U.S. patent application number 12/129055 was filed with the patent office on 2009-02-26 for nuclear material detection system.
Invention is credited to Peter Dugan, Robert L. Finch, Shawn Locke, John M. Munley, Gregory Reyner, Kenei Suntarat.
Application Number | 20090052622 12/129055 |
Document ID | / |
Family ID | 40088192 |
Filed Date | 2009-02-26 |
United States Patent
Application |
20090052622 |
Kind Code |
A1 |
Dugan; Peter ; et
al. |
February 26, 2009 |
NUCLEAR MATERIAL DETECTION SYSTEM
Abstract
A method for automatically detecting nuclear material using
radiographic images of a cargo container includes receiving a
plurality of radiographic images of the cargo container and
aligning the plurality of images with respect to each other to
produce registered images. The method also includes segmenting the
registered images using the atomic number and other edge/texture
information in order to locate one or more regions of interest
within the registered images and estimating atomic number
information for each of a predetermined number of portions of the
registered images. The method includes assigning a threat level and
a confidence value to regions of interest identified as a potential
threat and evaluating the regions of interest identified as
potential threats using material context information and adjusting,
based on the evaluation, the threat level values and confidences of
the regions of interest identified as potential threats. The method
also includes providing the regions of interest and adjusted threat
level and confidence values as output to an operator station.
Inventors: |
Dugan; Peter; (Ithaca,
NY) ; Finch; Robert L.; (Endicott, NY) ;
Locke; Shawn; (Owego, NY) ; Reyner; Gregory;
(Apalachin, NY) ; Munley; John M.; (Endwell,
NY) ; Suntarat; Kenei; (Funabashi-Shi, JP) |
Correspondence
Address: |
MILES & STOCKBRIDGE PC
1751 PINNACLE DRIVE, SUITE 500
MCLEAN
VA
22102-3833
US
|
Family ID: |
40088192 |
Appl. No.: |
12/129055 |
Filed: |
May 29, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60940632 |
May 29, 2007 |
|
|
|
Current U.S.
Class: |
378/57 |
Current CPC
Class: |
G01N 23/06 20130101;
G06K 9/6263 20130101 |
Class at
Publication: |
378/57 |
International
Class: |
G01N 23/04 20060101
G01N023/04 |
Claims
1. A method for automatically detecting nuclear material using
radiographic images of a cargo container, the method comprising:
receiving a plurality of radiographic images of the cargo
container; aligning the plurality of images with respect to each
other to produce registered images; segmenting the registered
images using the atomic number information in order to locate one
or more regions of interest within the registered images;
estimating atomic number information for each of a predetermined
number of portions of the registered images; assigning a threat
level and a confidence value to regions of interest identified as a
potential threat; evaluating the regions of interest identified as
potential threats using material context information and adjusting,
based on the evaluation, the threat level values and confidences of
the regions of interest identified as potential threats; and
providing the regions of interest and adjusted threat level and
confidence values as output to an operator station.
2. The method of claim 1, further comprising scanning the cargo
container with an imaging device to produce the plurality of
radiographic images.
3. The method of claim 1, wherein the imaging device includes a
linear accelerator and detector combination.
4. The method of claim 1, wherein the each of the radiographic
images is produced using a different energy level.
5. The method of claim 1, further comprising producing the
radiographic images at a shipping port.
6. The method of claim 5, wherein the evaluating is performed at a
geographic location remote from the port.
7. The method of claim 1, wherein the step of estimating atomic
number information includes generating a gray scale image having
gray values that correspond to an estimated atomic number of a
material being scanned.
8. The method of claim 6, wherein the operator station is located
at the port.
9. The method of claim 1, wherein the material context information
includes a cargo manifest.
10. A system for automatically detecting nuclear material in a
radiographic image of a cargo conveyance, the system comprising: a
material domain imaging module adapted to receive a plurality of
radiographic images and to determine an atomic number for each
pixel in a combined atomic number image, the atomic number based on
an analysis of a pixel in each of the radiographic images; an
object segment recognition module coupled to the material domain
imaging module and adapted to perform an image segmentation
operation on the combined atomic number image and to identify and
return one or more object regions to the material domain imaging
module, whereby the material domain imaging module can perform an
object level atomic number analysis using a region of the combined
atomic number image defined by the one or more object regions; a
material context analysis module coupled to the material domain
imaging module and adapted to receive the combined atomic number
image, having been segmented into the one or more object regions,
and to generate a hypothesis as to whether an object region that
has been assigned a high atomic number is in a suspicious location
of the image, the hypothesis generation based on a computer-usable
representation of expert knowledge and historical data, the
material context analysis module also being adapted to provide the
hypothesis as output to another module; and an advanced cognitive
arbitration module coupled to the material domain imaging module
and adapted to receive a plurality of threat hypotheses as inputs,
each having an associated confidence value, the advanced cognitive
arbitration module adapted to rank the inputs, assign a confidence
value to each ranked input and provide a ranked list of threats,
with associated confidence values, as output.
