System and method for integrating explosive detection systems

An integrated detection system that includes a first threat detection apparatus and a second threat detection apparatus is provided. The first threat detection apparatus may identify one or more areas within an item of baggage that may contain threats. Example threats include, but are not limited to, explosives, weapons, illegal drugs, and hazardous matter, among others. The remaining areas are in theory deemed clear of threats. The second threat detection apparatus may be configured to inspect only the suspect areas of the item of baggage that was previously identified by the first threat detection apparatus. This improves throughput and lowers the false positive rate. A method for intelligently fusing the independent information obtained by the first and second threat detection apparatuses is also provided.

BACKGROUND

1. Field of the Invention

The technology disclosed herein generally relates to at least one apparatus and method for detecting targeted materials at security checkpoints or inline screening systems, and more particularly, to an apparatus and method for integrating first threat analysis data with second threat analysis data to provide an accurate and reliable final threat assessment.

2. Discussion of Related Art

With acts of global terrorism on the rise, detection of targeted materials has become increasingly important. Targeted materials may include, but are not limited to, explosives, weapons, and narcotics, among others. Advanced detection systems have been developed that can automatically identify not only the shapes of articles carried within baggage, but also the material characteristics and/or composition of those articles. Such detection systems include computed tomography (CT) scanners, quadropole resonance (QR) scanners, x-ray diffraction (XRD) scanners, and Advanced Technology (AT) scanners.

The performance of these detection systems is measured using three primary parameters, false positive rate, probability of detection, and scanning speed (throughput). Often, the improvement of one parameter occurs at the expense of another.

False positives occur when a detection system incorrectly identifies a harmless object/substance as an actual threat object/substance. False positives are commonly generated because conventional detection systems cannot always correctly distinguish actual threat objects/substances from harmless objects/substances in situations where both types of objects/substances exhibit similar threat characteristics, such as similar density and/or mass.

Detection systems are usually required to have a minimum probability of detection, or detection rate. The detection rate can be determined by systematically inserting objects containing one or more target materials of interest and then measuring the percentage of the times at which the detection system alarms.

Low numbers of false positives and high throughput are required for security checkpoints at public transportation facilities, such as airports. Co-pending U.S. patent application publication No. 2005/0128069 (hereinafter, “'069 publication”), describes how an upstream computed tomography (CT) scanner and a downstream quadropole resonance (QR) scanner, directly connected via a shared conveyor, may be used in sequence to reduce the number of false positives. The CT scanner scans an entire item of baggage and outputs a set of risk values indicative of the presence of particular types of targeted material. This risk values are inputted to the QR scanner, which scans the entire item of baggage a second time, generates its own risk values, and integrates these with the risk values inputted from the CT scanner.

Often, however, a second screening system will have considerable lower throughput than the first screening system. In such cases, it is desirable from a throughput point of view to screen only the part of the scannable object that contains the suspect region identified by the first system. Co-pending U.S. patent application publication No. 2005/0123217 (hereinafter, “'217 publication”) describes a method for improving baggage throughput. The method determines whether and how an item of baggage's position has changed from one detection apparatus to the next, and saves time by permitting a downstream detection apparatus to examine only a particular suspect region within the item of baggage. This method is schematically represented inFIG. 5.

Referring toFIG. 5, a first threat detection apparatus takes a first transmission image502. In the first transmission image502, a first suspect region is identified, and a first list518of coordinates of the first suspect region is created. The item of baggage is then placed in a second threat detection apparatus, which takes a second transmission image504to determine the geometrical transformation between the coordinate systems of the two threat detection apparatuses. Each transmission images502,504is subjected to pre-processing506,508, during which geometric rectification and optical pre-processing of intensities may be performed. Various features of the respective image contents are then measured in preparation for performing features extractions510,512. Comparative features are then determined and the second position of the scannable object relative to its first position is determined. The extracted features are appraised, and a calculation514of the change in position is performed. There is also a geometric transformation516of the images. Following the successful determination of position via the calculation514of the change of position, a second list520of coordinates of a transformed second suspect region is created. The coordinates of the second suspect region are the coordinates of the first suspect region that have been transformed into the second transmission image504.

