Systems and Methods for Detecting Contraband

Abstract

A method for detecting contraband is provided. The method includes acquiring tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography, generating a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data, determining a differentiation parameter for a tissue material at each of the plurality of frequencies, determining a differentiation parameter for a non-tissue material at each of the plurality of frequencies, decomposing the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material, and determining whether the non-tissue image contains any non-tissue material.

Description

BACKGROUND OF THE INVENTION

The embodiments described herein relate generally to tomographic imaging systems and, more particularly, to detecting objects using tomographic imaging systems.

In restricted areas such as airports and correctional facilities, detecting contraband in and/or on individuals is a high priority. Contraband such as drugs, keys, and plastic weapons may be hidden within body cavities of an individual, or on the individual (e.g., hidden under the individual's clothing). While some contraband may be detected by manually frisking passengers, privacy concerns make such methods problematic.

At least some known security scanners are capable of detecting metallic objects within body cavities and/or on an individual. However, at least some known security scanners are unable to detect non-metallic objects within body cavities and/or on an individual. While some medical imaging methods, such as X-ray computed tomography (CT) and magnetic resonance imaging (MRI), may be used to detect non-metallic objects, these imaging methods are typically quite expensive, and may involve exposing subjects to significant levels of radiation.

Low frequency electromagnetic tomography provides a safe and low cost method for imaging. Such imaging methods include electrical impedance tomography (EIT), magnetic induction tomography (MIT) and electric field tomography (EFT). However, low frequency electromagnetic tomography generally provides lower resolution and/or image quality when compared to X-ray CT and MRI. While multiple frequency electromagnetic tomography has been used to improve imaging quality, reduce artifacts, and detect abnormalities in tissue for diagnostic applications of mammography and hemorrhage detection, the low quality image resolution often limits the efficacy of such methods for detecting contraband.

BRIEF SUMMARY OF THE INVENTION

In one aspect, a method for detecting contraband is provided. The method includes acquiring tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography, generating a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data, determining a differentiation parameter for a tissue material at each of the plurality of frequencies, determining a differentiation parameter for a non-tissue material at each of the plurality of frequencies, decomposing the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material, and determining whether the non-tissue image contains any non-tissue material.

In another aspect, a security scanner configured to detect contraband is provided. The security scanner includes a detector array configured to acquire tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography, and a processing device coupled to the detector array. The processing device is configured to generate a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data, determine a differentiation parameter for a tissue material at each of the plurality of frequencies, determine a differentiation parameter for a non-tissue material at each of the plurality of frequencies, decompose the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material, and determine whether the non-tissue image contains any non-tissue material.

In yet another aspect one or more computer-readable storage media having computer-executable instructions embodied thereon for scanning a subject for contraband are provided. When executed by at least one processor, the computer-executable instructions cause the at least one processor to instruct a detector array to acquire tomographic image data of the subject at a plurality of frequencies using low frequency electromagnetic tomography, generate a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data, determine a differentiation parameter for a tissue material at each of the plurality of frequencies, determine a differentiation parameter for a non-tissue material at each of the plurality of frequencies, decompose the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material, and determine whether the non-tissue image contains any non-tissue material.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an exemplary security scanner.

FIG. 2 is a schematic diagram of an imaging system that may be used with the security scanner shown in FIG. 1.

FIG. 3( a) is a schematic diagram of a detector array.

FIG. 3( b) is a composite image of the detector array shown in FIG. 3(a).

FIGS. 4( a)-4(c) are schematic diagrams of a detector array.

FIGS. 5( a)-5(c) are calibration graphs for the detector arrays shown in FIGS. 4(a)-4(c).

FIGS. 6( a)-6(c) are discrete images of an object acquired using the imaging system shown in FIG. 2.

FIGS. 7( a)-7(c) are discrete images of an object acquired using the imaging system shown in FIG. 2.

FIG. 8 is a flowchart of an exemplary method that may be used with imaging system shown in FIG. 2.

FIG. 9 is a flowchart of an exemplary method for detecting contraband that may be used with the imaging system shown in FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments described herein include an imaging system that can be used to detect contraband located in or near an individual's body. For example, embodiments of the imaging system can detect contraband concealed in an individual's abdominal, pelvic and/or groin area, such as between the passenger's legs or inside a body cavity. As used herein, the term “contraband” refers to illegal substances, explosives, narcotics, weapons, a threat object, and/or any other material that a person is not allowed to possess in a restricted area, such as an airport or a correctional facility.

In a particular embodiment, the imaging system acquires tomographic image data of an object at a plurality of frequencies and generates a composite image of the object at each of the frequencies. The imaging system further determines a scaling factor for a first material at each of the frequencies and a scaling factor for a second material at the frequencies. The imaging system decomposes the composite images into a first discrete image and a second discrete image using the scaling factors. From the discrete images, it can be determined whether contraband is located in or near the object.

