Patent Application
20090003699
Embodiments of the present invention relate generally to image segmentation and, more particularly, to computer systems and methods for user guided image segmentation of radiographic images.
Image segmentation, the process of separating objects of interest from the background (or from other objects) in an image, is typically a difficult task for a computer to perform. If an image scene is simple and the contrast between objects in the scene and the background is high, then the task may be somewhat easier. However, if an image scene is cluttered and the contrast between objects in the scene and the background (or other objects) is low, image segmentation can be a particularly difficult problem. For example, in a radiographic image of a three-dimensional object such as a cargo container there can be numerous layers of objects and contrast may be low between the objects and the background. In addition to the difficulties often associated with low contrast and cluttered scenes, radiographic images of objects having layers may also present a need to segment the image in two ways: in the x-y plane (i.e., the plane the image was produced on) and by layer of depth in order to correct for layer effects such as overlapping.
User guided object segmentation recognition can be considered a type of semi-automatic image segmentation. Due to the present difficulty of image segmentation discussed above and the potentially safety critical nature of image processing, such as for security screening, it may be desirable to use a human operator to aid a computer in segmentation of readiographic images. In user guided segmentation, an image is presented to an operator. This image can be a raw image or a processed image. For example, the image can be a radiographic image on which an automatic segmentation process has been performed.
Embodiments of the present invention can be used in an imaging system, such as a nuclear material detection system, that includes a capability of producing images using a plurality of different energy levels. Each energy level provides a different imaging characteristic such as energy penetration of the object being scanned. Different images produced using different energy levels can be used in conjunction with each other to better identify layers within the object being scanned.
In an exemplary embodiment, an operator (or user) of a system can view an image and outline or select a region of interest (ROI) using an input device such as a mouse or graphics tablet. For example, an operator can outline a region of interest using mouse clicks to define a path around the ROI. Alternatively, the operator can select a region of the image using a click and drag approach common in many Windows applications. Once the region of interest has been selected by the operator, then an automatic recognition (e.g., an Z effective estimation processes) process can be performed on the region. By narrowing the processing region, the automatic image segmentation algorithms may be able to achieve an improved Z effective result, or a different result. It is also possible that even with user guidance the automatic segmentation routines or modules produce the same result as was initially determined. In any case, the result of the automatic segmentation on the user selected region can be displayed for the operator to view.
One exemplary embodiment can include a system for user guided segmentation of radiographic images of a cargo container. The system can include an object segmentation recognition module and an operator terminal including a display device and an input device, the operator terminal coupled to the object segmentation module. The object segmentation recognition module can have instructions stored in a memory that when executed cause the object segmentation recognition module to perform a series of functions. The functions can include segmenting a plurality of radiographic images of a cargo conveyance and outputting region of interest coordinates and a corrected atomic number image as output. The functions can also include providing a first image to the operator terminal for display, the first image being based on the corrected atomic number image. The functions can include receiving input from the operator terminal, the input including an indication of a selected region of the first image; and performing an additional segmentation process on only the selected region of the first image. The functions can include providing a second image to the operator terminal for display, the second image based on the selected region of the first image and results of the additional segmentation process.
Another embodiment includes a method for user guided segmentation of radiographic images. The method includes segmenting a plurality of radiographic images in order to determine regions of interest and corrected atomic number values, and correcting an atomic number image to generate a corrected atomic number image. The method can also include providing a first image to an operator terminal for display, the first image being based on the corrected atomic number image, and receiving input from the operator terminal, the input including an indication of a selected region of the first image. The method can also include performing an additional segmentation process on the selected region of the first image and providing a second image to the operator terminal for display, the second image based on the selected region of the first image and results of the additional segmentation process. A goal of user guided segmentation can be to get a better characterization of the objects which can result in better layer extraction and a better Z estimation for the material being studied. Providing a human user or operator in the loop can allow interaction with areas of an image that might be potential threats and that may not get resolved by the automatic ROI processing. The method can include providing updated regions of interest and corrected atomic number values based on the additional segmentation process as output.
Another embodiment includes a radiographic image segmentation apparatus. The apparatus can include means for segmenting a radiographic image in order to determine regions of interest and corrected atomic number values, and means for providing a first image to an operator terminal for display. The apparatus can also include means for receiving input from the operator terminal, the input including an indication of a selected region of the first image and means for performing an additional segmentation process on the selected region of the first image. The apparatus can also include means for providing a second image to the operator terminal for display, the second image being based on the selected region of the first image and results of the additional segmentation process; and means for providing updated regions of interest and corrected atomic number values based on the additional segmentation process as output.
(OSR) processor 102 is shown receiving one or more images 104 as input and providing region of interest (ROI) or object coordinates 106 as output.