Thus, in the known integrated detection system described in the '217 publication, no intelligent fusion of the independent information obtained by the upstream detection apparatus and the downstream detection apparatus is performed. Additionally, the known threat detection system described in the '069 publication scans the entirety of an object multiple times. A need therefore exists for an improved integrated detection system that achieves high throughput with a minimal number of “false positives.”

SUMMARY

The technology disclosed herein overcomes the disadvantages associated with the related art and meets the needs discussed above by providing an integrated detection system and a method for quickly, accurately, and reliably identifying whether particular types of targeted materials and/or targeted objects are present in scannable objects, including but not limited to, airline baggage.

In an embodiment, a method of detecting a type of threat in a scannable object may include the following steps, which may be performed in any suitable order. A step may include scanning a scannable object in its entirety in a first threat detection apparatus. Another step may include identifying, from an image of the scannable object, a suspect region in the scannable object. Another step may include scanning only the suspect region of the scannable object in a second threat detection apparatus. Another step may include combining and statistically processing preliminary risk value data generated by the first threat detection apparatus with present risk value data generated by the second threat detection apparatus. Another step may include outputting final risk value data indicative of a presence or absence of at least one of a targeted material and a targeted object in the suspect region.

In an embodiment, an integrated threat detection system may be provided that includes a first threat detection apparatus and a second threat detection apparatus. The first threat detection apparatus may be configured to: scan a scannable object in its entirety, identify a suspect region in an image of the scannable object, and generate preliminary risk value data. The second threat detection apparatus may be configured to scan only the suspect region of the scannable object. The second threat detection apparatus may be further configured to generate present risk value data based on said preliminary risk value data. The second threat detection apparatus may be further configured to combine and statistically process preliminary risk value data generated by the first threat detection apparatus with present risk value data generated by the second threat detection apparatus. The second threat detection apparatus may be further configured to output final risk value data indicative of a presence or absence of one of a targeted material and a targeted object in the suspect region.

In another embodiment, a method may begin by obtaining first threat analysis data from an entire scan of the scannable object by a first threat detection apparatus. The first threat analysis data may include preliminary risk value data. The preliminary risk value may include at least one of substance and first shape threat probability data for one or more threat categories. The method may further include the step of obtaining second threat analysis data from a scan of only a suspect region of the suspect object by the second threat detection apparatus. The second threat analysis data may include present risk value data that includes at least one of density and second shape threat probability data for the one or more threat categories. Another step may include combining and statistically processing at least the preliminary risk value data and the present risk value data using a Bayesian likelihood function. Another step may include outputting final risk value data. The final risk value data may include final threat probability data indicative of an actual presence or absence of the type of threat in the scannable object. The type of threat may be a particular type of targeted material and/or a particular type of targeted object.

Another embodiment of the invention may provide an apparatus configured to detect a threat in a scannable object. The apparatus may include an advanced technology (AT) scanner having dual-energy x-ray source/detector pairs. The AT scanner may be configured to obtain first threat analysis data from a scan of the entire scannable object. The first threat analysis data may include preliminary risk value data. The preliminary risk value data may include at least one of substance and first shape threat probability data for one or more threat categories.

The apparatus may also include a computed tomography (CT) scanner. The CT scanner may have at least one x-ray source/detector pair mounted to a rotatable gantry. The CT scanner may be configured to scan only a suspect region of the scannable object. Additionally, the CT scanner may be further configured to obtain second threat analysis data from a scan of only the second suspect region of the suspect object. The second threat analysis data may include present risk value data. The present risk value data may include at least one of density and second shape threat probability data for the one or more threat categories.

The CT scanner may be further configured to combine and statistically process at least the preliminary risk value data and the present risk value data using a Bayesian likelihood function, and to output final risk value data. The final risk value data may include final threat probability data indicative of an actual presence or absence of the type of threat in the scannable object.