11. The system of claim 10, wherein the cargo conveyance includes a
cargo container.
12. The system of claim 11, wherein the cargo conveyance further
includes a truck and a trailer onto which the cargo container has
been loaded.
13. The system of claim 10, wherein the plurality of radiographic
images includes four images each produced using a different energy
level.
14. The system of claim 10, wherein the material domain imaging
module determines an atomic number estimate for each of the object
regions identified by the object segment recognition module.
15. A threat detection system comprising: means for determining
estimated material atomic number values based on two or more
radiographic images produced using a plurality of energy levels and
generating an estimated atomic number gray level image, the gray
level being based on the estimated material atomic number values;
means for segmenting the gray level image to define one or more
regions of interest; means for analyzing a threat level of each
region of interest using material context information; and means
for arbitrating among multiple potential threat results to
determine a final threat decision array for output.
16. The threat detection system of claim 15, wherein the plurality
of energy levels includes at least four energy levels.
17. The threat detection system of claim 15, wherein at least one
of the multiple threat results are received from another threat
detection system different from said threat detection system.
18. The threat detection system of claim 15, wherein the threat
decision array includes a confidence value corresponding to each
potential threat.
19. The threat detection system of claim 15, wherein the means for
determining estimated material atomic number values further
includes determining an estimated material atomic value for each
region of interest.
20. The threat detection system of claim 15, wherein the material
context information includes a cargo manifest listing the contents
of a cargo conveyance being screened for threats.
Description
[0001] The present application claims the benefit of U.S.
Provisional Patent Application No. 60/940,632, entitled "Threat
Detection System", filed May 29, 2007, which is incorporated herein
by reference in its entirety.
[0002] Embodiments of the present invention relate generally to
detection of nuclear (or other high atomic number) materials and,
more particularly, to systems and methods for computerized
automatic detection of nuclear materials in cargo conveyances.
[0003] In order to evaluate (or screen) cargo conveyances in an
efficient manner it is desirable to automatically analyze images
obtained by scanning the cargo conveyances. The images can be
analyzed to determine whether a suspicious (or potential threat)
material, such as a special nuclear material or other material
having a high atomic number, may be present in the cargo container.
In performing an automatic analysis of radiographic images to
determine whether a potential threat material is present in the
image, a need for sensitivity is often balanced against a need for
a low false alarm rate. These competing needs are often expressed
as requirements that an automatic image analysis system have a
certain probability of detection of a potential threat material and
a certain confidence of a true positive indication.
[0004] Embodiments of the automatic nuclear material detection
method and system of the present invention may provide a reduced
false alarm rate while maintaining a desired rate of detecting
threats by increasing both the rate of identifying potential
threats and the rate of identifying typical false alarms.
[0005] One exemplary embodiment includes a method for automatically
detecting nuclear material using radiographic images of a cargo
container includes receiving a plurality of radiographic images of
the cargo container and aligning the plurality of images with
respect to each other to produce registered images. The method also
includes segmenting the registered images using the atomic number
and other pattern information in order to locate one or more
regions of interest within the registered images and estimating
atomic number information for each of a predetermined number of
portions of the registered images. The method includes assigning a
threat level and a confidence value to regions of interest
identified as a potential threat and evaluating the regions of
interest identified as potential threats using material context
information and adjusting, based on the evaluation, the threat
level values and confidences of the regions of interest identified
as potential threats. The method also includes providing the
regions of interest and adjusted threat level and confidence values
as output to an operator station.
[0006] Another exemplary embodiment includes a system for
automatically detecting nuclear material in a radiographic image of
a cargo conveyance. The system includes a material domain imaging
module, an object segment recognition module, a material context
analysis module, and an advanced cognitive arbitration module.
[0007] The material domain imaging module is adapted to receive a
plurality of radiographic images and to determine an atomic number
for each pixel in a combined atomic number image, the atomic number
based on an analysis of a pixel in each of the radiographic
images.
