Patent Publication Number: US-9898678-B2

Title: Compound object separation

Description:
RELATED APPLICATION 
     This application is a continuation of and claims priority to U.S. patent application Ser. No. 13/380,079, titled “COMPOUND OBJECT SEPARATION” and filed on Dec. 22, 2011, which is a National Phase Entry of PCT/US2009/049236, filed on Jun. 30, 2009. U.S. application Ser. No. 13/380,079 and PCT Application PCT/US2009/049236 are incorporated herein by reference. 
    
    
     BACKGROUND 
     The present application relates to the field of x-ray and computed tomography (CT). It finds particular application with CT security scanners. It also relates to medical, security, and other applications where identifying sub-objects of a compound object would be useful. 
     Security at airports and in other travel related areas is an important issue given today&#39;s sociopolitical climate, as well as other considerations. One technique used to promote travel safety is baggage inspection. Often, an imaging apparatus is utilized to facilitate baggage screening. For example, a CT device may be used to provide security personnel with two and/or three dimensional views of objects. After viewing images provided by the imaging apparatus, security personnel may make a decision as to whether the baggage is safe to pass through the security check-point or if further (hands-on) inspection is warranted. 
     Current screening techniques and systems can utilize automated object recognition in images from an imaging apparatus, for example, when screening for potential threat objects inside luggage. These systems can extract an object from an image, and compute properties of these extracted objects. Properties of scanned objects can be used for discriminating an object by comparing the objects properties (e.g., density, shape, etc.) with known properties of threat items, non-threat items, or both classes of items. It can be appreciated that an ability to discriminate potential threats may be reduced if an extracted object comprises multiple distinct physical objects. Such an extracted object is referred to as a compound object. 
     A compound object can be made up of two or more distinct items. For example, if two items are lying side by side and/or touching each other, a security scanner system may extract the two items as one single compound object. Because the compound object actually comprises two separate objects, however, properties of the compound object may not be able to be effectively compared with those of known threat and/or non-threat items. As such, for example, luggage containing a compound object may unnecessarily be flagged for additional (hands-on) inspection because the properties of the compound object resemble properties of a known threat object. This can, among other things, reduce the throughput at a security checkpoint. Alternatively, a compound object that should be inspected further may not be so identified because properties of a potential threat object in the compound object are “contaminated” or combined with properties of one or more other (non-threat) objects in the compound object, and these “contaminated” properties (of the compound object) might more closely resemble those of a non-threat object than those of a threat object, or vice versa. 
     Compound object splitting can be applied to objects in an attempt to improve threat item detection, and thereby increase the throughput and effectiveness at a security check-point. Compound object splitting essentially identifies potential compound objects and splits them into sub-objects. Compound object splitting involving components with different densities may be performed using a histogram-based compound object splitting algorithm. Other techniques include using surface volume erosion to split objects. However, using erosion as a stand-alone technique to split compound objects can lead to undesirable effects. For example, erosion can reduce a mass of an object, and indiscriminately split objects that are not compound, and/or fail to split some compound objects. Additionally, in these techniques, erosion and splitting may be applied universally, without regard to whether an object is a potential compound object at all. 
     SUMMARY 
     Aspects of the present application address the above matters, and others. According to one aspect, a method for splitting a potential three-dimensional compound objects is provided. The method comprises projecting three-dimensional image data indicative of a potential three-dimensional compound object under examination onto a two-dimensional manifold projection and recording a correspondence between the three-dimensional image data (e.g., voxel data) and the 2D manifold projection (e.g., pixel data). The method also comprises segmenting the two-dimensional manifold projection to generate a two-dimensional segmented manifold projection indicative of one or more sub-objects. The method further comprises projecting the two-dimensional segmented manifold projection into three-dimensional image data indicative of the sub-objects utilizing the correspondence between the three-dimensional image data and the two-dimensional manifold projection. 
     According to another aspect, an apparatus is provided. The apparatus comprises a projector configured to project three-dimensional image data indicative of a potential compound object into a two-dimensional manifold projection indicative of the potential compound object. The apparatus also comprises a two-dimensional segmentation component configured to segment the two-dimensional manifold projection to generate a two-dimensional segmented projection indicative of one or more sub-objects of the potential compound object. The apparatus also comprises a back-projector configured to project the two-dimensional segmented projection into three-dimensional image data indicative of the sub-objects. 
     According to another aspect, a method is provided. The method comprises projecting three-dimensional image data indicative of a potential compound object under examination into a two-dimensional manifold projection of the potential compound object and recording a correspondence between the three-dimensional image data and the two-dimensional manifold projection. The method also comprises eroding the two-dimensional manifold projection using an adaptive erosion technique and segmenting the two-dimensional manifold projection to generate a two-dimensional segmented projection indicative of one or more sub-objects. The method further comprises pruning pixels indicative of sub-objects of the two-dimensional segmented projection that do not meet predetermined criteria and projecting the two-dimensional segmented projection into three-dimensional image data indicative of the corresponding one or more sub-objects utilizing the correspondence between the three-dimensional image data and the two-dimensional manifold projection. 
     Those of ordinary skill in the art will appreciate still other aspects of the present invention upon reading and understanding the appended description. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic block diagram illustrating an example scanner. 
         FIG. 2  is a component block diagram illustrating one or more components of an environment wherein compound object splitting of objects in an image may be implemented as provided herein. 
         FIG. 3  is a component block diagram illustrating details of one or more components of an environment wherein compound object splitting of objects in an image may be implemented as provided herein. 
         FIG. 4  is a flow chart diagram of an example method for compound object splitting. 
         FIG. 5  is a graphical representation of three-dimensional image data of a compound object being converted onto a two-dimensional manifold projection. 
         FIG. 6  illustrates a portion of a two-dimensional manifold projection. 
         FIG. 7  illustrates a portion of a two-dimensional manifold projection after the projection has been eroded. 
         FIG. 8  is a graphical representation of a two-dimensional manifold projection that has been eroded. 
         FIG. 9  is a graphical representation of a two-dimensional manifold projection that has been segmented. 
         FIG. 10  is a graphical representation of a two-dimensional manifold projection that has been pruned. 
         FIG. 11  is a graphical representation of a two-dimensional, segmented manifold projection being projected into three-dimensional space. 
         FIG. 12  is a graphical representation of a compound object, a compound object after two-dimensional segmentation, and a compound object after three-dimensional segmentation. 
         FIG. 13  is an illustration of an example computer-readable medium comprising processor-executable instructions configured to embody one or more of the provisions set forth herein. 
     