DETAILED DESCRIPTION

Reference is made herein to the accompanying drawings briefly described above, which show by way of illustration various embodiments of the claimed invention. Persons of ordinary skill in the above-referenced technological field will recognize that other embodiments may be utilized, and that structural, electrical, and procedural changes may be made without departing from the scope of the claimed invention. As used herein, the singular (illustratively, “region”) includes the plural (illustratively, “regions”), and the plural includes the singular.

Embodiments of the invention described and claimed herein provide novel and non-obvious apparatus and methods for creating and operating integrated threat detection systems. In contrast to prior teachings that one performance parameter of an integrated threat detection system can only be improved at the expense of another performance parameter, embodiments of the claimed invention improve at least two performance parameters (throughput and false positive rate) simultaneously. These improvements may result, in part, from a unique data fusion among stage one, stage two, and/or stage three threat detection apparatuses.

In embodiments of the claimed invention, the dual energy x-ray information collected by a first threat detection apparatus enables inference of an effective atomic number of the threat. This is orthogonal data to density information collected by a second threat detection apparatus. A Bayesian data fusion of the information collected by the first threat detection apparatus and the density information collected by the second threat detection apparatus lowers overall false positive rates for the integrated threat detection system.

In one embodiment, the targeted screening of scannable objects is enabled by registration of x-ray images obtained from the two different types of threat detection apparatuses. First, a global registration may be performed, which computes the translational and rotational offsets of the scannable object as they appear in the two detection systems. Part of this global registration includes determining whether the scannable object has been flipped and/or rotated since being scanned by the first threat detection apparatus. Second, the target area identified by the first threat detection apparatus may be transformed into a target area in the second detection device by utilizing the image-offset parameters computed from the global registration. Thirdly, the x-ray attenuation in the two areas may be compared for verification. If they compare unfavorably, the target area of the second threat detection apparatus may be enlarged iteratively.

Independent data obtained by each of the first and second threat detection apparatuses may be transformed into threat probability evidence using likelihood functions and threat priors in a Bayesian probability theory framework. In one embodiment, a particular Bayesian framework (DSFP) for explosive detection may be employed. Other types of intelligent data fusion, however, are possible.

Advantages afforded by embodiments of the claimed invention include, but are not limited to: higher throughput for integrated threat detection systems; lower false positive rate; and fewer operators needed downstream from the second threat detection apparatus.

As a matter of convenience, one or more embodiments of the invention are described herein in the context of a baggage inspection system implemented as part of a typical airport security system. However, it is to be understood that the claimed invention is not so limited, and that many other applications are envisioned and possible within the teachings of the claimed invention. For example, applications of an integrated threat detection system constructed in accordance with the principles of the claimed invention include, but are not limited to, seaports, public buildings, public transportation facilities, prisons, hospitals, power plants, office buildings, hotels, casinos, and military facilities, among others.

The terms “baggage,” “scannable object(s),” and “item of baggage,” are used herein to generally refer to any type and size of an item, object, or substance that may be screened by an integrated threat detection system constructed in accordance with the principles of the invention, regardless of the size of the item/object and/or the quantity of the substance. Non-limiting examples of scannable objects may include suitcases, briefcases, backpacks, gels, liquids, gases, persons, cargo holds, over-the-road trailers, railcars, sea-land containers, and the like. The terms “baggage,” “scannable object(s),” and “item of baggage,” also include items/objects/substances that are contained within another item, object, and/or substance. Non-limiting examples may include boxes in a cargo hold, objects contained in carry-on or checked luggage, containers in a railcar that contain liquids or gases, and the like.

Many embodiments of an integrated threat detection system constructed in accordance with the principles of the invention may examine small-sized to medium-sized scannable objects, but others may examine large-sized scannable objects. Non-limiting examples of small-sized scannable objects are a personal digital assistant (PDA) and a digital music player, among others. Non-limiting examples of medium-sized objects are a suitcase and a backpack, among others. Non-limiting examples of a large-sized scannable object are semi-trailers, sea/land containers, automobiles, railroad cars, and aircraft, among others.

