U.S. patent application number 12/129371 was filed with the patent office on 2009-02-26 for multi-energy radiographic system for estimating effective atomic number using multiple ratios.
Invention is credited to David M. Asner, David M. Brown, Peter Dugan, Robert L. Finch, Shawn Locke, John M. Munley, Christopher W. Peters, Kenei Suntarat.
Application Number | 20090052762 12/129371 |
Document ID | / |
Family ID | 40088192 |
Filed Date | 2009-02-26 |
United States Patent
Application |
20090052762 |
Kind Code |
A1 |
Dugan; Peter ; et
al. |
February 26, 2009 |
MULTI-ENERGY RADIOGRAPHIC SYSTEM FOR ESTIMATING EFFECTIVE ATOMIC
NUMBER USING MULTIPLE RATIOS
Abstract
A method of determining an effective atomic number, Z.sub.eff,
of an object may include obtaining a plurality of radiographic
images of the object. Each radiographic image can be obtained using
a different independent X-ray energy level. An intensity value for
each pixel in a region of interest in each radiographic image can
be determined. A plurality of measured ratios, R, can be formed
using attenuation coefficients from a pair of different
radiographic images. At least one adjusted measured ratio, R.sub.m,
can be calculated based on the plurality of measured ratios, R, and
at least one corresponding estimation coefficient, .alpha..
Z.sub.eff values can be assigned based on a comparison of the at
least one adjusted measured ratio, R.sub.m to a material
attenuation database.
Inventors: |
Dugan; Peter; (Ithaca,
NY) ; Finch; Robert L.; (Endicott, NY) ;
Munley; John M.; (Endwell, NY) ; Asner; David M.;
(Ithaca, NY) ; Peters; Christopher W.; (Cherry
Hill, NY) ; Suntarat; Kenei; (Funabashi-shi, JP)
; Brown; David M.; (Binghamton, NY) ; Locke;
Shawn; (Owego, NY) |
Correspondence
Address: |
MILES & STOCKBRIDGE PC
1751 PINNACLE DRIVE, SUITE 500
MCLEAN
VA
22102-3833
US
|
Family ID: |
40088192 |
Appl. No.: |
12/129371 |
Filed: |
May 29, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60940632 |
May 29, 2007 |
|
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Current U.S.
Class: |
382/132 |
Current CPC
Class: |
G06K 9/6263 20130101;
G01N 23/06 20130101 |
Class at
Publication: |
382/132 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A method of determining an effective atomic number, Z.sub.eff,
of an object, the method comprising the steps of: obtaining a
plurality of radiographic images of the object, each radiographic
image obtained using a different independent X-ray energy level and
each image including a plurality of pixels; registering the
plurality of radiographic images with each other; normalizing the
plurality of radiographic images; removing noise from the plurality
of radiographic images; determining an intensity value for each
pixel in a region of interest in each radiographic image; forming a
plurality of measured ratios for each pixel in the region of
interest, each ratio formed using intensity values from
corresponding pixels in a pair of different radiographic images;
calculating at least one adjusted measured ratio for each pixel in
the region of interest based on the plurality of measured ratios
and at least one corresponding estimation coefficient; comparing
the at least one adjusted measured ratio for each pixel in the
region of interest to a material attenuation database; assigning a
Z.sub.eff value to each pixel in the region of interest based on
the comparing; and outputting the assigned Z.sub.eff values.
2. The method of claim 1, wherein the step of obtaining a plurality
of radiographic images of the object comprises obtaining more than
two radiographic images of the object.
3. The method of claim 1, wherein said at least one adjusted
measured ratio includes a set of adjusted measured ratios and each
adjusted measured ratio of the set is based on a corresponding one
of the plurality of measured ratios and a corresponding one of a
plurality of estimation coefficients.
4. The method of claim 3, wherein comparing the at least one
adjusted measured ratio to a database includes: providing a
plurality of independent material assignment systems, each material
assignment system configured to determine a Z.sub.eff value based
on at least one different adjusted measured ratio from the set of
adjusted measured ratios and the material attenuation database;
generating a plurality of candidate Z.sub.eff values and associated
confidence values using the plurality of material assignment
systems; and selecting the Z.sub.eff value from the plurality of
candidate Z.sub.eff values with the highest associated confidence
value.
5. The method of claim 1, further comprising: mapping a color scale
to a range of Z.sub.eff values; and generating a color image of the
object using the color mapping and the assigned Z.sub.eff
values.
6. The method of claim 1, further comprising: comparing the
Z.sub.eff values to a threat threshold; and generating an output of
a threat region of interest and a confidence value for the threat
region of interest based on the comparison of the Z.sub.eff values
to the threat threshold.
7. The method of claim 1, wherein the material assignment database
comprises a densely populated attenuation ratio lookup table.
8. The method of claim 7, further comprising: receiving accepted
attenuation data of various materials, the accepted attenuation
data providing information regarding attenuation of X-rays at
various energy levels based on the corresponding Z.sub.eff values
of said materials; receiving configuration data including energy
scale resolution and Z.sub.eff resolution for said attenuation
lookup table; and generating said attenuation lookup table based on
said accepted attenuation data and said configuration data.
