Method and system for inspection of containers

A method and system for reconstructing and segmenting an image of the contents of a container. The method includes receiving actual scan data from a scan of the container, reconstructing the actual scan data to obtain a reconstructed image, and segmenting the reconstructed image to obtain a segmented image. The method also includes deriving simulated scan data corresponding to the segmented image, calculating an error term based on a difference between the simulated scan data and the actual scan data, and determining whether a criterion is satisfied. The method further includes using the error term to produce a modified reconstructed image and repeating the preceding steps with the modified reconstructed image substituted for the reconstructed image if the criterion is not satisfied, and outputting information from the segmented image if the criterion is satisfied.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The embodiments described herein relate generally to inspection of containers and, more particularly, to reconstructing and segmenting images of the contents of the containers to facilitate detecting objects concealed within the container.

2. Description of Prior/Related Art

At least some known detection systems construct an image of a container and analyze the image to detect explosives, drugs, weapons, and/or other contraband objects within the container. At least one known method for detecting objects within a container includes a first step of scanning the container with X-rays and reconstructing an image of the container, and a second step of inspecting the reconstructed image to detect objects of interest.

The first step involves reconstruction of an image, such as a computed tomography (CT) image, of the interior of the container. Each pixel in the reconstructed image generally represents a relative X-ray attenuation caused by the contents of the container corresponding to that pixel location. Known methods of image reconstruction include, for example, filtered backprojection (FBP) and iterative reconstruction (IR). Known implementations of these methods attempt to optimize performance based on various image-quality metrics. Such known image reconstruction algorithms typically have been developed for use by the medical community and, therefore, implementations of these algorithms are optimized for human interpretation of the images for the purpose of diagnosing disease.

At least some known detection systems accept such final reconstructed images as an input and apply various inspection algorithms to identify objects of interest within the image. Such inspection algorithms typically begin with a procedure, often referred to as image “segmentation” and/or “labeling,” to group the elements of the image data into clusters of like values that may correspond to particular objects or sub-objects within the container. Such known image segmentation procedures typically assume that certain inhomogeneities in the image data received from the reconstruction algorithm are artifacts induced in the reconstructed image through inconsistencies, noise, or errors in the measured X-ray data. As a result, detection systems using known image reconstruction and inspection algorithms must apply complex heuristic corrections during the inspection step to compensate for errors passed through from the image reconstruction step. The separation of image reconstruction and image segmentation into two independent steps thus decreases an accuracy of, and increases a time and cost required for inspection by, known detection systems.

BRIEF SUMMARY OF THE INVENTION

In one aspect, a method for reconstructing and segmenting an image of the contents of a container is provided. The method includes receiving actual scan data from a scan of the container, reconstructing the actual scan data to obtain a reconstructed image, and segmenting the reconstructed image to obtain a segmented image. The method also includes deriving simulated scan data corresponding to the segmented image, calculating an error term based on a difference between the simulated scan data and the actual scan data, and determining whether a criterion is satisfied. The method further includes using the error term to produce a modified reconstructed image and repeating the preceding steps with the modified reconstructed image substituted for the reconstructed image if the criterion is not satisfied, and outputting information from the segmented image if the criterion is satisfied.

In another aspect, an inspection system for detecting objects within a container is provided. The system includes a processor configured to receive actual scan data from a scan of the container, reconstruct the actual scan data to obtain a reconstructed image, and segment the reconstructed image to obtain a segmented image. The processor is also configured to derive simulated scan data corresponding to the segmented image, calculate an error term based on a difference between the simulated scan data and the actual scan data, and determine whether a criterion is satisfied. The processor is further configured to use the error term to produce a modified reconstructed image and to repeat the preceding steps with the modified reconstructed image substituted for the reconstructed image if the criterion is not satisfied, and output information from the segmented image if the criterion is satisfied.

In still another aspect, a computer program embodied on a computer-readable medium is provided. The computer program includes a code segment that configures a processor to receive actual scan data from a scan of the container, reconstruct the actual scan data to obtain a reconstructed image, and segment the reconstructed image to obtain a segmented image. The code segment also configures the processor to derive simulated scan data corresponding to the segmented image, calculate an error term based on a difference between the simulated scan data and the actual scan data, and determine whether a criterion is satisfied. The code segment further configures the processor to use the error term to produce a modified reconstructed image and to repeat the preceding steps with the modified reconstructed image substituted for the reconstructed image if the criterion is not satisfied, and output information from the segmented image if the criterion is satisfied.

DETAILED DESCRIPTION OF THE INVENTION

At least one embodiment of the present invention is described below with reference to a system for inspecting luggage. However, it should be apparent to those skilled in the art and guided by the teachings herein that embodiments of the invention likewise are applicable to any suitable system for scanning cargo containers including, without limitation, crates, boxes, drums, containers, baggage, and suitcases, transported by water, land, and/or air, as well as other containers and/or objects.