[0008] The object segment recognition module is coupled to the
material domain imaging module and is adapted to perform an image
segmentation operation on the combined atomic number image and to
identify and return one or more object regions to the material
domain imaging module, whereby the material domain imaging module
can perform an object level atomic number analysis using a region
of the combined atomic number image defined by the one or more
object regions.
[0009] The material context analysis module is coupled to the
material domain imaging module and is adapted to receive the
combined atomic number image, having been segmented into the one or
more object regions, and to generate a hypothesis as to whether an
object region that has been assigned a high atomic number is in a
suspicious location of the image, the hypothesis generation based
on a computer-usable representation of expert knowledge and
historical data, the material context analysis module is also
adapted to provide the hypothesis as output to another module.
[0010] The advanced cognitive arbitration module is coupled to the
material domain imaging module and is adapted to receive a
plurality of threat hypotheses as inputs, each having an associated
confidence value, the advanced cognitive arbitration module adapted
to rank the inputs, assign a confidence value to each ranked input
and provide a ranked list of threats, with associated confidence
values, as output.
[0011] Another exemplary embodiment includes a threat detection
system. The threat detection system includes means for determining
estimated material atomic number values based on two or more
radiographic images produced using a plurality of energy levels and
generating an estimated atomic number based on the estimated
material atomic number values. System is also capable of creating a
hybrid image which contains gray level intensity information and
atomic numbers by superimposing the atomic values onto the original
gray level images measured using the radiographic system. The
threat detection system also includes means for segmenting the
hybrid image to define one or more regions of interest and means
for analyzing a threat level of each region of interest using
material context information. The threat detection system also
includes means for arbitrating among multiple potential threat
results to determine a final threat decision array for output.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a block diagram of an object screening system
including an exemplary nuclear material detection system;
[0013] FIG. 2 is a block diagram of an exemplary nuclear material
detection system;
[0014] FIG. 3 is a block diagram of an exemplary nuclear material
detection system showing interfaces between four processing
modules; and
[0015] FIG. 4 is a flowchart of an exemplary method for detecting
nuclear materials in a screened object.
DETAILED DESCRIPTION
[0016] In general, embodiments of the present invention use an
image processing system to automatically identify and detect
potential threats based on a set of radiographic images of an
object being screened. The image processing system includes four
primary functional areas: material Z value (or atomic number)
determination; object segmentation; false alarm reduction; and
advanced decision making. These four areas can be embodied as a
material domain image (MDI) processor, object segmentation
radiograph (OSR) processor, material context analysis (MCA)
processor, and an advanced cognitive arbitrator (ACA) processor.
These functional areas and processors can comprise computer
hardware, software, or both.
[0017] The nuclear material detection system and method can be used
to detect unauthorized, illegal or illicit attempts to import,
assemble, or transport a nuclear explosive device (or a portion
thereof), fissile material and/or radiological material. Such
attempts may be connected with a potential threat to safety or
security. These contraband materials or devices often can be
detected and identified based on an estimate of the associated
atomic number of the materials. For example, materials with an
atomic number (or Z value) greater than 72 may be categorized as
high atomic number materials. Elements with a high atomic number
include special nuclear materials (SNM) such as plutonium (Pu),
highly enriched uranium (HEU), and some elements (e.g., lead (Pb)
and tungsten (W)) that may be effective in shielding SNM or other
radioactive materials from passive gamma radiation detection.
[0018] In addition to automatically detecting materials having a
high Z value, embodiments can include a capability for detecting
traditional contraband such as drugs, currency, guns, and
explosives. The capability to detect traditional contraband may be
fully automatic or may include some manual operator image
analysis.
[0019] FIG. 1 is a block diagram of an object screening system
including an exemplary nuclear material detection system. In
particular an object screening system 100 can be used to screen an
object to be scanned 102 in order to detect contraband such as
nuclear material. The object 102 is subjected to two or more
electromagnetic energies (with two, 104a and 104b, being shown for
illustration purposes) produced by the scanner 106. The scanner 106
receives returned or radiated energy and produces scanned images
108 that are sent to a threat detection system 110.
[0020] The threat detection system 110 processes the scanned images
108 to detect (preferably automatically) nuclear material or other
contraband and communicate results of the detection to an operator
station 114 via link 112. The threat detection system can be a
stand alone system or form part of a larger security system. Link
112 can be a wired or wireless link such as a LAN, WAN, wireless
network connection, radio link, optical link, or the like.