9. The method of claim 1, further comprising: determining said
region of interest through image processing and analysis of the
radiographic images.
10. A computer program product comprising: a computer readable
medium encoded with software instructions that, when executed by a
computer, cause the computer to perform the steps of: receiving
more than two radiographic images of an object, each image obtained
using a different independent X-ray energy level and each image
having a plurality of pixels; determining a normalized intensity
value for each pixel in each of the radiographic images of the
object; forming a plurality of measured ratios, each measured ratio
formed using the normalized intensity values from corresponding
pixels in a pair of different radiographic images; calculating at
least one adjusted measured ratio based on the plurality of
measured ratios and at least one corresponding estimation
coefficient; comparing the at least one adjusted measured ratio to
a material attenuation database; assigning a Z.sub.eff value based
on the comparing; and outputting the assigned Z.sub.eff value.
11. The computer program product of claim 10, wherein the
determining a normalized intensity value includes: registering the
radiographic images with each other; normalizing the radiographic
images; processing the radiographic images to remove noise; and
determining a plurality of intensity values, each of the plurality
of intensity values being determined for a different pixel of the
plurality of pixels in one of registered, normalized, and processed
radiographic images.
12. The computer program product of claim 10, wherein the comparing
the at least one adjusted measured ratio, includes: providing a
plurality of independent material assignment algorithms for
determining Z.sub.eff based on the at least one adjusted measured
ratio and the material attenuation database; generating a plurality
of candidate Z.sub.eff values and associated confidence values
using the plurality of independent material assignment algorithms;
and selecting the Z.sub.eff value from the plurality of candidate
Z.sub.eff values with the highest associated confidence value.
13. The computer program product of claim 10, further comprising
the steps of: mapping a color scale to a range of Z.sub.eff values;
generating a color image of the object using the color mapping and
the assigned Z.sub.eff values; comparing the Z.sub.eff values to a
threat threshold to determine a threat region of interest; and
generating an output of the color image, a threat region of
interest, and a confidence value for the threat region of interest
based on the comparison of the Z.sub.eff values to the threat
threshold.
14. The computer program product of claim 10, wherein the material
attenuation database comprises a densely populated attenuation
lookup table based on accepted attenuation data, the accepted
attenuation data providing information regarding attenuation of
X-rays at various energy levels based on Z.sub.eff values.
15. A system for determining an effective atomic number, Z.sub.eff,
of an object, the system comprising: at least one processor
configured to perform the steps of: forming a plurality of measured
ratios, each measured ratio formed using intensity values from a
pair of different radiographic images of an object; calculating at
least one adjusted measured ratio based on the plurality of
measured ratios and at least one corresponding estimation
coefficient; and outputting a Z.sub.eff value based on a comparison
of the at least one adjusted measured ratio to a material
attenuation database.
16. The system of claim 15, further comprising: an image processing
module configured to register, normalize, and remove noise from
each of the radiographic images.
17. The system of claim 15, further comprising: a plurality of
independent material assignment algorithms for determining a
plurality of Z.sub.eff values and corresponding confidence values,
each material assignment algorithm configured to compare the at
least one adjusted measured ratio to the material attenuation
database to determine a Z.sub.eff value and an associated
confidence value, wherein the at least one processor is configured
to assign a Z.sub.eff value from the plurality of determined
Z.sub.eff values based on the associated confidence value and to
generate an output at least based on the assigned Z.sub.eff
value.
18. The system of claim 15, further comprising: a color image
assignment module configured to map a color scale to a range of
Z.sub.eff values and to generate a color image of the object using
the color mapping and assigned Z.sub.eff values.
19. The system of claim 15, further comprising: a threat assignment
module configured to compare the Z.sub.eff values to a threat
threshold and to generate an output of at least one threat region
of interest and a corresponding confidence value for each threat
region of interest.
20. The system of claim 15, further comprising: a material
attenuation module configured to dynamically generate a densely
populated attenuation ratio lookup table based on accepted
attenuation data and configuration data.
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 hereby incorporated
by reference in its entirety.
[0002] The present invention relates generally to radiographic
imaging, and, more particularly, to determination of the effective
atomic number, Z.sub.eff, of a material using multi-energy
radiographic imaging.
[0003] Standard techniques using high-energy radiographic systems
exist to determine or estimate atomic composition of a material.
For example, two X-ray energy levels may be used to image an object
of interest. The gray level intensity values measured for each of
the two energy levels are used to compute a corresponding ratio.
The ratio of the intensity values for an unknown material is
compared against known materials. The known material with the
closest ratio to that measured is used to estimate the unknown
material's effective atomic number (Z.sub.eff) of the unknown
material. However, these radiographic systems may be prone to noise
and other non-linear effects that can cause errors in Z.sub.eff
determination, especially in high-Z materials. Elements with high-Z
include special nuclear materials, such as plutonium and highly
enriched uranium, as well as elements that would be extremely
effective in shielding special nuclear materials from passive
radiation detection techniques.