Although an electric field tomography (EFT) system is described herein, it should be understood that the embodiments described herein can be used with any suitable imaging system, such as a magnetic induction tomography (MIT) system and/or an electrical impedance tomography (EIT) system. That is, the systems and methods described herein may be implemented using various types of low frequency electromagnetic tomography. As used herein, low frequency electromagnetic tomography includes electromagnetic tomography techniques operating at frequencies less than or equal to 500 megahertz (MHz), and may include EFT, MIT, and EIT.

Further, although the methods and systems described herein are demonstrated using images reconstructed from finite element modeling (FEM) simulation data, experimental data would yield substantially similar results. FIG. 1 is a perspective view of an exemplary security scanner 100. Security scanner 100 includes a platform 102 and an imaging system 104. An object 106 to be scanned is positioned within imaging system 104. In the exemplary embodiment, object 106 is a human subject. Alternatively, object 106 may be any article and/or entity which are to be scanned for contraband. Security scanner 100 scans object 106 to detect contraband, as described in detail below.

FIG. 2 is a schematic diagram of an imaging system 200 that may be used with security scanner 100 (shown in FIG. 1). In the exemplary embodiment, imaging system 200 is an EFT system. Alternatively, imaging system 200 may be any imaging system that enables security scanner 100 to function as described herein. For example, imaging system 200 may include an MIT and/or EIT system.

In the exemplary embodiment, imaging system 200 includes a detector array 202, a processing device 204, and a display device 206. Processing device 204 is coupled to detector array 202 and acquires and processes image data utilizing detector array 202, as described in detail below. Display device 206 is coupled to processing device 204 and displays processed image data. Display device 206, may include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), an organic light emitting diode (OLED) display, and/or an “electronic ink” display.

In the exemplary embodiment, detector array 202 forms a closed ring. Alternatively, detector array 202 may have any shape that enables detector array 202 to function as described herein. Detector array 202 includes a plurality of electrodes 230. In the exemplary embodiment, detector array 202 includes seventeen electrodes 230. Alternatively, detector array 202 may include any number of electrodes 230 that enables detector array 202 to function as described herein. Detector array 202 acquires image data of object 106, as described in detail below.

Each of electrodes 230 is capable of functioning as both an emitting electrode 232 and a detecting electrode 234. During operation of detection array 202, one electrode 230 functions as emitting electrode 232, and the remaining electrodes 230 function as detecting electrodes 234. To acquire image data, emitting electrode 232 emits an electric field at a frequency, v. To generate the electric field, emitting electrode 232 may be coupled to, for example, an alternating voltage source (not shown). The electric field is emitted along a plurality of projection lines 236, and at least some of projection lines 236 pass through object 106. For clarity, a limited number of projection lines 236 are illustrated in FIG. 2. However, those of ordinary skill in the art will understand that the electric field is emitted from emitting electrode 232 along an infinite number of projection lines 236.

As the electric field passes through object 106 along projection lines 236, the electric field undergoes a phase shift, Δ. The magnitude of the phase shift Δ depends on the electrical properties of the material composing object 106, such as the conductivity and electrical permittivity. Accordingly, by actively detecting perturbations (e.g., the phase shift Δ) between the emitted electric field and the detected electric field, one or more materials in object 106 may be detected and/or identified, as described in detail herein.

In the exemplary embodiment, detecting electrodes 234 measure the phase shift Δ of the electric field. To measure the phase shift Δ, detecting electrodes 234 may be coupled to, for example, a phase sensitive voltmeter (not shown). Phase shift data including the detected phase shift Δ at each detecting electrode is transmitted to and stored at processing device 204. This process is repeated until each electrode 230 functions as emitting electrode 232.

After phase shift data has been transmitted to processing device 204 with each electrode functioning as emitting electrode 232, processing device 204 uses the phase shift data to reconstruct a composite image of object 106 at frequency v, M_v. In the exemplary embodiment, processing device 204 uses a filtered back-projection algorithm to reconstruct composite image M_v. Alternatively, processing device 204 may use any suitable image-reconstruction method to reconstruct composite image M_v.

Processing device 204 may be implemented to control, manage, operate, and/or monitor the various components associated with imaging system 200. In the exemplary embodiment, processing device 204 includes a graphical user interface 240, processor 242, and memory 244. Alternatively, processing device 204 may be implemented using any suitable computational device that provides the necessary control, monitoring, and data analysis of the various systems and components associated with imaging system 200.

In general, processing device 204 may be a specific or general purpose computer operating on any known and available operating system and operating on any device including, but not limited to, personal computers, laptops and/or hand-held computers. Graphical user interface 240 may be any suitable display device operable with any of the computing devices described herein and may include a display, for example, a CRT, a LCD, an OLED display, and/or an “electronic ink” display. In one embodiment, display device 206 serves as the display for graphical user interface 240.

