object such as a metal bar. If they are not merged, each part is eliminated by the mass threshold.
  • One way to merge these pieces in accordance with the invention is to fit a plane to each piece and determine the intersection of the two planes. If the intersection is close (in the predetermined limits) to both the pieces, then both pieces are considered to be part of a bigger sheet, and the mass becomes the total mass of the pieces. As a result, the merged object exceeds the mass threshold and is identified as a threat object.
  • liquid objects it may be the case that it is desirable to detect liquid objects. If it has been determined that liquid objects should be classified as non-threat items, then it can be beneficial to identify liquids as such to reduce the number of possible alarms. Many bags have bottles of liquid (shampoo, water, wine etc.) in them. If these bottles of liquid trigger a false alarm, they can be discriminated using a liquid detection method of the invention and thus reduce the number of false alarms.
  • liquid detection method of the invention it may be the case that it is desirable to detect liquid objects.
  • -54- liquid detection method distinguishes liquid bottles from solid objects. This detection uses the fact that the surface of a liquid is level with the horizontal, and air is usually above the surface. This assumes that the bottle of liquid is not completely full. Given the bounding box and the voxels of the detected object, the number of voxels touching each surface of the bounding box is determined and the percentage of the top surface count in the total count is computed. A voxel is concluded to touch a surface of the bounding box if it is labeled as being part of the object, i.e., bottle of liquid, and it is also located at or near the surface of the bounding box. If the percentage is larger than a predetermined threshold and the average value of the voxels above the liquid surface is close to air value, then the object is a liquid.
  • the invention determines whether an object is a contained liquid by first creating a bounding box which surrounds the object. Along the height of the bounding box, the histogram of the number of top-surface voxels and the histogram of the number of bottom-surface voxels are computed.
  • the top-surface voxels are defined as the voxels of the object that are visited first while traversing a column of voxels in the bounding box from top to bottom.
  • the bottom- surface voxels are defined as the voxels of the object that are visited last while traversing a column of voxels in the bounding box from top to bottom.
  • the position of the maximum of the histogram for top-surface voxels defines the location of the top surface along the height of the bounding box and the maximum count in the histogram defines the number of top-surface voxels.
  • the position of the maximum defines the location of the bottom surface and the maximum count defines the number of bottom-surface voxels.
  • the ratio of the number of top-surface voxels to the top-surface area of the bounding box is calculated.
  • this ratio exceeds a predetermined threshold, and the ratio of the number of top-surface voxels to the number of bottom-surface voxels exceeds another predetermined threshold, and if the average density of voxels above the top surface indicates that air is located above the top surface, then it is concluded that the object is a contained liquid. In one embodiment, it can then be concluded that the object does not pose a threat.
  • the overall performance of the system including detection rate and false
  • the overall detection rate of the many types of explosives depends on their a priori probabilities and the likelihood of detection.
  • the detection likelihood depends on the individual detection processes of each of these explosive types. The detection process may have done very well on one type but not so well on another type of explosive.
  • the overall detection rate is the average of the individual detection rates.
  • the overall false alarm rate of the system also depends on the individual false alarm rates.
  • the overall system false alarm rate is the sum of the individual false alarm rates.
  • individual and overall system detection and false alarm rates can be as presented in Table 1 for a particular set of predetermined thresholds and parameters for three separate types of materials labeled, for illustration purposes, as Type 1, Type 2, and Type 3.
  • Type 1 95 5 Type 2 100 15 Type 3 100 10
  • the individual detection rates for the three material types are interrelated. That is, for example, the detection rate for Type 2 materials is affected by the detection rate for Type 1 materials. In addition, as a detection rate is changed, its corresponding false alarm rate also changes.
  • one or more of the individual detection rates can be adjusted to bring the overall system into compliance. This can be done, for example, by lowering the detection rates of Type 2 and Type 3 materials. An example of the result of this adjustment is shown in Table 2. It is noted that the values in Tables 1
  • Type 1 95 5 Type 2 92 2 Type 3 98 3
  • the system is now in compliance with the sample specifications as a result of the adjustment to the two individual detection rates. It is noted that as each detection rate was adjusted down, its associated false alarm rate was also reduced. Hence, given the desired ranges for overall and individual detection rates, the overall detection rate and/or false alarm rate can be optimized by adjusting the individual detection likelihoods and false alarm rates. In one embodiment, the system is tunable to a specific detection rate and/or a specific false alarm rate.
  • an individual detection rate can be dependent on other individual detection rates. As a result, adjustment of one rate may inadvertently change another rate. To account for this effect, statistical data analysis approaches such as simulated annealing and genetic algorithms can be employed to determine parameters required to adjust the individual rates as required for a particular desired overall system performance.
  • the detection rates can be adjusted by one or more of several approaches.
  • the extensive analysis performed on actual threat and non-threat items has resulted in a relationship between object density and threat mass threshold.
  • a higher mass threshold for the density range of the particular threat can be employed.
  • other such deterministic parameter adjustment approaches can be employed. That is, other parameters described in
  • Intelligence data can provide additional information regarding possible shapes of explosive devices and their likely locations inside a bag. Statistics on shapes and locations of typical non-threat items carried in checked luggage can be gathered by testing items. This information can be used at the decision making stages of the method to further discriminate between threats and innocuous objects. For example, new discrimination features and/or process changes may be incorporated to account for perishable items.
  • the invention is applicable to detect explosives other than the specific materials disclosed above.
  • the invention is applicable to detect other objects and materials including drugs and currency.
  • the invention can be used to detect any of these items in checked and carried-on luggage and in shipping and other types of containers. What is claimed is:

Abstract

L'invention concerne un procédé et un appareil (100) pour la détection d'objets (112) dans des données de tomodensitométrie (TD). On peut détecter des objets en feuille, tels que des explosifs en feuille, en analysant la densité d'un voxel d'essai et la densité moyenne de ses voxels voisins. On peut également détecter les objets en feuille en procédant à des opération d'érosion et de dilatation. On peut calculer la masse corrigée en utilisant la densité érodée moyenne de l'objet et en comparant le résultat à des seuils de masse, pour classer l'objet selon qu'il constitue une menace ou non. On peut détecter des objets en vrac par une technique de marquage modifié de composants objets basée sur la morphologie, dans laquelle on procède à une série de phases d'érosion et de dilatation pour séparer les objets adjacents dans les données, de sorte qu'ils soient marqués et analysés séparément. Le système peut également identifier des objets contenant des liquides. On peut ajuster les performances globales du système, dont la vitesse de détection globale de l'objet et le taux de fausses alarmes, en ajustant la/les vitesses de détection des objets séparés.
PCT/US1999/001514 1998-02-11 1999-01-25 Appareil de tomodensitometrie et procede de classement d'objets WO1999041676A1 (fr)

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JP2000531792A JP2002503816A (ja) 1998-02-11 1999-01-25 対象を分類するコンピュータ断層撮影装置および方法
EP99904247A EP1062555A4 (fr) 1998-02-11 1999-01-25 Appareil de tomodensitometrie et procede de classement d'objets
AU24689/99A AU2468999A (en) 1998-02-11 1999-01-25 Computed tomography apparatus and method for classifying objects

Applications Claiming Priority (24)

Application Number Priority Date Filing Date Title
US09/021,782 1998-02-11
US09/022,060 1998-02-11
US09/021,781 US6075871A (en) 1998-02-11 1998-02-11 Apparatus and method for eroding objects in computed tomography data
US09/022,204 1998-02-11
US09/022,165 US6026143A (en) 1998-02-11 1998-02-11 Apparatus and method for detecting sheet objects in computed tomography data
US09/022,189 US6111974A (en) 1998-02-11 1998-02-11 Apparatus and method for detecting sheet objects in computed tomography data
US09/022,165 1998-02-11
US09/021,889 1998-02-11
US09/022,062 1998-02-11
US09/022,062 US6272230B1 (en) 1998-02-11 1998-02-11 Apparatus and method for optimizing detection of objects in computed tomography data
US09/022,059 US6317509B1 (en) 1998-02-11 1998-02-11 Computed tomography apparatus and method for classifying objects
US09/022,164 US6035014A (en) 1998-02-11 1998-02-11 Multiple-stage apparatus and method for detecting objects in computed tomography data
US09/021,781 1998-02-11
US09/022,164 1998-02-11
US09/022,354 1998-02-11
US09/022,059 1998-02-11
US09/022,204 US6067366A (en) 1998-02-11 1998-02-11 Apparatus and method for detecting objects in computed tomography data using erosion and dilation of objects
US09/022,064 1998-02-11
US09/021,889 US6078642A (en) 1998-02-11 1998-02-11 Apparatus and method for density discrimination of objects in computed tomography data using multiple density ranges
US09/022,354 US6108396A (en) 1998-02-11 1998-02-11 Apparatus and method for correcting object density in computed tomography data
US09/022,189 1998-02-11
US09/022,060 US6128365A (en) 1998-02-11 1998-02-11 Apparatus and method for combining related objects in computed tomography data
US09/021,782 US6076400A (en) 1998-02-11 1998-02-11 Apparatus and method for classifying objects in computed tomography data using density dependent mass thresholds
US09/022,064 US6026171A (en) 1998-02-11 1998-02-11 Apparatus and method for detection of liquids in computed tomography data

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