    
    
     DETAILED DESCRIPTION 
     The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are illustrated in block diagram form in order to facilitate describing the claimed subject matter. 
     Systems and techniques for separating a compound object representation into sub-objects in an image generated by subjecting one or more objects to imaging using an imaging apparatus (e.g., a computed tomography (CT) image of a piece of luggage under inspection at a security station at an airport) are provided herein. That is, in one embodiment, techniques and systems for splitting compound objects into distinct sub-objects is provided. 
       FIG. 1  is an illustration of an example environment  100  in which a system may be employed for identifying potential threat containing objects, from a class of objects, inside a container that has been subjected to imaging using an imaging apparatus (e.g., a CT scanner). In the example environment  100  the imaging apparatus comprises an object scanning apparatus  102 , such as a security scanning apparatus (e.g., used to scan luggage at an airport). The object scanning apparatus  102  may be used to scan one or more objects  110  (e.g., a series of suitcases at the airport). The scanning apparatus typically comprises a rotating gantry portion  114  and a stationary gantry portion  116 . 
     The rotating gantry portion  114  comprises a radiation source  104  (e.g., an X-ray tube), an array of radiation detectors  106  (e.g., X-ray detectors), and a rotator  112  (e.g., a gantry motor) for rotating the rotating gantry portion  114  (i.e., including the radiation source  104  and detectors  106 ) around the object(s) being scanned  110 . An examination surface  108  (e.g., a conveyor belt) passes through a hole in the rotating gantry portion  114  and may be configured to convey the object(s)  110  from an upstream portion of the object scanning apparatus  102  to a downstream portion. 
     As an example, a computer tomography (CT) security scanner that includes an X-ray source  104 , such as an X-ray tube, can generate a fan, cone, wedge, or other shaped beam of X-ray radiation that traverses one or more objects  110 , such as suitcases, in an examination region. In this example, the X-rays are emitted by the source  104 , traverse the examination region that contains the object(s)  110  to be scanned, and are detected by an X-ray detector  106  across from the X-ray source  104 . Further, a rotator  112 , such as a gantry motor drive attached to the scanner, can be used to rotate the X-ray source  104  and detector  106  around the object(s)  110 , for example. In this way, X-ray projections from a variety of perspectives of the suitcase can be collected, for example, creating a set of X-ray projections for the object(s). While illustrated with the x-ray source  104  and detector  106  rotating around an object, in another example, the radiation source  104  and detector  106  may remain stationary while the object  110  is rotated. 
     In the example environment  100 , a data acquisition component  118  is operably coupled to the object scanning apparatus  102 , and is typically configured to collect information and data from the detector  106 , and may be used to compile the collected data into projection space data  150  for an object  110 . As an example, X-ray projections may be acquired at each of a plurality of angular positions with respect to the object  110 . Further, as the object(s)  110  is conveyed from an upstream portion of the object scanning apparatus  102  to a downstream portion (e.g., conveying objects parallel to the rotational axis of the scanning array (into and out of the page)), the plurality of angular position X-ray projections may be acquired at a plurality of points along the axis of rotation with respect to the object(s)  110 . In one embodiment, the plurality of angular positions may comprise an X and Y axis with respect to the object(s) being scanned, while the rotational axis may comprise a Z axis with respect to the object(s) being scanned. 
     In the example environment  100 , an image extractor  120  is coupled to the data acquisition component  118 , and is configured to receive the data  150  from the data acquisition component  118  and generate three-dimensional image data  152  indicative of the scanned object  110  using a suitable analytical, iterative, and/or other reconstruction technique (e.g., backprojecting from projection space to image space). 