As used herein, the phrase “targeted material” includes both “targeted substances” and/or “targeted objects.” A “targeted substance” may be any type of matter or substance for which detection is desired that can be identified by atomic number. Non-limiting examples include explosives, illegal drugs, hazardous matter (such as chemical/biological/nuclear substances), and the like. A “targeted object” may be any type of item for which detection is desired that can be identified at least by its cross-sectional shape and/or silhouette. Non-limiting examples of targeted objects include weapons such as firearms and knives, explosives such as ammunition, grenades, and pipe bombs; and drug paraphernalia such as hypodermic needles and glass pipes, among others. A “targeted object” may also include a “targeted material.” As a non-limiting example, a firearm (targeted object) may contain metal, polymers, lubricants, and/or powder residue (e.g., targeted materials). Targeted objects may include organic materials and inorganic materials.

FIG. 1is a schematic of an integrated threat detection system10that includes a scanning subsystem12, and includes a computer subsystem14having that may include one or more databases28.

The scanning subsystem12may include a first threat detection apparatus16, a second threat detection apparatus18, and conveyor belts20,22. As shown inFIG. 1, the outlet of the first threat detection apparatus16may be physically separated from the inlet of the second threat detection apparatus18. For example, the first threat detection apparatus16could be located across the room, or in a different room/building, from the second threat detection apparatus18. A communication means30, such as a wired network, a wireless network, or a transferable computer disk, connects the first threat detection apparatus16(and/or a first computer26associated therewith) to the second threat detection apparatus18(and/or a second computer26associated therewith).

The first threat detection apparatus16and the second threat detection apparatus18may be any combination of any type of scanning device that may be configured to detect a targeted material and/or a targeted object. Thus, although several embodiments are illustratively described herein with reference to x-ray based scanning devices, the scope of the claimed invention is not so limited, and may include other types of scanning devices.

In an embodiment, the first threat detection apparatus16is an advanced technology (AT) hardware scanner (hereinafter referred to as “AT scanner”) of the type known to persons skilled in the threat detection art, such as, but not limited to, a dual-energy x-ray scanner. The second threat detection apparatus18is a computed tomography scanner (hereinafter referred to as “CT scanner) of the type known to persons skilled in the threat detection art. In another embodiment, the first threat detection apparatus16may be a CT scanner, and the second threat detection apparatus may be an AT scanner.

The AT scanner16includes a detection area into which scannable objects may be placed. The AT scanner16further includes two fixedly mounted x-ray source/detector pairs. Each x-ray source has a different voltage relative to the other, and both x-ray sources are positioned to interrogate, with x-ray radiation, a scannable object placed within the detection area.

CT scanner18includes a gantry support with a tubular detection area therethrough. A rotatable gantry may be mounted to the gantry support and configured to rotate an X-ray source and X-ray detector, secured to diametrically opposing sides of the movable gantry, about the tubular detection area. The CT scanner may be configured to perform a pre-scan and/or a detailed scan of a scannable object24. In an embodiment where the CT scanner is configured only to perform the detailed scan, a third threat detection apparatus may be positioned between the AT scanner and the CT scanner and configured to perform the pre-scan. The third threat detection apparatus may be coupled with the computer subsystem14. In an alternate embodiment, the CT scanner may be configured to perform a full volumetric scan, thereby eliminating the need for a pre-scan.

Referring again toFIG. 1, the computer subsystem14may include one or more computers26and may optionally include at least one database28configured to be accessed by each of the computers26. The computers26are of the type known to persons skilled in the computer and/or threat detection arts. As shown inFIG. 1, the one or more computers26may include a first computer configured to communicate with the first threat detection apparatus16, and a second computer configured to communicate with the second threat detection apparatus18and/or with the first computer. Each computer26may be configured to trigger an alarm to alert operators of the integrated threat detection system to a suspected or actual threat if a calculated threat probability meets a pre-determined threshold.