[0021] The energies 104a and 104b can include, for example, two or
more different energy levels of x-ray energy. It will be
appreciated that other types of electromagnetic energy can be used
to scan the object 102. It will also be appreciated that although
two energies (104a and 104b) are shown, more energy levels (e.g.,
four) can be used with an embodiment. Any type of scanner suitable
for detecting contraband such as nuclear material and capable or
producing an image (or array of values) may be used. The object
being screened (or scanned) can include a cargo container, a truck,
a tractor trailer, baggage, cargo, luggage, a vehicle, an air cargo
container, and/or any object being transported that could
potentially contain nuclear material or a portion of a threat or
weapon system, or any object for which threat or contraband
screening is contemplated or desired. The object being screened or
scanned can also include a mail piece such as a letter, flat,
package, parcel or the like. The radiographic images can be
produced or generated at a shipping port, a border crossing, an
airport, a truck terminal, or other facility or location where
scanning or screening of objects using radiographic techniques may
be desired. The images may be analyzed at the location where they
are produced or may be provided to another location for analysis
using a suitable communication method. Also, while the exemplary
embodiments discussed herein are directed to detection of nuclear
materials in cargo using threat scanning systems, it will be
appreciated that the invention has application in other areas such
as medical imaging and detection, material imaging for structural
analysis or quality control, and the like. In general, the system
described herein may be applied to any imaging context where
detection of regions of interest having certain characteristics is
desired.
[0022] FIG. 2 is a block diagram of an exemplary nuclear material
detection system, showing greater detail. In particular, the threat
detection system 110 includes a Z-analysis module 202, a
segmentation processing module 204, a material context analysis
module 206, and a threat decision arbitration module 208.
Radiographic images 210 can be provided as input to the threat
detection system 110. The output can include an indication of
potential threat and/or false alarms 212.
[0023] In operation, the threat detection system 110 receives two
or more radiographic images 210. The radiographic images, and
associated data, can be provided in a proprietary format or in a
standard format such as the N42 format, promulgated by American
National Standards Institute (ANSI). Two or more images are
typically used, and four images taken at four different energy
levels can be particularly advantageous. The various energy levels
provide different imaging characteristics. By using the different
images for analysis, the advantages of each energy level can be
realized, while attempting to reduce the disadvantages of each
energy level. For example, while higher energy levels may provide
better penetration through certain materials, the higher energy
levels may saturate other materials. On the other hand, low energy
levels may not provide as much penetration, but may also not have
the saturation that accompanies higher energy levels. Thus, by
using a combination of high and low energy levels, an embodiment
may provide some of the benefits of each energy level and this may
lead to a reduced false alarm rate and improved detection rate.
[0024] These images are typically first registered (or aligned) in
order that subsequent analyses of the images are performed on
corresponding portions of the images. Registration may be needed
because the different images may be taken at different times with
different imaging characteristics. Thus, registration may be needed
for transforming the different sets of image data into one
coordinate system. Registration may be done through a feature-based
process or any other known or later developed registration method,
such as area-based, transformation, search-based, spatial domain,
frequency domain or the like. Two or more registration methods can
be combined to register the images. In addition to the images,
other data, such as a threat threshold, may also be provided as
input. The registered images are provided as input to the
Z-analysis processor (or routines) 202.
[0025] The Z-analysis module 202 determines an estimated atomic
weight for the materials within the registered images. This
determination can be performed at the pixel level, or at an object
level including regions and layers to provide an enhanced analysis.
If the determination of estimated atomic number is being performed
at the object level, then object segmentation (described below)
would be performed prior to Z-analysis. The Z-analysis module 202
can employ multiple algorithms to provide an enhance Z-analysis
imaging capability. Once a Z-analysis has been performed, the pixel
level Z-analysis is provided, along with the images, to the
segmentation processing module 204.
[0026] The segmentation processing module 204 uses one or more
image segmentation algorithms in order to identify objects in the
image and report them to the Z-analysis module in the form of
region of interest (ROI) coordinates. The threat objects being
screened for by nuclear threat detection systems are typically
dense and may appear solid in nature when analyzed. However,
because various items in a cargo container may be layered between
the scanner and the imaging device, overlapping regions of less
dense material may appear as a denser and higher atomic number
material. This poses a significant challenge for the segmentation
processing module 204. In general there are four main approaches to
image segmentation in order to separate an image into distinct
objects that can be used, these are threshold, boundary,
region-based and hybrid approaches. Region-based techniques include
connected region analysis (CRA) and template region analysis (TRA)
can be used. Another technique, independent component analysis
(ICA) can be used. Also, an approach combining one or more of the
above techniques may be used.