[0004] Using a radiographic system with only two X-ray energy
levels, two common problems can occur. First, insufficient
penetration may occur when high-Z or high density materials do not
allow enough energy to penetrate through the object of interest to
a detector. Second, low-Z or low density materials may induce
over-saturation. In the case of over-saturation, little to no
attenuation may occur at a particular X-ray energy level. To
overcome the problem of over-saturation, lower energies may be
used, whereas higher energies may be used in cases of insufficient
penetration. Higher energies may solve insufficient penetration
issues but exacerbate over-saturation issues and vice versa. Thus,
in dual energy systems, the solution for overcoming over-saturation
issues may be at odds with the solution for overcoming insufficient
penetration. Embodiments of the present invention may address the
above-mentioned problems and limitations, among other things.
[0005] An embodiment of the present invention can include a method
of determining an effective atomic number, Z.sub.eff, of an object
including obtaining a plurality of radiographic images of the
object. Each radiographic image can be obtained using a different
independent X-ray energy level. Each image can also include a
plurality of pixels. The images can be registered and normalized.
Noise may be removed from the images. An intensity value for each
pixel in the region of interest in each radiographic image can be
determined. A plurality of measured ratios, R, for each pixel in
the region of interest can be formed. Each measured ratio can be
formed using intensity values from corresponding pixels in a pair
of different radiographic images. The method can include
calculating at least one adjusted measured ratio, R.sub.m, for each
pixel in the region of interest based on the plurality of measured
ratios, R, and at least one corresponding estimation coefficient,
.alpha., and comparing the at least one adjusted measured ratio,
R.sub.m, for each pixel in the region of interest to a material
attenuation database. The method can include assigning a Z.sub.eff
value to each pixel in the region of interest based on the
comparison and outputting the assigned Z.sub.eff values.
[0006] Another embodiment may include a computer program product
including a computer readable medium encoded with software
instructions for causing a computer to perform the steps of
receiving more than two radiographic images of an object and
determining an intensity value for each pixel in each of the
plurality of radiographic images. Each image can be obtained using
a different independent X-ray energy level and can have a plurality
of pixels. The steps can also include forming a plurality of
measured ratios, R. Each measured ratio, R, can be formed using
intensity values from corresponding pixels in a pair of different
radiographic images. The steps can also include calculating at
least one adjusted measured ratio, R.sub.m, based on the plurality
of measured ratios, R, and at least one corresponding estimation
coefficient, .alpha., and comparing the at least one adjusted
measured ratio, R.sub.m, to a material attenuation database. The
steps can include assigning a Z.sub.eff value based on the
comparison and outputting the assigned Z.sub.eff value.
[0007] Another embodiment may include a system for determining an
effective atomic number, Z.sub.eff, of an object. The system may
include at least one processor. The at least one processor can form
a plurality of measured ratios, R. Each measured ratio may be
formed using intensity values taken from a pair of different
radiographic images. The at least one processor can calculate at
least one adjusted measured ratio, R.sub.m, based on the plurality
of measured ratios, R, and at least one corresponding estimation
coefficient, .alpha.. The at least one processor can output a
Z.sub.eff value based on a comparison of the at least one adjusted
measured ratio, R.sub.m, to a material attenuation database.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram showing an overview of an
embodiment of a system for estimating Z.sub.eff.
[0009] FIG. 2 is a flowchart showing a process overview of an
embodiment of a process for estimating Z.sub.eff.
[0010] FIG. 3 is a block diagram showing an embodiment of a dynamic
material attenuation processor for use in estimating Z.sub.eff.
[0011] FIG. 4 is a flowchart showing a process overview of an
embodiment of Z assignment arbitration for use in estimating
Z.sub.eff.
DETAILED DESCRIPTION
[0012] The present invention is directed to identifying material
compositions by estimating their effective atomic number or
Z.sub.eff value. The present invention further seeks to overcome
the limitations of the above-mentioned ratio method by using a
multi-energy approach. Multiple energies are used to determine
intensity ratios that are proportional to attenuation coefficient
ratios. These intensity ratios can then be used to build a series
of measured ratios. These measured ratios can then be used to build
a series of adjusted measured ratios for use in a Z.sub.eff
determination that may be more accurate than conventional
methods.
[0013] Imaging of a material relies on the application of Beer's
law. Beer's law equation can be stated using an arbitrary energy
level k as,
I(k)=I.sub.o(k)e.sup.-.mu..sup.k.sup.t, (1)
[0014] where I(k) is the measured intensity of the radiation at
energy level k, I.sub.O(k) is the input intensity of the radiation
at energy level k, .mu..sub.k is the linear attenuation coefficient
of the material at energy level k, and t is the energy independent
material thickness.
[0015] Experimentally, the linear attenuation coefficient depends
on the material cross-section coefficient, .sigma.. Since
attenuation is energy dependent, each material has the basic
regions of energy scatter, including photoelectric absorption
.sigma..sub.pe, coherent scatter .sigma..sub.cs, incoherent scatter
.sigma..sub.is, and pair-wise production .sigma..sub.pp. Each
material has a material combined cross-sectional coefficient which
depends on the sum of the individual cross-sections. For
example,
.sigma.=.sigma..sub.pe+.sigma..sub.cs+.sigma..sub.is+.sigma..sub.pp.