A communication link between processing device 204 and detector array 202 may be implemented using any suitable technique that supports the transfer of data and necessary signaling for operational control of the various components of detector array 202. The communication link may be implemented using conventional communication technologies such as micro transport protocol, Ethernet, wireless, coaxial cables, serial or parallel cables, and/or optical fibers, among others. In some embodiments, processing device 204 is physically configured in close physical proximity to detector array 202. Alternatively, processing device 204 may be remotely implemented if desired. Remote implementations may be accomplished by configuring processing device 204 and detector array 202 with a suitably secure network link that includes a dedicated connection, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), and/or the Internet, for example.

The various methods and processes described herein may be implemented in a computer-readable medium using, for example, computer software, hardware, or some combination thereof. For a hardware implementation, the embodiments described herein may be performed by processor 242, which may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a selective combination thereof. For a software implementation, the embodiments described herein may be implemented with separate software modules, such as procedures, functions, and the like, each of which perform one or more of the functions and operations described herein. The software codes can be implemented with a software application written in any suitable programming language and may be stored in a memory unit, for example, memory 244, and executed by a processor, for example, processor 242. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor using known communication techniques. Memory 244 shown in FIG. 2 may be implemented using any type (or combination) of suitable volatile and nonvolatile memory or storage devices including random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk, or other similar or effective memory or data storage device.

In the exemplary embodiment, object 106 is composed of a muscle component 250, a bone component 252, and a plastic component 254. Muscle and bone are two exemplary tissue materials, and plastic is an exemplary non-tissue material. Alternatively, object 106 may be composed of any tissue and/or non-tissue material such as, for example, a crystalline material, a biological material, a non-metallic material, a metallic material, and/or a ceramic material. In an embodiment where object 106 is a human subject, muscle component 250 and bone component 252 typically correspond to anatomical structures of the human subject. However, the presence of plastic component 254 in a human subject may indicate the presence of a foreign object and/or contraband.

Notably, the electrical properties of tissue and/or tissue-like materials, such as muscle and bone, are significantly different from the electrical properties of non-tissue materials, such as plastic. Given this difference in electrical properties, using the methods and systems described herein, components of an object composed of a tissue-like material can be differentiated from components of an object composed of a non-tissue material. Accordingly, while in the exemplary embodiment, imaging system 200 detects plastic component 254 by differentiating plastic component 254 from muscle component 250 and bone component 252, as described in detail below, imaging system 200 may be used differentiate a wide range of non-tissue materials from tissue-like materials.

In the exemplary embodiment, imaging system 200 uses scaling factors to decompose a composite image into discrete tissue and non-tissue images, as described in detail below. However, the methods and systems described herein are not limited to using scaling factors to perform the decomposition. Instead, any parameter that is sensitive to the different electrical properties between a tissue material and a non-tissue material may be used to separate a composite image into discrete images of different materials. These parameters are referred to herein as differentiation parameters, and the scaling factors described herein are merely one example of a differentiation parameter. Accordingly, while scaling factors are utilized in the exemplary embodiment, the systems and methods described herein may be implemented using any suitable differentiation parameter.

When object 106 is composed of several different materials, for example muscle component 250, bone component 252, and plastic component 254, composite image M_v contains image data for all of the different materials. However, when using an imaging system utilizing a relatively low resolution imaging technique, such as EFT, individual materials may not be distinguishable from one another in the composite image H_v.

For example, FIG. 3(a) is a schematic diagram of a detector array 300. FIG. 3(b) is a composite image M_{5 MHz}, constructed from finite element modeling (FEM) data, of muscle component 250, bone component 252, and plastic component 254 in detector array 300 at an electric field frequency of 5 Megahertz (MHz). The components 250, 252 and 254 have relative locations and dimensions in object 106 as shown in FIG. 3(a). As demonstrated by FIG. 3(b), muscle component 250, bone component 252, and plastic component 254 are not distinguishable from one another in composite image M_{5 HHz}. Accordingly, when generating a composite image M_v at only a single frequency v, given the relatively low resolution of imaging system 200, it cannot easily be determined whether object 106 includes plastic component 254, and accordingly, whether contraband is present on and/or within object 106.

To determine whether object 106 includes plastic component 254, image data is acquired at a plurality of frequencies. More specifically, image data is acquired at j different frequencies v₁, v₂, . . . v_j. From the acquired image data, corresponding composite images M_v1, M_v2, . . . M_vj generated using processing device 204. In the exemplary embodiment, frequencies v₁, v₂, . . . v_j are within a range of 1 megahertz (MHz) to 20 MHz. Alternatively, frequencies v₁, v₂, . . . v_j may span any range of frequencies that enables imaging system 200 to function as described herein.