Moreover, although embodiments of the present invention are described below with reference to a system incorporating an X-ray computed tomography (CT) scanning system for inspecting luggage, it should apparent to those skilled in the art and guided by the teachings herein provided that any suitable scanning radiation source including, without limitation, neutrons or gamma rays, may be used in alternative embodiments. Further, it should be apparent to those skilled in the art and guided by the teachings herein provided that any scanning system may be used that produces a sufficient number of image elements to enable the functionality of the inspection system described herein. The term “image element” refers to an element, such as a pixel and/or voxel, within image data. Embodiments of the present invention may be used in association with three-dimensional images comprising voxels, or alternatively with two-dimensional images comprising pixels, or alternatively with images of any suitable dimension comprising any suitable combination and/or type of image element.

FIG. 1is a block diagram of an exemplary inspection system10for scanning a container12, such as a cargo container, box, parcel, luggage, or suitcase, to identify the contents and/or determine the type of material contained within container12. The term “contents” as used herein refers to any object and/or material contained within container12and may include contraband.

In an exemplary embodiment, inspection system10includes at least one X-ray source14configured to transmit at least one primary beam15of radiation through container12. In an alternative embodiment, inspection system10includes a plurality of X-ray sources14configured to emit radiation of different energy distributions. Alternatively, each X-ray source14is configured to emit radiation of selective energy distributions, which can be emitted at different times. In a particular embodiment, inspection system10utilizes multiple-energy scanning to obtain an attenuation map for container12. In addition to the production of CT images, multiple-energy scanning enables the production of density maps and atomic number of the object contents. In one embodiment, the dual energy scanning of container12includes scanning container12at a low energy and then scanning container12at a high energy. The data is collected for the low-energy scan and the high-energy scan to reconstruct a density image and/or atomic number image of container12, as well as a CT image, to further facilitate identifying the type of material within container12.

In an exemplary embodiment, inspection system10also includes at least one X-ray detector16configured to detect radiation emitted from X-ray source14and transmitted through container12. X-ray detector16is configured to cover an entire field of view or only a portion of the field of view. In certain embodiments, X-ray detector16includes a plurality of X-ray detector elements (not shown) arranged in a one-dimensional or two-dimensional matrix configuration. Upon detection of the transmitted radiation, X-ray detector16generates signals representative of the detected transmitted radiation.

In an exemplary embodiment of inspection system10, a data collection system18is operatively coupled to and in signal communication with X-ray detector16. Data collection system18is configured to receive the signals generated and transmitted by X-ray detector16. A processor20is operatively coupled to data collection system18. Processor20is configured to generate one or more images of container12and its contents and to process the produced image(s) to facilitate an inspection of the contents of container12. As used herein, the term “processor” is not limited to only integrated circuits referred to in the art as a processor, but broadly refers to a computer, a microcontroller, a microcomputer, a programmable logic controller, an application specific integrated circuit and any other programmable circuit. In certain embodiments, processor20refers to multiple individual processors, whether operating in concert or independently of each other. In certain embodiments, processor20reconstructs a CT image of container12in real time, near-real time, or delayed time. In the exemplary embodiment, such CT images are generated and examined to infer the presence and shape of objects, such as object28and object30, within container12.

In the exemplary embodiment, inspection system10also includes a display device22, a memory device24and/or an input device26operatively coupled to data collection system18and/or processor20. Display device22may be, but is not limited to, a monitor, a cathode ray tube (CRT), a liquid crystal display (LCD), and/or another suitable output device that enables system10to function as described herein. Memory device24may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a magnetic or optical drive (not shown), and/or another suitable storage device that enables inspection system10to function as described herein. Input device26may be, but is not limited to, a mouse, a keyboard, and/or another suitable input device that enables inspection system10to function as described herein.

FIG. 2shows a flowchart illustrating a method100for identifying objects within a container. In the exemplary embodiment, method100is implemented on inspection system10. However, method100is not limited to implementation on inspection system10, but rather method100may be embodied on any suitable system, such as a separate system that receives any suitable images for inspection. Moreover, method100may be embodied on a computer readable medium as a computer program, and/or implemented and/or embodied by any other suitable means. The computer program may include a code segment that, when executed by a processor, configures the processor to perform one or more of the functions of method100.

In an exemplary embodiment of method100, processor20receives110scan data45, for example CT data from a scan of container12by system10(shown inFIG. 1). Processor20reconstructs120a reconstructed image50from the received scan data45using any suitable reconstruction method, such as, but not limited to, a filtered back-projection algorithm. Next, processor20segments130the reconstructed image50using any suitable method such as, but not limited to, seeded region-growing, unseeded region-growing, clustering, edge detection, and/or a histogram-based algorithm, to obtain a segmented image55. Such methods implicitly assume that container12contains a number of discrete objects such as objects28and30(shown inFIG. 1) that each are homogeneous and, therefore, that images of the interior of container12are amenable to segmentation. Segmentation step130may include a thresholding step to reduce, for example, background objects, clutter or artifacts in the reconstructed image50.