(2)
[0016] The cross-sections and linear attenuation coefficients are
related by:
.mu. = .sigma. N .rho. A , ( 3 ) ##EQU00001##
[0017] where .mu. is the material linear attenuation coefficient,
.sigma. is the material combined cross-sectional coefficient, N is
Avogadro's number, A is atomic weight, and .rho. is density.
[0018] Using any two energies, denoted as x and y, the following
intensity ratio may apply:
R j = ln ( I ( x ) I O ( x ) ) ln ( I ( y ) I O ( y ) ) = .mu. x t
.mu. y t = .mu. x .mu. y , ( 4 ) ##EQU00002##
[0019] where R.sub.j is the ratio of the logarithm of the
respective intensity ratios and x and y represent the upper and
lower energy levels. This expression eliminates the dependency on
mass thickness. Thus, the R.sub.j value may be compared against
attenuation curves at the upper and lower energy levels for various
materials to determine the Z.sub.eff of an unknown material.
[0020] Intensity values can be determined from measured
radiographic images and used in the determination of a plurality of
ratios for different pairs of radiographic source energy levels.
Any number of energy levels in the X-ray regime may be used. For
example, at least two energy levels and corresponding measured
ratios can be used. In another example, four different energy
levels and corresponding measured ratios can be used.
TABLE-US-00001 TABLE 1 Multi-Energy Attenuation Coefficient and
Ratio Table Source EnergyLevels(1 < 2 < 3 < 4)
CorrespondingAttenuationCoefficients(.mu..sub.x, .mu..sub.y)
EnergyRegime R j = ln ( I ( x ) I o ( x ) ) ln ( I ( y ) I o ( y )
) = .mu. x .mu. y ##EQU00003## E.sub.1, E.sub.2 .mu..sub.1,
.mu..sub.2 Low R.sub.i E.sub.1, E.sub.3 .mu..sub.1, .mu..sub.3 Low-
R.sub.ii Medium E.sub.1, E.sub.4 .mu..sub.1, .mu..sub.4 Low-High
R.sub.iii E.sub.2, E.sub.3 .mu..sub.2, .mu..sub.3 Medium R.sub.iv
E.sub.2, E.sub.4 .mu..sub.2, .mu..sub.4 Medium- R.sub.v High
E.sub.3, E.sub.4 .mu..sub.3, .mu..sub.4 High R.sub.vi
[0021] Table 1 shows an example of an application employing four
different source energy levels. Four different source energy levels
(E.sub.1-E.sub.4) are used to generate four different radiographic
images of an object. E.sub.1 corresponds to the lowest X-ray source
energy level. For example, E.sub.1 may be 1MeV. E.sub.4 corresponds
to the highest X-ray source energy level. For example, E.sub.4 may
be 10 MeV. Measured intensity values I(x) for each energy level, x,
are extracted from the corresponding radiographic images and used
in the determination of each measured ratio, R.sub.j. Source
intensity values I.sub.o(x) for each energy level are known from
measurement conditions of the radiographic images and are also used
in the determination of the measured ratios, R.sub.j. Although only
four source energy levels are discussed in detail with respect to
Table 1, it is of course contemplated that more than four or less
than four energy levels may be employed.
[0022] Ratios exist for various combinations of intensity values
based on the different source energy levels. Therefore, it may be
feasible that one could select between low, medium, or high energy
ratios for estimating the effective atomic number of the material
of the object to minimize errors due to saturation, noise, or
insufficient penetration.
[0023] A family of attenuation curves for a group of materials with
respect to X-ray energy is typically non-linear, with various
ranges of polynomial order. Although the attenuation curves
themselves are non-linear, the ratios from energy level to energy
level can be modeled using linear methods. At least two energy
solutions form a linear attenuation fit based on the ratio.
Determining the atomic structure of a material can be based on
comparing the slopes of the intensity values from radiographic
images with those values of known materials residing in a database.
The attenuation coefficient is proportional to measured X-ray
intensities.
[0024] Radiographic imagers can use measured X-ray intensity to
create a corresponding gray level picture, otherwise known as a
radiographic image. The conversion from intensity to gray level can
be defined by an inverse linear ratio, but the process of imaging
various materials poses several non-linear settings. Despite the
nonlinearity, ratios between the measured intensity values are
approximately the same as ratios between the true material linear
attenuation coefficients when adjusted for gray level conversion.
In other words, an adjusted ratio of measured intensity values, as
defined in equation (4), may be correlated with a similar ratio of
known linear attenuation coefficients in determining the Z.sub.eff
of an unknown material.
[0025] An adjusted measured ratio, R.sub.m,j, can be determined
from the equation:
R.sub.m,j=.alpha..sub.jR.sub.j
[0026] where R.sub.j is the measured ratio of attenuation
coefficients at two different source energy levels, .alpha..sub.j
is an estimation coefficient corresponding to the particular two
energy levels used in determining R.sub.j, and j is a subscript
referring to the particular pair of source energy levels. The
estimation coefficient, .alpha..sub.j, can be determined from the
mapping of the photon intensity values to a gray level value.