[0027] The Z-analysis module 202 can perform a subsequent
Z-analysis at the object level on each of the ROIs returned by the
segmentation module 204. The object level Z-analysis results (or
effective Z values, Z.sub.eff) are then provided to the material
context analysis module 206 for context and non-penetration
analysis.
[0028] The material context analysis module 206 uses a-priori
knowledge of the contents of the container and/or typical false
alarms areas in order to analyze whether the ROIs received as input
are potential threats or merely false alarms. The a-priori
knowledge can be in the form of cargo manifests, expert system,
historical knowledge, or the like. The suspect threat ROIs are
provided as output from the material contest analysis module 206 to
the Z-analysis module 202. Optionally, false alarm areas or other
ROIs may be reported to the Z-analysis module 202 as well. The
Z-analysis module can then provide the ROIs and associated
confidence values to the threat decision arbitration module
208.
[0029] The threat decision arbitration module 208 can arbitrate
between threat hypotheses produced by one threat detection system,
or may arbitrate between results or hypotheses provided by multiple
threat detection system of the same or different configuration. The
threat decision arbitration module 208 uses expert-based rules to
determine an optimal decision (given the inputs and rules)
regarding the decision on potential threats and the confidences in
those decisions. For example, artificial intelligence research has
shown that arbitrating between the results of a plurality of
different solutions or result sets may provide improved decision
making ability for computerized systems under certain
circumstances. Thus, the threat decision arbitration module 208 can
accept input (e.g., potential threat ROIs and confidence values)
from the Z-analysis module 202 and, optionally, from other internal
or external systems or modules.
[0030] FIG. 3 is a block diagram of an exemplary nuclear material
detection system showing interfaces between four processing
modules. In particular, a Material Domain Imaging (MDI) module 302
has interfaces for receiving radiographic images 304 and also has
interfaces to an Object Segmentation Recognition (OSR) module 306,
a Material Context Analysis (MCA) module 312 and an Advanced
Cognitive Arbitration (ACA) module 318. The interface for receiving
the radiographic images 304 can include use of a proprietary or
standard format, such as ANSI N42. In addition to the radiographic
images, other data may be input to the MDI module 302, such as
threat thresholds.
[0031] The interface between the MDI module 302 and the OSR module
306 includes input parameters 308 and output parameters 310,
relative to the OSR module 306. The input parameters 308 include
registered images and assigned Z-values. The registered images can
be in gray scale and in an internal format. The output parameters
310 include region of interest (ROI) coordinates in an internal
format.
[0032] The interface between the MDI module 302 and the MCA module
312 includes input parameters 314 and output parameters 316,
relative to the MCA module 312. The input parameters 314 include
assigned z-values and ROI coordinates (and can also include the
hybrid images which contain the Zeff values and gray level data).
Another input to the MCA module is configuration data. The
configuration data can include container non-penetrable areas, a
cargo manifest, and/or other encoded knowledge, data, or
information that may be helpful in determining the context of ROIs.
The output parameters 316 include non-penetrable regions and
context suspicious regions. Optionally, false alarm regions may be
output as well. The regions may be output as a set of
coordinates.
[0033] The interface between the MDI module 302 and the ACA module
318 includes input parameters 320 and output results 322. The input
parameters 320 can include one or more threat, warning, or false
alarm ROIs and an associated confidence value for each. Another
input to the ACA module 318 is expert rules that are used to
determine an optimal output from the set of inputs received. The
output results 322 include a decision array containing threats,
warnings, and/or false alarms and associated confidence values for
each. The output results 322 can include images and data in the
ANSI N42 format. The output results can include a gray scale or
colorized z-value image (where the gray value or color is based on
the estimated atomic number determined by the MDI module 302),
threat, warning or false alarm ROIs and associated confidences for
each. Inputs to the ACA module 318 can also come from other
radiographic systems, thus allowing the ACA module 318 to arbitrate
between answers using data provided by various sources and/or
vendors.
[0034] FIG. 4 is a flowchart of an exemplary method for detecting
nuclear materials in a screened object. Processing begins at step
402 and continues to step 404.
[0035] In step 404, two or more radiographic images are received.
These images can be in the format described above. Also, the images
may be accompanied by other data, such as configuration parameters
(information relating to the security system, scanning system,
threat detection system or object being scanned) and/or a threat
threshold. Once the radiographic images are received, control
continues to step 406.