Each of the computers26includes a main bus to which are coupled a main memory, a processor, an alpha-numeric input device, a display device, and/or one or more of the following: a static memory, cursor control device, a drive unit including a machine-readable medium, a signal generation device, and a network interface device. One or both of the database28and the static memory may be components of the computer subsystem14. The machine-readable medium may include a set of instructions, which may be transferred to the processor and the main memory through the main bus. Additionally, a wired or wireless communication means30links the computer subsystem14with at least the first threat detection apparatus16, the second threat detection apparatus18, and the database28.

In another embodiment, the computer subsystem14may include a single central computer having the components described above. The central computer may be coupled with the database28, the first threat detection apparatus16, and the second threat detection apparatus18.

Referring toFIGS. 1 and 2, a technical effect afforded by embodiments of the invention may be a statistical data fusion240that outputs final risk value data250. The data fusion240may combine and/or statistically process (in a Bayesian or other statistical framework) the preliminary risk value219and the present risk value data229. The final risk value data250includes final threat probability data indicative of the presence or absences of a targeted material and/or a targeted object in the scannable object24. The phrase “combine and statistically process” includes adding, subtracting, multiplying, dividing, comparing, averaging, generating and applying Bayes' rule, and/or otherwise processing first threat probability values included in the preliminary risk value data219together with second threat probability values included in the present risk value data229.

In one embodiment, the first threat analysis data210associated with the first threat detection apparatus16may be stored in the database28and/or in the static memory, processed by the processor of at least the computer26coupled with the first threat detection apparatus16, and/or inputted to the second threat detection apparatus18. Similarly, the second threat analysis data220associated with the second threat detection apparatus18may be stored in the database28and/or in the static memory, and/or processed by the processor of the computer26coupled with the second threat detection apparatus18. The final risk value data250may be stored in the database28and/or in the static memory. Any or all of the first threat analysis data210, the second threat analysis data220, differences in object orientation data230, and final risk value data230may be displayed on a display device coupled with one or both the computers26.

The first threat analysis data210may include, but is not limited to, substance data211indicative of a targeted substance detected within a first suspect region32of the scannable object24; Bayesian likelihood functions217; and preliminary risk value data219that includes substance and/or first shape threat probability data.

The first threat analysis data210may further include first image data218. The first image data218may include, but is not limited to, first shape data212indicative of a targeted object detected within the first suspect region32of the scannable object24; first transmission image data213showing the exterior, interior, and/or contents of the scannable object24; geometric identification data214indicating the coordinates in the first transmission image data of the first suspect region323; features215extracted from the first transmission image data213; and comparative features216identified in the first transmission image data213.

Similarly, second threat analysis data220may include, but is not limited to: density data221indicative of a targeted substance detected within a second suspect region34(that corresponds to the first suspect region32in a different orientation and/or coordinate system) of the scannable object24; Bayesian likelihood function227; and present risk value data229that includes density and/or second shape threat probability data. The second threat analysis data220may further include second image data228. The second image data228may include, but is not limited to, second shape data222indicative of a targeted object detected within the second suspect region34of the scannable object24; second transmission image data223showing at least an exterior, interior, or contents of the second suspect region34; geometric identification data224indicating the coordinates of the second suspect region34in the second transmission image data223; features225extracted from the second transmission image data223; and comparative features226identified in the second transmission image data223.

In one embodiment, the first image data218and the second image data228may be processed by a computer to obtain data230indicative of a difference (or differences) in the orientation of the scannable object24from one threat detection apparatus to another. The differences in object orientation data230may be used by the second threat detection apparatus18to translate the coordinates of the first suspect region32into the coordinates of the second suspect region34. Once the coordinates of the second suspect region34have been determined, the second threat detection apparatus18may be configured260to scan and analyze only the second suspect region34to reduce scanning times and increase throughput.

In one embodiment, at least the preliminary risk value data219and the present risk value data229may be combined at data fusion240to generate final risk value data250indicative of the confirmed presence or absence of an actual threat in, or posed by, the scannable object24. The final risk value data250may include, but is not limited to, final threat probability data, in one or more threat categories of interest, detected within the second suspect region34of the scannable object24. The threat probability data may be, or may include, data identifying the type of targeted material and/or target object detected within the second suspect region34.