Separate adjusted measured ratios, R.sub.m, may be determined for
each measured ratio, R, used. Alternately, the multiple separate
measured ratios may be combined into a single vector. For example,
using the set of ratios defined by Table 1, equation (5) would
become:
R.sub.m=.alpha..sub.iR.sub.i .sub.i+.alpha..sub.iiR.sub.ii
.sub.ii=.alpha..sub.iiiR.sub.iii .sub.iii+.alpha..sub.ivR.sub.iv
.sub.iv+.alpha..sub.vR.sub.v .sub.v+.alpha..sub.ivR.sub.iv
.sub.iv
[0027] wherein .sub.j represents a vector in n-space corresponding
to attenuation coefficient R.sub.j. The n-space may be a
multi-dimensional space based on the number of energy levels
employed or the number of measured ratios formed.
[0028] While it is contemplated that all radiographic images
corresponding to different energy levels can be used in the
determination of the Z.sub.eff, it is also possible that only
specific measured ratios of intensity values may be selected
depending on the conditions associated with a particular energy
level. For example, for a given energy level, an intensity value
may not be applicable due to an over-saturation or non-penetration
condition. Therefore, the measured energy ratios, R, using the
intensity values for that particular energy level may be excluded
from the calculation of the adjusted measured ratio, R.sub.m,j.
Alternately, the ratio R.sub.m,j corresponding to those conditions
of over-saturation or non-penetration may be excluded from use in
the determination of Z.sub.eff.
[0029] Gray level mapping of the estimation coefficient is
nonlinear across material and energy levels. To compensate for the
nonlinearity, the estimation coefficients, .alpha., may be
determined empirically through experimental evaluation of known
materials. Alternately, an algorithm can be used for minimum error
tuning so as to optimize the estimation coefficients, .alpha.. It
should be appreciated that other methods may be used to determine
the estimation coefficients.
[0030] In view of the foregoing features described above,
structures and methodologies in accordance with various aspects of
the present invention will be better appreciated with reference to
FIGS. 1-5.
[0031] FIG. 1 is a block diagram showing an overview of an
exemplary embodiment of a system for estimating Z.sub.eff. A
material domain imaging processor 100 may receive multiple
radiographic images and other data as input and may output a
multi-energy high Z-mapping for identification of threats. An image
processor 108 may receive a set of radiographic images of an object
of interest from radiographic imaging system 102 or another
processor, system, or database. Each of the radiographic images may
be taken at a different X-ray energy level. Each image may be
processed by the image processor 108 to register the images,
normalize the images, and remove any noise in the images.
[0032] The processed images may be sent to the Z.sub.eff processor
116. The Z.sub.eff processor 116 may extract corresponding
intensity values from each pixel in each image. In an alternate
embodiment, the Z.sub.eff processor 116 may only determine an
intensity value for each pixel in each image that is within a
region of interest. For determination of a region of interest, the
images may be sent to an external analysis system, such as
segmentation processor 114. Segmentation processor 114 may use, for
example, edge boundary detection, texture based detection, or other
region processing in order to determine boundaries within the
images and to select a common region of interest for evaluation by
the material domain imaging processor 100. The region of interest
in each image may then be communicated to the Z.sub.eff processor
116 for determination of intensity values. The relevant pixels for
subsequent processing may be limited to those pixels within the
region of interest instead of the entirety of pixels in each image.
Relevant pixels may also be selected based on penetration
conditions (e.g., oversaturation, non-penetration). For example,
like regions across the various registered images may be compared
to determine penetration conditions.
[0033] After determination of the intensity values, the Z.sub.eff
processor 116 may use the intensities to form a set of measured
ratios, R.sub.j, for the relevant pixels. The measured ratios,
R.sub.j, may be formed using the relation set forth in equation (4)
above. Normalized intensity values from corresponding pixels in a
pair of different radiographic images may also be used to form each
measured ratio. Corresponding pixels may refer to pixels in
different images which correspond to the same point or location on
an imaged object. For example, the set of ratios for each pixel may
take the form shown in Table 1.
[0034] The Z.sub.eff processor 116 may use estimation coefficients,
.alpha..sub.j, with the set of measured ratios, R.sub.j, to
determine at least one adjusted measured ratio, R.sub.m,j, for each
relevant pixel. Separate adjusted measured ratios, R.sub.m,j, may
be determined for each energy ratio in the set or only for certain
energy ratios within the set. Alternately, the multiple energy
ratios may also be combined into a single measured ratio vector,
R.sub.m. The estimation coefficients may be provided by tuning
module 110.
[0035] The relevant pixels may include, for example, a single
pixel, all the pixels in the entire image, or just the pixels
within a region of interest. In an alternative embodiment, relevant
pixels may be selected based on penetration conditions (e.g.,
over-saturation, non-penetration, etc.). The material domain
imaging processor 100 or a separate system may compare regions
across various images to determine penetration conditions. The
material domain imaging processor 100 or a separate system (i.e.,
segmentation processor 114 or context analysis system 106) may
correlate regions with pixel values at a lower extreme of a photon
intensity scale as regions of non-penetration, while regions with
pixel values at an upper extreme of the photon intensity scale may
be correlated as regions of over-saturation. The Z.sub.eff
processor 116 may be configured to exclude these regions from the
list of relevant pixels for further processing.
[0036] In an exemplary embodiment, tuning module 110 can employ an
algorithm for minimum error tuning of the estimation coefficients.