In the exemplary embodiment, composite images M_v1, M_v2, . . . M_vj are decomposed into a discrete plastic image I_P, a discrete muscle image I_M, and a discrete bone image I_B. Discrete plastic image I_P contains any regions of object 106 composed of plastic component 254, discrete muscle image I_M contains any regions of object 106 composed of muscle component 250, and discrete bone image I_B contains any regions of object 106 composed of bone component 252. Alternatively, composite images M_v1, M_v2, . . . M_vj may be decomposed into any number of discrete images corresponding to an identical number of components.

Using a linear least square approximation, a composite image M_v at a given frequency v can be modeled using Equation (1):

αI_P+βI_M+γI_B=M_v   (1)

where α, β, and γ are constants. While in the exemplary embodiment, a linear least square approximation is used, any approximation method that enables imaging system 200 to function as described herein may be used. Further, while in the exemplary embodiment, composite image M_v is modeled as having three components, I_P, I_M, and I_B, composite image H_v may be modeled as being composed of any number of components that enables system 200 to function as described herein.

Across a plurality of frequencies v₁, v₂, . . . v_j, the detected phase shifts Δ for muscle component 250 and bone component 252 generally have much greater variation than the detected phase shift Δ of plastic component 254, due to the conductive properties of muscle and bone, as compared to the conductive properties of plastic. More specifically, the difference between a detected phase shift of muscle component 250 at a first frequency and a detected phase shift of muscle component 250 at a second frequency, |Δ_v1^M−Δ_v2^M|, and the difference between a detected phase shift of bone component 252 at the first frequency and a detected phase shift of bone component 252 at the second frequency, |Δ_v1^B−Δ_v2^B|, are both appreciably greater than the difference between a detected phase shift of plastic component 254 at the first frequency and a detected phase shift of plastic component 254 at the second frequency, |Δ_v1^P−Δ_v2^P|.

Accordingly, in Equation (1), α is set equal to a plastic image scaling factor C_v^P, β is set equal to a muscle image scaling factor C_v^M, and γ is set equal to a bone image scaling factor C_v^B. Thus, at frequencies v₁, v₂, . . . v_j, composite images M_v1, M_v2, . . . M_vj can be represented as Equation (2):

$\begin{array}{cc}\left[\begin{array}{ccc}{C}_{v_1}^P& C_{v_1}^M& C_{v_1}^B\\ C_{v_2}^P& C_{v_2}^M& C_{v_2}^B\\ ⋮& ⋮& ⋮\\ C_{v_j}^P& C_{v_j}^M& C_{v_j}^B\end{array}\right]\left[\begin{array}{c}{I}_P\\ I_M\\ I_B\end{array}\right]=\left[\begin{array}{c}{M}_{v_1}\\ M_{v_2}\\ ⋮\\ M_{v_j}\end{array}\right]& \left(2\right)\end{array}$

This matrix equation can also be written as Equation (3):

$\begin{array}{cc}\mathrm{Ax}=b\mathrm{where}& \left(3\right)\\ A=\left[\begin{array}{ccc}{C}_{v_1}^P& C_{v_1}^M& C_{v_1}^B\\ C_{v_2}^P& C_{v_2}^M& C_{v_2}^B\\ ⋮& ⋮& ⋮\\ C_{v_j}^P& C_{v_j}^M& C_{v_j}^B\end{array}\right]& \left(4\right)\\ x=\left[\begin{array}{c}{I}_P\\ I_M\\ I_B\end{array}\right]\mathrm{and}& \left(5\right)\\ b=\left[\begin{array}{c}{M}_{v_1}\\ M_{v_2}\\ ⋮\\ M_{v_j}\end{array}\right]& \left(6\right)\end{array}$

Matrix x includes the discrete tissue images, into which the composite images M_v1, M_v2, . . . M_vj are decomposed. In the exemplary embodiment, matrix x includes three discrete images, I_P, I_M, and I_B. Alternatively, matrix x can include any number of discrete images. In the exemplary embodiment, matrix A includes muscle image scaling factors C_v1^M, C_v2^M, . . . C_vj^M, bone image scaling factors C_v1^B, C_v2^B, . . . C_vj^B, and plastic scaling factors C_v1^P, C_v2^P, . . . C_vj^P. Image scaling factors C_v^M, C_v^B, and C_v^P are determined as described in detail below.

While the above equations are for an embodiment where composite images M_v1, M_v2, . . . M_vj are decomposed into discrete plastic image I_P, discrete muscle image I_M, and discrete bone image I_B, those of ordinary skill in the art will appreciate that the above equations can be modified to decompose composite images into M_v1, M_v2, . . . M_vj into any suitable number of discrete images for any types of materials which enable imaging system 200 to function as described herein. For example, for security applications, imaging system 200 may decompose composite images into two discrete images: an image of tissue material in object 106 and an image of non-tissue material in object 106.