In one embodiment, threat probability values may be calculated, using Bayesian likelihood functions217and227for each of the substance data211(and/or the first shape data212) and the density data221(and/or the second shape data222), respectively. The threat probability values may be used to trigger an alarm if one or more of the calculated probability values meets a predetermined critical probability value threshold. The preliminary risk value data219may include substance and/or first shape threat probability data values that indicate how likely the scannable object24is to contain a particular type of targeted material and/or a particular type of targeted object. In one embodiment, the present risk value data229may include density and/or second shape threat probability data values that indicate how likely the suspect region34is to contain the targeted material and/or the targeted object initially identified by the first threat detection apparatus16.

FIG. 3illustrates an exemplary threat probability data table300(hereinafter “table300”) that may be stored in the database28and/or the static memory. A separate threat probability table300may be created for each of the preliminary risk value data219, the present risk value data229, the data fusion240, and the final risk value data250. The table300may include “n” columns and “m” rows, where “n” is an integer greater than zero and where “m” is an integer equal to or greater than “n.” As a non-limiting example, the table300illustratively shown inFIG. 3includes four rows310,320,330,340, and five numbered columns350,360,370,380,390. InFIG. 3, an unnumbered column filled with ellipsis “ . . . ” is illustratively positioned between columns370and380, and an unnumbered row filled with ellipsis “ . . . ” is illustratively positioned between rows330and340.

Row310may be a header row. The header row310may contain titles of various threat categories. Column350is illustratively titled “Person/Object.” Column360is illustratively titled “Threat Category 1.” Column370is illustratively titled “Threat Category 2.” Column380is illustratively titled “Threat Category N,” where “N” is an integer greater than two. Column390is illustratively titled “Clear.” Each of rows320,330, and340may be data rows. In column250, data row320may include data identifying a first scannable object (e.g., “Person/Object 1”); data row330may include data identifying a second scannable object (e.g., “Person/Object 2”); and data row340may include data identifying another scannable object (e.g., “Person/Object M”), where “M” is an integer greater than 2.

As shown inFIG. 3, threat probability data301,302is located at the intersections of one or more data threat category columns360,370,380, and390and scannable object rows320,330, and340. If the scannable object24is the first scannable object, its threat probability values are populated across row320. If the scannable object24is the second or Mth scannable object, its threat probability values are populated across rows330and340, respectively.

FromFIG. 3, it can be seen that the threat probability values across each of rows320,330, and340total “1.” Thus, each of the “preliminary risk values,” “present risk values,” and “final risk values” may be a number from 0 to 100, a percentage from 0% to 100%, or a number between 0 and 1, among other ranges.

The threat probability values in the table300signify the probability that a targeted object or targeted material is present in the scannable object24. Thus, the threat probability values in row320may be interpreted as follows: “Person/Object 1” is 40% percent likely to contain: a) a first targeted material (and/or a first targeted object) associated with “Threat Category 1,” and/or b) a second targeted material (and/or a second targeted object) associated with “Threat Category 2.” Additionally, the “Person/Object 1” is 0% likely to contain an nth targeted material (and/or nth targeted object) associated with “Threat Category N.” Consequently, “Person/Object 1” is 20% likely to be clear of targeted materials and/or targeted objects. Thus, in one embodiment, a low number in the “Clear” column390represents significant threat risk warranting further inspection and/or special handling of the scannable object24, while a high number represents minimal threat risk that may not warrant further inspection and/or special handling of the scannable object24. Illustratively, a non-limiting example of a low number in the “Clear” column390may be a value in the range of “0.00” to “0.59”, while a non-limiting example of a high number may be a value in the range of “0.60 to 1.0.”

A low number in any of “Threat Category” columns360,370, and380may indicate a minimal probability that a scannable object24contains a particular type of targeted material and/or a particular type of targeted object. Thus, referring again to table300, the intersection of row330and column380contains a threat probability value of “0.1”, which may signify that the scannable object24is 10% likely to contain a targeted material (and/or a targeted object) associated with the “Threat Category N.” A high number in any of “Threat Category” columns360,370, and380may indicate a significant probability that a scannable object24contains a particular type of targeted material and/or a particular type of targeted object. Thus, referring again to table300, the intersection of row330and column360contains a threat probability value of “0.8”, which may signify that the scannable object24is 80% likely to contain a targeted material (and/or a targeted object) associated with the “Threat Category 1.”