Accepted attenuation database 104 may be provided to the tuning
module 110. Alternately, accepted attenuation data may be provided
by an integrated database, a memory device, or a separate system or
processor. The tuning module 110 may use ratios from radiographic
images of a known material to optimize the value of the estimation
coefficients such that adjusted measured ratios correspond to
ratios of accepted attenuation data. The accepted attenuation data
may be data from public attenuation sources, such as the NIST
public data source. In an alternate embodiment, the tuning module
110 may include a database of previously determined estimation
coefficients for use by the Z.sub.eff processor 116. The estimation
coefficients may be determined via experimental evaluation or other
means.
[0037] The resulting adjusted measured ratios, R.sub.m,j, can be
output from the Z.sub.eff processor 116 to the material assignment
module 118. The material assignment module 118 can compare the
adjusted measured ratios to a material attenuation database from
dynamic material attenuation (DMA) module 112. Alternately, the
Z.sub.eff processor 116 may send the measured ratios, R.sub.j, to
the material assignment module 118. In such a scenario, material
assignment module 118 would use the estimation coefficients and the
measured ratios to form the adjusted measured ratios, R.sub.m,j,
for comparison to the material attenuation database. In either
embodiment, the material assignment module 118 may assign a
Z.sub.eff value to each relevant pixel or to a region of pixels
based on the comparison.
[0038] In an exemplary embodiment, the DMA module 112 may be a
processor, such as dynamic material attenuation processor 300, that
uses configurable settings to dynamically create a densely
populated attenuation ratio lookup table with variable resolution
in both the energy scale and the effective atomic number
(Z.sub.eff) scale. The DMA module 112 can be used for the two or
more energies used in the radiographic images for determining
Z.sub.eff values. Sparse attenuation data from public sources for
any material (liquid, solid, gas) can be stored on a disk using a
standard format. Alternately, accepted attenuation data (i.e.,
attenuation coefficients) may be input to the DMA module 112 from
accepted attenuation database 104. Accepted attenuation data may
also be provided by an integrated database, a memory device, or a
separate system or processor. The accepted attenuation data may be
data from public attenuation sources, such as the NIST public data
source. The attenuation data can be created using a variety of
scattering approaches, such as coherent, incoherent, photoelectric,
pairwise production nuclear field, pairwise production electric
field, total scatter with coherent, and total scatter without
coherent. A user may select the materials used to create the
attenuation lookup table, such as water, peroxide, lead, carbon,
etc. Using the attenuation data, the attenuation lookup table may
be created as a function of the X-ray energy level and the
Z.sub.eff of the material. Alternately, the DMA module 112 may
include a database of previously determined attenuation lookup
tables, such as material attenuation table 322 in FIG. 3, for use
by the material assignment module 118.
[0039] Material assignment module 118 may include a plurality of
independent material assignment algorithms. Each material
assignment algorithm may employ a different methodology or use
different measured ratios for comparison to the same attenuation
database to generate a set of candidate Z.sub.eff values for each
relevant pixel. Each material assignment algorithm can also assign
a confidence value to the candidate Z.sub.eff values.
[0040] The set of candidate Z.sub.eff values may be sent to the
Z.sub.eff processor 116. The Z.sub.eff processor 116 may compare
the confidence values for each Z.sub.eff value in the set and may
select the Z.sub.eff value for each relevant pixel with the highest
associated confidence value. The result can be a Z.sub.eff image
with each pixel having an assigned Z.sub.eff value with the highest
confidence. In an exemplary embodiment, the Z.sub.eff processor 116
may output the result to external analysis system, such as context
analysis system 106, for evaluation of regions of non-penetration
or over-saturation for inclusion in the final processor output. For
example, context analysis system 106 may use a-priori information,
in the way of configuration data, to assist in identifying
non-penetrable and false alarm cases.
[0041] The result from the Z.sub.eff processor 116 can be sent to a
color image assignment module 120. The color image assignment
module 120 can map a color scale to a range of corresponding
Z.sub.eff values. Thus, each relevant pixel may be assigned a color
based on the assigned Z.sub.eff value, thereby creating a color
image of the object. This color image can be output from the
material domain imaging processor 100 to threat decision processor
124. In addition, the color image and Z.sub.eff values may be
further processed by a threat assignment module 122. The Z.sub.eff
value for each pixel can be compared with a threat threshold by the
threat assignment module 122 to determine regions where the
threshold is exceeded, i.e., those regions where a threat exists.
These regions and associated confidence values may be output from
the material domain imaging processor 100 to the threat decision
processor 124 for further processing or integrated decision
making.
[0042] FIG. 2 shows a process flow 200 of an exemplary embodiment
of a method of material domain image processing. The process begins
at step 201 and proceeds to step 202. In step 202, radiographic
images may be obtained of an object. Each radiographic image of an
object may be obtained at a different X-ray energy level. For
example, at least two independent X-ray energy levels can be used.
In another example, four X-ray energy levels can be used to
generate four independent radiographic images of an object. In step
204, the radiographic images may be subject to image processing.
The image processing may be any of a number of processing steps
known in the art. For example, each radiographic image may be
registered with the other radiographic images such that regions of
interest in the object are aligned. Further, the radiographic
images may be normalized to a gray scale. In addition, noise
filtering may be employed to reduce noise artifacts in the
image.