In operation, the images 104 provided or obtained as input to the OSR processor 102 can include radiographic images or other images. For example, the images 104 can include radiographic images of a cargo conveyance such as a cargo container. The images 104 can include one or more images, for example four images can be provided with each image being generated using a different radiographic energy level. Also, the images 104 can include radiographic images or other images derived from radiographic images, such as, for example, an atomic number image representing estimated atomic numbers associated with radiographic images.
The OSR processor 102 can obtain, request or receive the images 104 via a wired or wireless connection, such as a network (e.g., LAN, WAN, wireless network, Internet or the like) or direct connection within a system. The OSR processor 102 can also receive the images 104 via a software connection (e.g., procedure call, standard object access protocol, remote procedure call, or the like). In general, any known or later developed wired, wireless or software connection suitable for transmitting data can be used to supply the images 104 to the OSR processor 102. The OSR processor 102 can be requested to segment images by another process or system, or can request images for segmenting from another process or system. If the images 104 include more than one image, the images can be registered prior to being sent for segmentation.
The OSR processor 102 processes the images 104 to segment the images 104 and identify objects within the images 104. The OSR processor 102 can also extract or identify layers within the images in order to help segment the images more accurately. The layer information can also be used to correct or adjust estimated atomic numbers in an atomic number image or map. The atomic number image or map can include a representation of estimated atomic numbers determined from the images 104.
Once the images 104 have been segmented and the layer information has been determined, regions of interest (ROIs) within the images 104 can be located or determined. The ROIs can be determined based on an image characteristic such as estimated atomic number of the ROI (or object), shape of the ROI, position or location of the ROI, or the like. The OSR processor 102 can provide ROI/object coordinates 106 as output. The ROI/object coordinates 106 can be associated with the input images 104 or an atomic number image. The output ROI/object coordinates 106 can be outputted via a wired or wireless connection, such as a network (e.g., LAN, WAN, Internet or the like) or direct connection within a system. The output ROI/object coordinates 106 can be outputted via a software connection (e.g., response to a procedure call, standard object access protocol, remote procedure call, or the like).
In operation, the segment processing section receives the images 104. Once received, the images 104 can be processed using one or more image segmentation modules (e.g., the connected region analysis module 204, the edge analysis module 206, or a combination of the above). It will be appreciated that the segmentation modules shown are for illustration purposes and that any known or later developed image segmentation processes can be used. Also, the selection of the number and type of image segmentation modules employed in the OSR processor 102 may depend on a contemplated use of an embodiment and the selection may be guided by a number of factors including, but not limited to, type of materials being scanned, configuration of the scanning system and objects being scanned, desired performance characteristics, time available for processing, or the like. The images 104 can also be processed by one or more layer analysis modules (e.g., the ratio layer analysis module 208, the BSS layer analysis module 210, or a combination of the above).
Once the segmentation processing (object segmentation, layer analysis, or both) has been completed, the resulting image segment data can be provided to the object ROI section 212. In the object ROI section 212, the layers and segments of the image segment data are analyzed and combined or associated to produce segment-layer data that contains information about objects and layers within the images 104. The segment-layer data can be in the form of an atomic number image that represents a composite of the images 104 and has been adjusted or corrected based on layers and segments to provide an image suitable for identification of ROIs. The segment-layer data can also be represented in any form suitable for transmitting the information that may be needed to analyze the images 104. The segment-layer data is then provided to the object ROI determination module 216 for analysis and identification of ROIs.
The object ROI determination module 216 can use one or more image characteristics to identify ROIs within the images 104 or the segment-layer data. Image characteristics can include an estimated atomic number for a portion of the image (e.g., a pixel, segment, object, region or the like), a shape of a segment or object within the image, or a position or location of an object or segment. In general, any image characteristic that is suitable for identifying an ROI can be used.
Once the ROIs have been determined, coordinate data (106) representing each ROI can be provided as output. The output can be provided as described above in connection with reference number 106 of
In step 304, one or more radiographic images are obtained. These images can be provided by an imaging system (e.g., an x-ray, magnetic resonance imaging device, computerized tomography device, or the like). In general, any imaging device suitable for generating images that may require segmenting can be used. Processing continues to step 306.
In step 306, the radiographic images are segmented. The segmentation can be performed using one or more image segmentation processes. Examples of segmentation methods include modules or processes for segmentation based on clustering, histograms, edge detection, region growing, level set, graph partitioning, watershed, model based, and multi-scale. Processing continues to step 308.
In step 308, any layers present in the images are determined. The layers can be determined using one or more layer extraction or identification processes. For example, a ratio layer analysis process and a BSS layer analysis process can be used together to identify layers in the images. A goal of layer identification and extraction is to remove overlapping effects which may be present. By removing overlapping effects, the true gray level of a material can be determined. Using the true gray level, a material's effective atomic number (and, optionally, material density) can be determined. Using the effective atomic number the composition of the material can be determined and illicit materials, such as special nuclear materials can be detected automatically.