Referring toFIGS. 2 and 3, to populate a first table300, first threat probability values are calculated by processing the substance data211(and/or the first shape data212) with Bayesian likelihood functions217. The first threat probability values collectively form the preliminary risk value data219. To populate a second table300, second threat probability values are calculated by processing the density data221(and/or the second shape data222) with Bayesian likelihood functions227. The first threat probability values may serve as priors for the Bayesian likelihood functions227. The second threat probability values collectively form the present risk value data229. To populate a third table300, the first and second threat probability data (e.g., the preliminary and present risk value data219,229) may be combined and/or statistically processed to generate final threat probability values, which collectively form the final risk value data250. Depending on the value(s) of the final risk value data250, an alarm may be triggered, and the scannable object24may be subjected to further scanning and/or special handling.

An exemplary operation of the integrated threat detection system10is now described with reference toFIGS. 1,2and3. To begin, a scannable object24may be placed in a first orientation36on a conveyor20within a passageway of a first threat detection apparatus16, which in one embodiment, may be an AT scanner configured to produce x-rays of two different voltages. The two different x-ray voltages provided by the AT scanner create two different x-ray images of the scannable object24and provide dual-energy data indicative of the atomic number of one or more materials comprising and/or contained in and/or attached to the scannable object24. The dual-energy x-ray data may be processed using conventional image processing techniques to form a first transmission image. The first transmission image may be used to determine the scannable object's first orientation36and/or to identify a first suspect region32within the scannable object24. A list of coordinates of the first suspect region32may also be created, and various pre-processing steps (such as those described above with respect toFIG. 5) may be performed.

Once the dual-energy data identifies an atomic number, the atomic number may be correlated to a particular type of material associated with a predetermined threat category. For example, if an atomic number associated with cyclotrimethylenetrinitramine (RDX) (or other type of targeted material) is determined, a threat probability value, calculated using the Bayesian likelihood functions217, may be entered into table300under a threat category360,370, or380to which RDX was previously associated, and a counterpart threat probability value may be entered into table300under the “Clear” column390.

In like manner, any first shape data212identified by the CT scan data may be correlated to a particular type of targeted object previously associated with a predetermined threat category. The threat probability values associated with the first shape data212may be entered into the same or a different table300as the threat probability values associated with the density data211.

When it is determined that a first suspect region32of a scanned object24contains (or is likely to contain) a targeted material and/or a targeted object, an alarm may trigger. Additionally, the preliminary risk value data219indicative of the same may be shared with the second threat detection apparatus18as Bayesian priors for the Bayesian likelihood functions227. Additionally, the threat probability values associated with the first shape data212may be shared with the second threat detection apparatus18as Bayesian priors for the Bayesian likelihood functions227. At data fusion240, the preliminary risk value data219may be combined and/or statistically processed with the present risk value data229generated from the second threat detection apparatus18.

Thereafter, the scannable object24may be positioned on a separate conveyor22for scanning and/or pre-scanning by the second threat detection apparatus18, which may be a CT scanner having rotatable, gantry-mounted, x-ray source/detector pairs.

A second transmission image of the scannable object24may be used to determine whether the scannable object has been rotated and/or flipped (e.g., second orientation38), and if so, what the coordinates of the first suspect region32are in the CT scanner's coordinate system. If differences in the object orientation230are determined, coordinates of a second suspect region34(which is the first suspect region32positioned within the CT scanner's coordinate system) are determined and used to configure the CT scanner to scan only the second suspect region34. This reduces scan times and increases baggage throughput. The resulting x-ray CT image slices may be combined to create a three-dimensional view of the second suspect region34of the scannable object24.

This three-dimensional view may be used to obtain density data221of the materials and/or second shape data223of the objects that comprise and/or are contained within the second suspect region34.