[0043] In step 206, an intensity value may be determined for each
relevant pixel in each processed image. The relevant pixels may
include a single pixel, pixels within a designated region of
interest, or all pixels within each image. At step 208, the
intensity values may be used to create a set of measured ratios,
R.sub.j, for each relevant pixel based on the different images,
with each image corresponding to a different X-ray energy level.
For example, the set of measured ratios, R.sub.j, for each pixel
may be formed as shown in Table 1. At step 210, at least one
adjusted measured ratio, R.sub.m,j, for each relevant pixel can be
determined. The measured ratio can be based on the product of at
least one estimation coefficient, .alpha..sub.j, and the set of
measured ratios, R.sub.j, from step 208. Separate adjusted measured
ratios, R.sub.m,j, may be determined for each measured ratio,
R.sub.j, in the set or only for certain energy ratios within the
set.
[0044] The resulting set of adjusted measured ratios, R.sub.m,j,
may be compared to a material attenuation database in step 212
using at least one algorithm. The material attenuation database can
be a densely populated attenuation ratio lookup table based on data
from public sources of attenuation data. For example, this
comparison may be a determination of an error, .epsilon., for each
measured ratio with respect to the corresponding accepted
attenuation ratio, as given by:
.epsilon.=|R.sub.m,j-R.sub.T,j|=|(.alpha..sub.j*R.sub.j)-R.sub.T,j|
(7)
[0045] where .alpha..sub.j is the estimation coefficient
corresponding to the measured ratio, R.sub.j, and R.sub.T,j is an
accepted attenuation ratio derived from the densely populated
attenuation ratio lookup table.
[0046] Results of the comparison in step 212 can be used in step
214 to determine the Z.sub.eff for each relevant pixel. In an
exemplary embodiment, step 212 can provide a comparison using a set
of algorithms. Each algorithm may employ a different methodology,
different adjusted measured ratios, and/or different estimation
coefficients for comparison to the same database. The results of
this comparison may be used to generate a set of candidate
Z.sub.eff values and associated confidence values for each relevant
pixel in step 214. An arbitration step may be included in step 214
to select the Z.sub.eff value that has the highest associated
confidence value for the given measurement conditions.
[0047] In steps 216 and 218, which are optional, the Z.sub.eff
value for each relevant pixel may be used in the construction of a
color image. In step 216, colors may be assigned to correspond with
different Z.sub.eff values. For example, the assignment of colors
may be predetermined or set dynamically to correspond with the
range of measured Z.sub.eff values. In step 218, colors may be
assigned to each pixel according to the Z.sub.eff value of the
pixel so as to generate a Z.sub.eff color image of the object. The
method can end at step 220.
[0048] FIG. 3 illustrates a dynamic material attenuation processor
300, which may be used as DMA module 112 in the embodiment of FIG.
1. The dynamic material attenuation processor 300 may be used to
dynamically create a densely populated attenuation ratio lookup
table for use by the material assignment module 118 in the
determination of Z.sub.eff values. The processor 300 can use
configurable settings to create, for example, a table of accepted
attenuation coefficient ratios, R.sub.T,j, with variable resolution
in both the energy scale and Z.sub.eff. Similar to equation (4)
above, the accepted attenuation coefficient ratio, R.sub.T,j, can
be expressed as:
R T , j = .mu. x .mu. y . ( 8 ) ##EQU00004##
[0049] Inputs into the dynamic material attenuation processor 300
may be derived from public attenuation data source 302, such as
periodic attenuations, liquid attenuations, or other forms
available through the NIST public data source, for example. The
public attenuation data source 302 may include a sparsely populated
database. The data from the public attenuation data source 302 may
provide attenuation data of various material compositions at
different radiographic energy levels under a variety of scattering
conditions, including, but not limited to, coherent, incoherent,
photoelectric, pair-wise production nuclear field, pair-wise
production electric field, total coherent, and total non-coherent.
Using this data, the processor 300 may build a fully connected
table of Z.sub.eff values versus ratio of accepted attenuation
coefficients at different energy levels. For example, the table may
have rows with different Z.sub.eff values. Each column in the table
may have an accepted attenuation coefficient ratio, R.sub.T,j, that
corresponds with a different pair of source energy levels used in
the radiographic images by Z.sub.eff processor 116.
[0050] The densely populated attenuation ratio lookup table may be
dynamically configurable based on user inputs or configuration
data. For example, the dynamic material attenuation processor 300
may receive a subsystem request 304. This request 304 may include,
for example, a particular source energy range and a particular
Z.sub.eff range. This information may also be provided by
configuration data. Configuration data can control the energy
density needed for the resultant attenuation data table as well as
the Z.sub.eff density needed. At 306, the requested energy values
and energy resolution may be obtained from the configuration data.
At 312, the requested Z.sub.eff values and Z.sub.eff resolution may
be obtained from the configuration data. Although shown in FIG. 3
as being external to processor 300, the configuration data may be
integrated and stored with the processor 300. The configuration
data may be predetermined based on the radiographic system or
object under test. Alternately, the configuration data may be input
by a user.