[0036] In step 406, a Z-analysis as described above is performed.
The Z-analysis results in a Z-value map or array of estimated
material atomic numbers that corresponds to two or more of the
radiographic images. Control continues to step 408.
[0037] In step 408, object segmentation is performed on the
radiographic images. As mentioned above, it may be desirable to
perform object segmentation prior to Z-analysis, in which case step
408 may be performed before step 406. Also, a second Z-analysis can
be performed after the object segmentation and step 406 could be
repeated after step 408 using the object regions of interest (ROIs)
identified by the segmentation process. Control continues to step
410. The segmentation process can use as few as one image; however
the Z-analysis may require at least two images using different
energies.
[0038] In step 410, the material context of any regions of interest
is analyzed to help identify both false alarm areas and potential
threat areas. Control continues to step 412.
[0039] In step 412, possible or potential threat ROIs are
arbitrated using a set of expert rules. Control continues to step
414.
[0040] In step 414, potential threats, warnings, and/or false alarm
ROIs are provided as output. Control continues to step 416 where
the method ends.
[0041] It will be appreciated that steps 404-414 may be repeated in
whole or in part in order to accomplish a contemplated nuclear
material detection task. Further, it should be appreciated that the
steps mentioned above may be performed on a single or distributed
processor. Also, the processes, modules, and sub-modules described
in the various figures of the embodiments above may be distributed
across multiple computers or systems or may be co-located in a
single processor or system.
[0042] The modules, processors or systems described above can be
implemented as a programmed general purpose computer, an electronic
device programmed with microcode, a hard-wired analog logic
circuit, software stored on a computer-readable medium or signal, a
programmed kiosk, an optical computing device, a GUI on a display,
a networked system of electronic and/or optical devices, a special
purpose computing device, an integrated circuit device, a
semiconductor chip, and a software module or object stored on a
computer-readable medium or signal, for example.
[0043] Embodiments of the method and system for nuclear material
detection (or their sub-components), may be implemented on a
general-purpose computer, a special-purpose computer, a programmed
microprocessor or microcontroller and peripheral integrated circuit
element, an ASIC or other integrated circuit, a digital signal
processor, a hardwired electronic or logic circuit such as a
discrete element circuit, a programmed logic circuit such as a PLD,
PLA, FPGA, PAL, or the like. In general, any process capable of
implementing the functions or steps described herein can be used to
implement embodiments of the method, system, or a computer program
product (software program) for nuclear material detection.
[0044] Furthermore, embodiments of the disclosed method, system,
and computer program product for nuclear material detection may be
readily implemented, fully or partially, in software using, for
example, object or object-oriented software development
environments that provide portable source code that can be used on
a variety of computer platforms. Alternatively, embodiments of the
disclosed method, system, and computer program product for nuclear
material detection can be implemented partially or fully in
hardware using, for example, standard logic circuits or a VLSI
design. Other hardware or software can be used to implement
embodiments depending on the speed and/or efficiency requirements
of the systems, the particular function, and/or particular software
or hardware system, microprocessor, or microcomputer being
utilized. Embodiments of the method, system, and computer program
product for nuclear material detection can be implemented in
hardware and/or software using any known or later developed systems
or structures, devices and/or software by those of ordinary skill
in the applicable art from the function description provided herein
and with a general basic knowledge of the computer, image
processing, radiographic, and/or threat detection arts.
[0045] Moreover, embodiments of the disclosed method, system, and
computer program product for nuclear material detection can be
implemented in software executed on a programmed general purpose
computer, a special purpose computer, a microprocessor, or the
like. Also, the method for nuclear material detection of this
invention can be implemented as a program embedded on a personal
computer such as a JAVA.RTM. or CGI script, as a resource residing
on a server or image processing workstation, as a routine embedded
in a dedicated processing system, or the like. The method and
system can also be implemented by physically incorporating the
method for nuclear material detection into a software and/or
hardware system, such as the hardware and software systems of
multi-energy radiographic cargo inspection systems.
[0046] It is, therefore, apparent that there is provided, in
accordance with the present invention, a method, computer system,
and computer software program for nuclear material detection. While
this invention has been described in conjunction with a number of
embodiments, it is evident that many alternatives, modifications
and variations would be or are apparent to those of ordinary skill
in the applicable arts. Accordingly, Applicant intends to embrace
all such alternatives, modifications, equivalents and variations
that are within the spirit and scope of this invention.
* * * * *