FIGS. 4( a)-4(c) are schematic diagrams of detector array 202. FIGS. 5(a)-5(c) are calibration graphs of detected phase shift Δ versus detecting electrode number for detector array 202 as shown in FIGS. 4(a)-4(c), respectively. The data shown in the calibration graphs of FIGS. 5(a)-5(c) includes FEM data generated by simulating detector array 202 of FIGS. 4(a)-4(c). However, acquiring experimental data for detector array 202, as described herein, would yield substantially similar results.

In the embodiment of FIG. 4(a), a muscle calibration object 402 is located at a center 404 of detector array 202. Muscle calibration object 402 is composed of muscle material, and does not include bone material or plastic material. Detector array 202 acquires image data of muscle calibration object 402, as described above. Because muscle calibration object 402 is located at center 404, image data need only be acquired using one electrode 230 as emitting electrode 232. More specifically, when muscle calibration object 402 is located at center 404 of detector array 202, image data acquired using any one electrode 230 as emitting electrode 232 should be identical to image data acquired using any other electrode 230 as emitting electrode 232.

To generate the calibration graph of FIG. 5(a), image data of muscle calibration object 402 is acquired for the plurality of electric field frequencies v₁, v₂, . . . v_j. In the exemplary embodiment, image data of muscle calibration object 402 is acquired at 1, 5, 10, 15, and 20 MHz. Alternatively, image data of muscle calibration object 402 may be acquired at any frequencies that allow imaging system 200 to function as described herein. From the calibration graph, muscle image scaling factors C_v1^M, C_v2^M, . . . C_vj^M can be determined. In the exemplary embodiment, the maximum value of each frequency curve is selected as the muscle image scaling factor. Alternatively, scaling factors C_v1^M, C_v2^M, . . . C_vj^M may be determined using any method that enables imaging system 200 to function as described herein.

In the embodiment of FIG. 4(b), a bone calibration object 406 is located at center 404 of detector array 202. Bone calibration object 406 is composed of bone material, and does not include muscle material or plastic material. Detector array 202 acquires image data of bone calibration object 406, as described above. Because bone calibration object 406 is located at center 404, image data need only be acquired using one electrode 230 as emitting electrode 232. More specifically, when bone calibration object 406 is located at center 404 of detector array 202, image data acquired using any one electrode 230 as emitting electrode 232 should be identical to image data acquired using any other electrode 230 as emitting electrode 232.

To generate the calibration graph of FIG. 5(b), image data of bone calibration object 406 is acquired for the plurality of electric field frequencies v₁, v₂, . . . v_j. In the exemplary embodiment, image data of bone calibration object 406 is acquired at 1, 5, 10, 15, and 20 MHz. Alternatively, image data of bone calibration object 406 may be acquired at any frequencies that allow imaging system 200 to function as described herein. From the calibration graph, bone image scaling factors C_v1^B, C_v2^B, . . . C_vj^B can be determined In the exemplary embodiment, the maximum value of each frequency curve is selected as the bone image scaling factor. Alternatively, scaling factors C_v1^B, C_v2^B, . . . C_vj^B may be determined using any method that enables imaging system 200 to function as described herein.

In the embodiment of FIG. 4(c), a plastic calibration object 408 is located at center 404 of detector array 202. Plastic calibration object 408 is composed of plastic material, and does not include muscle material or bone material. Detector array 202 acquires image data of plastic calibration object 408, as described above. Because plastic calibration object 408 is located at center 404, image data need only be acquired using one electrode 230 as emitting electrode 232. More specifically, when plastic calibration object 408 is located at center 404 of detector array 202, image data acquired using any one electrode 230 as emitting electrode 232 should be identical to image data acquired using any other electrode 230 as emitting electrode 232.

To generate the calibration graph of FIG. 5(c), image data of plastic calibration object 408 is acquired for the plurality of electric field frequencies v₁, v₂, . . . v_j. In the exemplary embodiment, image data of plastic calibration object 408 is acquired at 1, 5, 10, 15, and 20 MHz. Alternatively, image data of plastic calibration object 408 may be acquired at any frequencies that allow imaging system 200 to function as described herein. From the calibration graph, plastic image scaling factors C_v1^P, C_v2^P, . . . C_vj^P can be determined In the exemplary embodiment, the minimum value of each frequency curve is selected as the plastic image scaling factor. Alternatively, scaling factors C_v1^P, C_v2^P, . . . C_vj^P may be determined using any method that enables imaging system 200 to function as described herein.

Comparing FIG. 5(c) with FIGS. 5(a) and 5(b), it can be seen that the detected phase shifts Δ for plastic calibration object 408 generally have much less variation over the range of frequencies than the detected phase shift Δ of muscle calibration object 402 and bone calibration object 406. This is due to the difference between the electrical properties of plastic and the electrical properties of bone and muscle.