[0051] At 308, the dynamic material attenuation processor 300 can
receive the request 304, the energy configuration data 306, and/or
the Z.sub.eff configuration data 312 and may obtain the necessary
attenuation data from public attenuation data source 302. At 314,
the attenuation data may be normalized for both energy and
Z.sub.eff values. The resultant attenuation data may then be formed
into accepted attenuation coefficient ratios, R.sub.T,j, and
organized into tabular form at 324. Publicly available attenuation
data source 302 may lack some of the Z.sub.eff values or the source
energy values requested in subsystem request 304. Accordingly, the
dynamic material attenuation processor 300 may create a
high-resolution table so as to account for this missing
information. At 326, the tabular form of accepted attenuation
coefficient ratios, R.sub.T,j, from 324 may be expanded based on
the subsystem request 304, the energy configuration data 306,
and/or the Z.sub.eff configuration data 312. Any data missing in
the table generated by 326 may be interpolated using a polynomial
spline fit. Poly-spline fit 310 may interface with the
high-resolution table created in 326 to interpolate missing
information with respect to source energy values. Poly-spline fit
316 may interface with the high-resolution table created in 326 to
interpolated missing information with respect to Z.sub.eff values
(i.e., material composition). The result can be an attenuation
ratio lookup table based on source energy values and Z.sub.eff
values. Thus, sparse input data and configuration information can
be used to create a densely populated configurable attenuation
ratio lookup table.
[0052] A color map to the Z.sub.eff values may also be created to
coincide with the Z.sub.eff value settings. The attenuation ratio
lookup table may be output to 318 for assigning colors to the
various Z.sub.eff values. The resultant attenuation ratio lookup
table and the assigned colors may then be output to 320 for
formatting to a given data standard. Alternately, the attenuation
ratio lookup table may be sent directly from 326 to 320 for
formatting to the standard. The format may be any available data
standard. For example, the standard can be an N42 standard, such as
ANSI N42.42. After formatting, the attenuation ratio table may be
output as a material attenuation ratio table 322 for use by
material assignment module 118.
[0053] FIG. 4 illustrates a Z algorithm arbitration process 400 for
use in estimating Z.sub.eff. The process starts at step 401 and
continues to step 402. In step 402, a pixel may be selected in a
region of interest for which at least one adjusted measured ratio,
R.sub.m, has been determined. At step 404, an algorithm may be
selected from a set of algorithms. Using the selected algorithm,
the at least one adjusted measured ratio, R.sub.m, may be compared
to a densely-populated attenuation lookup table in step 406. Based
on the comparison, the selected algorithm can determine a Z.sub.eff
value for the relevant pixel at step 408. At step 410, the selected
algorithm may also determine a confidence value for the Z.sub.eff
value. This confidence value may be based on measurement
conditions. Alternately, the confidence value may be based on a
particular set of estimation coefficients employed and the
resulting range of Z.sub.eff determined. At step 412, the
arbitration process can check to see if all algorithms within the
set have been evaluated. The process can repeat steps 404-410 until
all algorithms have been evaluated. Once all algorithms have been
evaluated, thereby generating a set of candidate Z.sub.eff values
with associated confidence values, the process may proceed to step
414. At step 414, the Z.sub.eff value with the highest confidence
value can be selected and assigned to the relevant pixel. At step
416, the arbitration process can check to see if all relevant
pixels have been evaluated. The process can repeat steps 402-414
until all relevant pixels have been evaluated. If all pixels have
been evaluated, the process may proceed to step 418, wherein the
Z.sub.eff values and associated confidence values for each relevant
pixel may be output for further processing. For example, the
Z.sub.eff values may be output to color image assignment module 120
for subsequent color imaging of the Z.sub.eff values. The process
may continue to step 420 where the process may end.
[0054] It should be appreciated that the steps of the present
invention may be repeated in whole or in part in order to perform
the contemplated Z.sub.eff estimation. Further, it should be
appreciated that the steps mentioned above may be performed on a
single or distributed processor. Also, the processes, modules, and
units 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.
[0055] Embodiments of the method, system, and computer program
product for determining an effective atomic number, Z.sub.eff, of
an object, 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 computer program product for
determining an effective atomic number, Z.sub.eff, of an
object.
[0056] Furthermore, embodiments of the disclosed method, system,
and computer program product for determining an effective atomic
number, Z.sub.eff, of an object 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 determining an effective
atomic number, Z.sub.eff, of an object 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 determining an effective atomic number, Z.sub.eff, of
an object 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, radiographic, and image processing
arts.
[0057] Moreover, embodiments of the disclosed method, system, and
computer program product for determining an effective atomic
number, Z.sub.eff, of an object can be implemented in software
executed on a programmed general purpose computer, a special
purpose computer, a microprocessor, or the like. Also, the
Z.sub.eff determination method 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 determining Z.sub.eff of
an object into a software and/or hardware system, such as the
hardware and software systems of multi-energy X-ray inspection
systems.
[0058] It is, therefore, apparent that there is provided, in
accordance with the present invention, a method, system, and
computer program product for determining Z.sub.eff of an object.
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, Applicants
intend to embrace all such alternatives, modifications, equivalents
and variations that are within the spirit and scope of this
invention.
* * * * *