For example, in an exemplary embodiment of a method200for performing segmentation step130as shown inFIG. 3, processor20identifies202a plurality of significant image elements75in reconstructed image50. Significant image elements75are those image elements that are assumed to belong to significant objects within container12(shown inFIG. 1). For example, but not by way of limitation, significant image elements75may be identified as those having CT values that are above a threshold value. Processor20then groups204the plurality of significant image elements75into a plurality of connected regions80. For example, processor20applies a suitable clustering algorithm, such as a connected components analysis (CCA), that groups204together significant image elements having certain characteristics. Moreover, processor20labels206each significant image element75that is grouped into at least a first connected region80as a labeled image element85of the first connected region80. Further in the exemplary embodiment, processor20calculates208a representative value90of first connected region80. For example, processor20calculates208representative value90to be the average CT value of labeled image elements85of first connected region80. In alternative embodiments, representative value90is, for example but not by way of limitation, a mode CT value of labeled image elements85of first connected region80, or some other suitable statistic. Processor20then creates210segmented image55from reconstructed image50by replacing a value of each labeled image element85, for example the CT value, with the corresponding representative value90.

In certain circumstances, exemplary embodiment200of segmentation step130may operate in a strongly non-linear fashion. An alternative exemplary embodiment300of segmentation step130, shown inFIG. 4, illustrates alternative or additional steps that may be included in segmentation step130to reduce the non-linearity. Processor20initially segments302the reconstructed image50using any suitable method to obtain at least one image segment92. If the method used is a clustering algorithm, for example, the at least one image segment92is akin to at least first connected region80(shown inFIG. 3). Processor20then calculates304representative value90, for example an average CT number, for the at least one image segment92. For each image element of a plurality of image elements within the at least one segment92, processor20further replaces306an original value associated with the image element with a value between the original value and representative value90for the at least one segment92. Processor20combines308the plurality of image elements having replaced values with reconstructed image50to obtain segmented image55.

Returning toFIG. 2, processor20further derives140simulated scan data60corresponding to the scan data that would have to be acquired by system10in order to obtain segmented image55as an output of reconstruction step120. For example, processor20may use a forward-projection algorithm incorporating model40to derive140simulated scan data60from segmented image55.

Further in the exemplary embodiment, processor20calculates150an error term65based on a difference between simulated scan data60and actual scan data45. Calculation150may be any suitable method that quantifies a variation between simulated scan data60and actual scan data45. In the exemplary embodiment, error term65is in the form of a sinogram, that is, it has the same dimensions, and may have the same units, as actual scan data45. For example, error term65may be a set of values representing a difference between each value in actual scan data45and the corresponding value in simulated scan data60, with some weighting applied. In certain embodiments, such weighting is chosen based on statistical estimates of the reliability of particular measurements within actual scan data45. For example, attenuation measurements by elements of X-ray detector16(shown inFIG. 1) that represent very low intensities may be considered less reliable than measurements that represent higher intensities. In such cases, the difference between actual scan data45and simulated scan data60for such low-intensity values may be weighted to have a smaller contribution to error term65. In an alternative embodiment, error term65is computed as a set of values based on a ratio of each value of simulated scan data60to the corresponding value in actual scan data45. Other functions may also be applied to error term65on a value-by-value basis.

Processor20then determines160whether a criterion is satisfied. For example, but not by way of limitation, the criterion may depend on a function based on the values of error term65, such as a maximum acceptable value for a summation of the squares of the values of error term65. Additionally or alternatively, the criterion may include a predetermined maximum number of iterations.

If processor20determines160that the criterion is satisfied, for example, if a function based on the values of error term65is below a maximum acceptable value or if a predetermined number of iterations has been reached, processor20outputs170information from segmented image55. For example, but not by way of limitation, processor20outputs170segmented image55itself by displaying it to an operator on display22and/or storing it in computer-readable memory24.

On the other hand, if processor20determines160that the criterion is not satisfied, processor20uses180error term65, for example by applying a suitable filtered back-projection algorithm, to produce a modified reconstructed image70. Segmentation130, derivation140of simulated scan data60, and calculation150of error term65are then repeated, with modified reconstructed image70substituted for reconstructed image50. In the exemplary embodiment, this iteration of steps continues until the criterion of step160is satisfied, at which point processor20outputs170information from resulting segmented image55.

The above-described systems and methods for inspection of containers facilitate increasing an accuracy of, and decreasing a time and cost required for, the detection of objects, including contraband, within containers. More specifically, the embodiments described herein combine image reconstruction and image segmentation into a single operation, and also incorporate into image reconstruction the expectation that the container contains a discrete number of homogeneous objects. A technical effect of the embodiments described herein is to reduce a number of artifacts in a reconstructed and/or segmented image. A further technical effect of the embodiments described herein is to reduce a need for inspection algorithms to apply complex heuristic corrections during the inspection step to compensate for errors passed through from the image reconstruction step.

Exemplary embodiments of methods and systems for inspection of containers 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 inspection systems as described herein. Rather, the exemplary embodiments can be implemented and utilized in connection with many other inspection and/or detection applications.