In the exemplary embodiment, the image scaling factors are determined by acquiring image data of calibration objects, such as, for example, muscle calibration object 402, bone calibration object 406, and plastic calibration object 408. Alternatively, any technique that enables imaging system 200 to function as described herein may be utilized to determine the image scaling factors, including, but not limited to, finite element modeling. Once image scaling factors, for example, image scaling factors C_v^M, C_v^B, and C_v^P, are determined, matrix x, and accordingly, discrete images, I_P, I_M, and I_B, are given by Equation (7):

x=(A^TA)⁻¹A^Tb   (7)

Thus, after decomposing the composite images M_v1, M_v2, . . . M_vj into discrete images I_P, I_M, and I_B, each discrete image can be displayed separately, for example, on display device 206. In the exemplary embodiment, discrete image I_P includes any regions of object 106 composed of plastic component 254. As such, from discrete image I_P, it can be determined whether or not a plastic component 254 is present in object 106.

FIGS. 6( a)-6(c) are a discrete muscle image I_M, a discrete bone image I_B, and a discrete plastic image I_P, respectively, of an object including muscle component 250 and bone component 252, but no plastic component 254. FIGS. 7(a)-7(c) are a discrete muscle image I_M, a discrete bone image I_B, and discrete plastic image I_P, respectively, of an object including muscle component 250, bone component 252, and plastic component 254. As demonstrated by a comparison of FIG. 6(c) and FIG. 7(c), the presence of plastic component 254 is clearly identifiable in discrete plastic image I_P. Accordingly, in embodiments where imaging system decomposes composite images into an image including tissue material and an image including non-tissue material, the non-tissue image may be analyzed and/or visually inspected to determine whether object 106 includes any non-tissue material.

FIG. 8 is a flowchart of an exemplary method 800 that may be used with imaging system 200 (shown in FIG. 2). Processing device 204 instructs detector array 202 to acquire 802 tomographic image data of an object, for example, object 106, at a plurality of frequencies v₁, v₂, . . . v_j. Using the acquired tomographic image data, processing device 204 generates 804 a composite image M_v of the object at each of the plurality of frequencies v₁, v₂, . . . v_j. That is, processing device 204 generates composite images M_v1, M_v2, . . . M_vj. Composite image M_v is modeled 806 as a function of discrete component images. For example, using a linear least square approximate, M_v may be modeled by αI₁+βI₂=M_v, where α and β are constants, and I₁ and I₂ are discrete images of a first material and a second material, respectively.

For the first material, processing device 204 determines 808 a scaling factor C_v¹ at each of the plurality of frequencies v₁, v₂, . . . v_j. For example, processing device 204 may determine scaling factors C_v1¹, C_v2¹, . . . C_vj¹. Further, for the second material, processing device 204 determines 810 a scaling factor C_v² at each of the plurality of frequencies v₁, v₂, . . . v_j. For example, processing device 204 may determine scaling factors C_v1², C_v2², . . . C_vj². The scaling factors C_v¹ and C_v² may be determined using methods and systems similar to those described with respect to FIGS. 4(a)-4(c) and 5(a)-5(c). Alternatively, any methods and systems that enable imaging system 200 to function as described herein may be utilized to determine the scaling factors C_v.

Using the determined scaling factors C_v, processing device 204 decomposes 812 the composite images M_v into the first discrete image I₁ and the second discrete image I₂. Discrete image I₁ contains any region of the object composed of the first material, and discrete image h contains any region of the object composed of the second material. In one embodiment, discrete image I₁ is discrete muscle image I_M containing any region of object 106 composed of muscle component 250, and discrete image I₂ is discrete plastic image I_P containing any region of object 106 composed of plastic component 254. While in exemplary method 800, composite images M_v are only decomposed into two discrete images, I₁ and I₂, composite images M_v can be decomposed 812 into any number of discrete images, each discrete image representative of a different material. In method 800, processing device 204 also causes at least one of the discrete images, I₁ and I₂, to be displayed 814 on a display device, such as, for example, display device 206.

Because contraband objects may have electrical properties significantly different from body tissue of a subject, method 800 and/or system 200 may be implemented in various security applications. For example, potential contraband objects made of powder crystalline material and/or plastic generally have relative permittivities and conductivities several orders of magnitude different from the values for body tissue.