The ratio method of layer identification and overlap effect removal is known in the art as applied to dual energy and is described in “The Utility of X-ray Dual-energy Transmission and Scatter Technologies for Illicit Material Detection,” a published Ph.D. Dissertation by Lu Qiang, Virginia Polytechnic Institute and State University, Blacksburg, Va., 1999, which is incorporated herein by reference in its entirety. Generally, the ratio method provides a process whereby a computer can solve for one image layer and remove any overlapping effects of another layer. Thus, regions that overlap can be separated into their constituent layers and a true gray level can be determined for each layer.
Blind source separation (or blind signal separation) is a technique known in the art, and refers generally to the separation of a set of signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. However, if information about how the signals were mixed (e.g., a mixing matrix) can be estimated, it can then be used to determine an un-mixing matrix which can be applied to separate the components within a mixed signal.
The BSS method may be limited by the amount of independence between materials within the mixture. Several techniques exist for estimating the mixing matrix; some include using an unsupervised learning process. The process can include incrementally changing and weighting coefficients of the mixing matrix and evaluating the mixing matrix until optimal conditions are met. Once the mixing matrix is estimated, un-mixing coefficients can be computed. Examples of some BSS techniques include projection pursuit gradient ascent, projection pursuit stepwise separation, ICA gradient ascent, and complexity pursuit gradient ascent. In general, an iterative hill climbing or other type of optimization process can be used to estimate the mixing matrix and determine an optimal matrix. Also, contemplated or desired performance levels may require development of custom algorithms that can be tuned to a specific empirical terrain provided by the mixing and un-mixing matrices. Once the layers are identified and overlapping effects are removed, processing continues to step 310.
In step 310, segments that have been identified are associated with any layers that have been determined or identified in step 308. Associating segments with layers can help to remove any overlapping effects and also can improve the ability to determine a true gray value for a segment. Processing continues to step 312.
In step 312, ROIs are determined. The ROIs can be determined based on an image characteristic as described above. Processing continues to step 314.
In step 314, a gray level atomic number image is optionally adjusted to reflect the corrections or adjustments provided by the layer determination. The adjustments or corrections can include changes related to removal of overlap effects or other changes. Processing continues to step 316.
In step 316, the ROI coordinates and, optionally, the adjusted or corrected gray level image are provided as output to an operator or another system. The output can be in a standard format or in a proprietary format. Processing continues to step 318 where the method ends. It will be appreciated that steps 304-316 can be repeated in whole or in part to perform a contemplated image segmentation process.
The pixel-level Z-value gray scale image 412 can be provided to an image segmentation and layer analysis module 414. The segmentation and layer analysis module 414 segments the image and analyzes layers, as described above, to generate a layer corrected image representing true gray values, ROI coordinates, or both.
As mentioned above, it may be desirable to use a human operator to aid in segmentation.
The threat detection system 510 processes the scanned images 508 to detect (either automatically or with some manual input) nuclear material or other possible threats revealed by the radiographic imaging. Part of the detection process includes segmenting one or more of the images using OSR module 511. The results of the segmentation can be provided to an operator station 514 via link 512. The results can be in the form of a graphical image suitable for display to an operator or user of the object screening system 500.
Once the operator has viewed the image displayed on the operator station 514, the operator can provided input to the operator station 514. The input (or an encoded form thereof) can be transmitted via link 512 to the threat detection system 510 and/or the OSR module 511. The OSR module 511 can then perform an additional segmentation process on a selected portion of the image to produce another segmentation result. A second image, based on the result of the additional segmentation process can be provided to the operator station 514 for viewing by the operator. The segmentation results can be released by the operator for additional processing by the threat detection system 510 or additional segmentation process can be requested. Also, the operator can manually indicated object segmentations using an input device associated with the operator station 514. The manually generated segmentations, the automatic segmentation results, or both, can be sent to the threat detection system 510.
The threat detection system can be a stand alone system or form part of a larger security system. Link 512 can be a wired or wireless link such as a LAN, WAN, wireless network connection, radio link, optical link, or the like.
The energies 504a and 504b can include, for example, two or more different energy levels of x-ray energy. It will be appreciated that other types of electromagnetic energy can be used to scan the object 502. It will also be appreciated that although two energies (504a and 504b) are shown, more or less energies can be used with an embodiment. Any type of scanner suitable for detecting contraband such as nuclear material and capable or producing an image (or array of values) may be used. The object being screened (or scanned) can include a cargo container, a truck, a tractor trailer, baggage, cargo, luggage, a vehicle, an air cargo container, and/or any object being transported that could potentially contain nuclear material or a portion of a threat or weapon system, or any object for which threat or contraband screening is contemplated or desired.