Once the CT scan data identifies a material's density, the density value may be correlated to a particular type of targeted material associated with a predetermined threat category. For example, if a density associated with cyclotrimethylenetrinitramine (RDX) is determined, a threat probability value, calculated using the Bayesian likelihood functions227, may be entered into a second table300under a threat category360,370, or380to which RDX was previously associated. Additionally, a counterpart threat probability value may be entered into the second table300under the “Clear” column390.

In like manner, shape data identified by the CT scan data may be correlated to a particular type of targeted object associated with a predetermined threat category. The threat probability values associated with the second shape data222may be entered into the same or a different table300as the threat probability values associated with the density data221.

At the data fusion step240, the preliminary and present risk value data219,229may, as previously described, be combined, and/or further processed using additional Bayesian likelihood functions to output final risk value data250.

FIG. 4is a flowchart illustrating one embodiment of a method400of providing an accurate and reliable final threat assessment of a first suspect region32and a counterpart second suspect region34within a scannable object24. The method400may begin at step402when initial Bayesian prior information is inputted to the computer associated with the AT scanner. The inputted Bayesian prior information may include pre-determined threat probability values associated with one or more types of scannable objects. For example, an exemplary prior substance threat probability value of “0.6” in a first threat category (such as “explosives”) could be inputted for a scannable object such as a pistol case, whereas an exemplary prior threat probability value of “0.1” could be inputted in the same threat category for a different scannable object such as a handbag. Similarly, an exemplary prior shape threat probability value of “0.7” in a first threat category (such as “Firearm”) could be inputted for a scannable object such as a person. Additionally, a pre-determined threat probability value may be inputted for a particular nationality of origin associated with the scannable object.

At step404, the AT scanner scans the scannable object in its entirety and obtains both image data and substance data. At step406, the image data is processed using any suitable image processing technique, and at step408, a first suspect region in the scannable object is identified. Statistical analysis of the substance data may be used to identify and define the first suspect region, which is an area of the scannable object likely to contain a targeted material or targeted object. At step420, the coordinates of the first suspect region are stored in a database and/or in a static memory. At step412, the dual-energy x-ray data obtained from the AT scanner is processed to identify a probable targeted material and/or a targeted object in the first suspect region. At step414, substance and/or first shape data obtained by the AT scanner is converted, using statistical analysis, into probabilities of a presence of a threat in one or more threat categories of interest, using one or more first Bayesian likelihood functions. At step416, a decision is made. If a pre-determined threshold is not met, the scannable object may be subjected to optional further processing (step438), such as a full scan by a subsequent scanner. If the pre-determined threshold is met, an alarm may be triggered. At step418, if the alarm is triggered, the threat probabilities of step414may be shared with a second scanner as Bayesian priors.

At step420, the scannable object is prepared for a second scan, which may be performed by a CT scanner. At step422, and optional pre-scan may be performed to obtain second image data. At step424, the second image data is processed using any suitable image processing technique. At step426, differences in the orientation of the scannable object between scanning systems are determined, and the coordinates of the first suspect region are transformed into coordinates of a second suspect region. At step428, the CT scanner is configured to scan only the second suspect region. At step430, a CT scan of only the second suspect region is performed. At step432, density and/or second shape data obtained by the CT scanner are converted to probabilities of a presence of a threat in one or more threat categories of interest, using one or more second Bayesian likelihood functions. At step434, the preliminary and present risk data obtained from the AT scanner and the CT scanner, respectively, are combined as described above. At step438, final risk value data is outputted and/or displayed on a display device. At step440, the final threat probability values are compared with pre-determined thresholds. At step442, an alarm is triggered if one or more of the alarm thresholds are met. At step444, the scannable object may optionally be further processed. Thereafter, the method400may end.

A detailed description of various embodiments of the claimed invention has been provided; however, modifications within the scope of the claimed invention will be apparent to persons having ordinary skill in the above-referenced technological field. Such persons will appreciate that features described with respect to one embodiment may be applied to other embodiments. Thus, the scope of the claimed invention is to be properly construed with reference to the following claims.