FIG. 9 is a flowchart of an exemplary method 900 for detecting contraband that may be used with the imaging system 200 (shown in FIG. 2). Processing device 204 instructs detector array 202 to acquire 902 tomographic image data of a subject, such as object 106 (shown in FIG. 1), at a plurality of frequencies. Similar to method 800, using the acquired tomographic image data, processing device 204 generates 904 a composite image of the subject at each of the plurality of frequencies. Processing device 204 determines 906 a scaling factor for a tissue material (e.g., bone, muscle, etc.) at each frequency, and determines 908 a scaling factor for a non-tissue material at each frequency. In some embodiments, the scaling factors may be determined 908 for a finite set of non-tissue materials. The finite set may include, but is not limited to a crystalline material, a metallic material, a non-metallic material, and/or a ceramic material that may indicate the presence of a plastic, a weapon, an explosive, and/or a narcotic on or in the subject.

Using the determined scaling factors, the composite images are decomposed 910 into a tissue image and a non-tissue image. In the exemplary embodiment, these images are displayed 912 on display device 206 (shown in FIG. 2). To detect contraband, processing device 204 determines 914 whether the non-tissue image includes any non-tissue material. In the exemplary embodiment, processing device 204 determines 914 whether the non-tissue image includes any non-tissue material by analyzing the intensity of pixels in the non-tissue image. For example, if a mean pixel value of the non-tissue image is greater than a predetermined threshold value, processing device 204 may determine that non-tissue image includes non-tissue material. Alternatively, processing device 204 may use other suitable methods to determine 914 whether the non-tissue image includes non-tissue material.

If processing device 204 does determine that the non-tissue image includes non-tissue material, contraband is potentially present on or in the subject. Accordingly, in the exemplary embodiment, processing device 204 generates 916 an alert when non-tissue material is detected. The alert may include any audio and/or visual indication that notifies an operator of the potential presence of contraband. For example, the alert may include at least one of a sound generated by processing device 204 and/or an icon, symbol, and/or message displayed on display device 206. Upon observing the alert, the operator may take appropriate action, such as detaining the subject and/or subjecting the subject to additional searching.

Notably, scanning subjects for contraband using security scanner 100 does not involve exposing the subjects to ionizing radiation. Further, while the resolution of images produced using security scanner 100 is sufficient to detect contraband, the resolution is below the level required to reveal specific body details of the subject, avoiding potential privacy issues. Moreover, as the processing device 204 determines 914 whether the subject includes non-tissue material, visual analysis of images by an operator is not required to detect potential contraband.

As described above, security scanner 100 may be implemented in various environments. Security scanner 100 provides a relatively fast method of determining whether contraband is present on a subject. Accordingly, a large number of subjects can be scanned in a relatively short time. For example, security scanner 100 may be utilized in correctional facilities where inmates or visitors may have contraband objects such as plastic weapons, drugs, money, cell phones, and other electronic devices hidden on their person or within their body cavities. Inmates or visitors can be quickly scanned using security scanner 100 when entering or leaving the facility. In another example, security scanner 100 may be used at border crossings to scan for drugs and other contraband in or on suspected smugglers. In yet another example, security scanner 100 may be used in airport security to scan for contraband within body cavities or locations where manual searches may be problematic (e.g., in a passenger's underwear). The security scanner 100 may be used as a stand-alone contraband detection system, or may also be combined with other imaging technologies, such as, for example, x-ray imaging and terahertz imaging.

The above-described embodiments provide an imaging system that can be used to detect contraband located in or near an individual's body. For example, in a particular embodiment, the imaging system acquires tomographic image data of an object at a plurality of frequencies and generates a composite image of the object at each of the frequencies. The imaging system further determines a scaling factor for a first material and a second material at each of the frequencies and decomposes the composite images into a first discrete image and a second discrete image using the scaling factors. From the discrete images, it can be determined whether contraband is located in or near the object.

A technical effect of the systems and methods described herein includes at least one of: (a) instructing a detector array to acquire tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography; (b) generating a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data; (c) determining a differentiation parameter for a tissue material at each of the plurality of frequencies; (d) determining a differentiation parameter for a non-tissue material at each of the plurality of frequencies; (e) decomposing the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material; and (f) determining whether the non-tissue image contains any non-tissue material.

A computer, such as those described herein, includes at least one processor or processing unit and a system memory. The computer typically has at least some form of non-transitory computer readable media. By way of example and not limitation, computer readable media include computer storage media and communication media. Computer storage media include volatile and nonvolatile, removable and nonremovable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Communication media typically embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. Those skilled in the art are familiar with the modulated data signal, which has one or more of its characteristics set or changed in such a manner as to encode information in the signal. Combinations of any of the above are also included within the scope of computer readable media.

Exemplary embodiments of an imaging system for use with a security scanner and methods for using the same are described above in detail. The methods and systems are not limited to the specific embodiments described herein, but rather, components of systems and/or steps of the methods may be utilized independently and separately from other components and/or steps described herein. For example, the methods may also be used in combination with other imaging systems and methods, and are not limited to practice with only the EFT systems and methods as described herein. Rather, the exemplary embodiment can be implemented and utilized in connection with many other imaging applications.