In step 604, radiographic images are obtained. The images can be in the form of raw radiographic image data, gray level data representing effective atomic number, or a hybrid image containing both. Processing continues to step 606.
In step 606, the radiographic images are automatically segmented by computer using one or more segmentation and/or layer analysis algorithms as discussed above. Processing continues to step 608.
In step 608, ROI coordinates and, optionally, adjusted gray level image(s) are output as a first image to an operator station. At the operator station, the output can be displayed in any suitable means such as on a display screen or printed. Processing continues to step 610.
In step 610, input is received from an operator using the operator station indicating a selected region of the first image. Processing continues to step 612.
In step 612, an additional segmentation process is performed on the selected region. The additional segmentation process can be performed using the same algorithms as used for the initial automatic segmentation or may be performed using different algorithms. Also, the same algorithms may be used with different parameters for the segmentation of the selected region. Processing continues to step 614.
In step 614, a second image is provided to the operator station. The second image is based on results of the additional segmentation process of step 612. Processing continues to step 616. The second image could be a smaller region of the original image or sub-image.
In step 616, ROI coordinates and, optionally, adjusted gray level images based on the additional segmentation processing are output. The ROI coordinates and adjusted gray level image can be provided to another module in a threat detection system, such as material context analysis processor. The output can be provided to another system or another operator. The decision to release the results of the additional segmentation processing can be made by the operator or can be automatic. The final output can be a combination of the segmentation results of the initial processing and the segmentation results of the additional processing. Processing continues to step 618 where processing ends. It will be appreciated that steps 604-616 can be repeated in whole or in part to perform a contemplated user guided image segmentation process.
Once the selected region or portion of the image has been indicated by the operator, the region can be sent to the segmentation module for an additional segmentation processing. The additional segmentation processing can include the same or different algorithms or parameters as used for the initial automatic segmentation. Also, if multiple images are available, a user guided layer extraction can be performed in which the operator can view each image individually or view the group as a composite and can select regions of interest from each of the images for additional layer extraction or analysis processing. Once the additional segmentation and/or layer correction has been performed, a second image can be provided to the operator station. This image can be the same or different from the initial segmentation image.
For example,
It will be appreciated that the modules, processes, systems, and sections described above can be implemented in hardware, software, or both. Also, the modules, processes systems, and sections can be implemented as a single processor or as a distributed processor. Further, it should be appreciated that the steps mentioned above may be performed on a single or distributed processor. Also, the processes, modules, and sub-modules described in the various figures of the embodiments above may be distributed across multiple computers or systems or may be co-located in a single processor or system. Exemplary structural embodiment alternatives suitable for implementing the modules, sections, systems, means, or processes described herein are provided below.
The modules, processors or systems described above can be implemented as a programmed general purpose computer, an electronic device programmed with microcode, a hard-wired analog logic circuit, software stored on a computer-readable medium or signal, a programmed kiosk, an optical computing device, a GUI on a display, a networked system of electronic and/or optical devices, a special purpose computing device, an integrated circuit device, a semiconductor chip, and a software module or object stored on a computer-readable medium or signal, for example.
Embodiments of the method and system (or their sub-components or modules), may be implemented on a general-purpose computer, a special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmed logic circuit such as a PLD, PLA, FPGA, PAL, or the like. In general, any process capable of implementing the functions or steps described herein can be used to implement embodiments of the method, system, or a computer program product (software program).
Furthermore, embodiments of the disclosed method, system, and computer program product 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 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 can be implemented in hardware and/or software using any known or later developed systems or structures, devices and/or software by those of ordinary skill in the applicable art from the function description provided herein and with a general basic knowledge of the computer, image processing, radiographic, and/or threat detection arts.
Moreover, embodiments of the disclosed method, system, and computer program product can be implemented in software executed on a programmed general purpose computer, a special purpose computer, a microprocessor, or the like. Also, the method of this invention can be implemented as a program embedded on a personal computer such as a JAVA® 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 into a software and/or hardware system, such as the hardware and software systems of multi-energy radiographic cargo inspection systems.
It is, therefore, apparent that there is provided, in accordance with the present invention, a method, computer system, and computer software program for user guided image segmentation. While this invention has been described in conjunction with a number of embodiments, it is evident that many alternatives, modifications and variations would be or are apparent to those of ordinary skill in the applicable arts. Accordingly, Applicant intends to embrace all such alternatives, modifications, equivalents and variations that are within the spirit and scope of this invention.
The present application claims the benefit of U.S. Provisional Patent Application No. 60/940,632, entitled “Threat Detection System”, filed May 29, 2007, which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 60940632 | May 2007 | US |