Although specific features of various embodiments of the invention may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the invention, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.

This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

1. A method for detecting contraband, said method comprising: acquiring tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography;generating a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data;determining a differentiation parameter for a tissue material at each of the plurality of frequencies;determining a differentiation parameter for a non-tissue material at each of the plurality of frequencies;decomposing the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material; anddetermining whether the non-tissue image contains any non-tissue material.

2. A method in accordance with claim 1, wherein determining a differentiation parameter for a non-tissue material comprises determining differentiation parameters for a finite set of non-tissue materials.

3. A method in accordance with claim 1, further comprising generating an alert if the non-tissue image contains any non-tissue material.

4. A method in accordance with claim 1, wherein acquiring tomographic image data comprises acquiring tomographic image data of the body cavity of a subject, and wherein determining whether the non-tissue image contains any non-tissue material comprises determining whether the body cavity contains any non-tissue material.

5. A method in accordance with claim 1, wherein acquiring tomographic image data comprises actively detecting a phase shift between an emitted electric field and a detected electric field.

6. A method in accordance with claim 1, wherein determining whether the non-tissue image contains any non-tissue material comprises analyzing an intensity of pixels in the non-tissue image.

7. A method in accordance with claim 6, wherein analyzing an intensity of pixels in the non-tissue image comprises determining whether a mean intensity value of pixels in the non-tissue image is above a threshold value.

8. A security scanner configured to detect contraband, said security scanner comprising: a detector array configured to acquire tomographic image data of a subject at a plurality of frequencies using low frequency electromagnetic tomography; anda processing device coupled to said detector array and configured to: generate a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data;determine a differentiation parameter for a tissue material at each of the plurality of frequencies;determine a differentiation parameter for a non-tissue material at each of the plurality of frequencies;decompose the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material; anddetermine whether the non-tissue image contains any non-tissue material.

9. A security scanner in accordance with claim 8, wherein to determine a differentiation parameter for a non-tissue material, said processing device is configured to determine differentiation parameters for a finite set of non-tissue materials.

10. A security scanner in accordance with claim 8, wherein said processing device is further configured to generate an alert if the non-tissue image contains any non-tissue material.

11. A security scanner in accordance with claim 8, wherein to acquire tomographic image data, said detector array is configured to acquire tomographic image data of a body cavity of the subject.

12. A security scanner in accordance with claim 8, wherein said detector array is configured to acquire tomographic image data by actively detecting a phase shift between an emitted electric field and a detected electric field.

13. A security scanner in accordance with claim 8, wherein to determine whether the non-tissue image contains any non-tissue material, said processing device is configured to analyze an intensity of pixels in the non-tissue image.

14. A security scanner in accordance with claim 13, wherein to analyze an intensity of pixels in the non-tissue image, said processing device is configured to determine whether a mean intensity value of pixels in the non-tissue image is above a threshold value.

15. One or more computer-readable storage media having computer-executable instructions embodied thereon for scanning a subject for contraband, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: instruct a detector array to acquire tomographic image data of the subject at a plurality of frequencies using low frequency electromagnetic tomography;generate a composite image of the subject at each of the plurality of frequencies using the acquired tomographic image data;determine a differentiation parameter for a tissue material at each of the plurality of frequencies;determine a differentiation parameter for a non-tissue material at each of the plurality of frequencies;decompose the composite images into a tissue image and a non-tissue image using the determined differentiation parameters, wherein the tissue image contains any region of the subject composed of the tissue material and the non-tissue image contains any region of the subject composed of the non-tissue material; anddetermine whether the non-tissue image contains any non-tissue material.

16. One or more computer-readable storage media in accordance with claim 15, further comprising computer executable-instructions that cause the at least one processor to generate an alert if the non-tissue image contains any non-tissue material.

17. One or more computer-readable storage media in accordance with claim 15, wherein to generate a composite image of the subject, said one or more computer-readable storage media comprise computer executable-instructions that cause the at least one processor to generate a composite image of a body cavity of the subject at the plurality of frequencies.

18. One or more computer-readable storage media in accordance with claim 15, wherein to instruct a detector array to acquire tomographic image data, said one or more computer-readable storage media comprise computer executable-instructions that cause the at least one processor to instruct the detector array to actively detect a phase shift between an emitted electric field and a detected electric field.

19. One or more computer-readable storage media in accordance with claim 15, wherein to determine whether the non-tissue image contains any non-tissue material, said one or more computer-readable storage media comprise computer executable-instructions that cause the at least one processor to analyze an intensity of pixels in the non-tissue image.

20. One or more computer-readable storage media in accordance with claim 19, wherein to analyze an intensity of pixels in the non-tissue image, said one or more computer-readable storage media comprise computer executable-instructions that cause the at least one processor to determine whether a mean intensity value of pixels in the non-tissue image is above a threshold value.