     In one embodiment, the three-dimensional image data  152  for a suitcase, for example, may ultimately be displayed on a monitor of a terminal  132  (e.g., desktop or laptop computer) for human observation. In this embodiment, an operator may isolate and manipulate the image, for example, rotating and viewing the suitcase from a variety of angles, zoom levels, and positions. 
     It will be appreciated that, while the example environment  100  utilizes the image extractor  120  to extract three-dimensional image data from the data  150  generated by the data acquisition component  118 , for example, for a suitcase being scanned, the techniques and systems, described herein, are not limited to this embodiment. In another embodiment, for example, three-dimensional image data may be generated by an imaging apparatus that is not coupled to the system. In this example, the three-dimensional image data may be stored onto an electronic storage device (e.g., a CD-ROM, hard-drive, flash memory) and delivered to the system electronically. 
     In the example environment  100 , in one embodiment, an object and feature extractor  122  may receive the data  150  from the data acquisition component  118 , for example, in order to extract objects and features  154  from the scanned items(s)  110  (e.g., a carry-on luggage containing items). It will be appreciated that the systems, described herein, are not limited to having an object and feature extractor  122  at a location in the example environment  100 . For example, the object and feature extractor  122  may be a component of the image extractor  120 , whereby three-dimensional image data  152  and object features  154  are both sent from the image extractor  120 . In another example, the object and feature extractor  122  may be disposed after the image extractor  120  and may extract object features  154  from the three-dimensional image data  152 . Those skilled in the art may devise alternative arrangements for supplying three-dimensional image data  152  and object features  154  to the example system. 
     In the example environment  100 , an entry control  124  may receive three-dimensional image data  152  and object features  154  for the one or more scanned objects  110 . The entry control  124  can be configured to identify a potential compound object in the three-dimensional image data  152  based on an object&#39;s features. In one embodiment, the entry control  124  can be utilized to select objects that may be compound objects  156  for processing by compound object splitting system  126 . In one example, object features  154  (e.g., properties of an object in an image, such as an Eigen-box fill ratio) can be computed prior to the entry control  124  and compared with pre-determined features for compound objects (e.g., features extracted from known compound objects during training of a system) to determine whether the one or more objects are compound objects. In another example, the entry control  124  calculates the density of a potential compound object and a standard deviation of the density. If the standard deviation is outside a predetermined range, the entry control  124  may identify the object as a potential compound object. Objects that are not determined to be potential compound objects by the entry control  124  may not be sent through the compound object splitting system  126 . 
     In the example environment  100 , the compound object splitting system  126  receives three-dimensional image data indicative of a potential compound object  156  from the entry control  124 . The compound object splitting system  126  can be configured to generate sub-objects from the potential compound object by projecting the three-dimensional image data onto a two-dimensional manifold projection (i.e., modeled on Euclidean space, for example) and recording a correspondence between the three-dimensional image data (e.g., voxel data) and the two-dimensional manifold projection (e.g., pixel data). Once projected, one or more pixels indicative of the compound object in the two-dimensional manifold projection are eroded. Pixels that are not eroded may be segmented to generate a two-dimensional segmented projection indicative of one or more sub-objects of the potential compound object  156 . It will be appreciated that where the potential compound object  156  is actually a single object (and not a plurality of objects), the two-dimensional segmented projection may be indicative of a sub-object that substantially resembles the potential compound object  156 . The two-dimensional segmented projection may then be projected from two-dimensional manifold projection space to three-dimensional image data indicative of the sub-objects  158  utilizing the correspondence between the three-dimensional image data and the two-dimensional manifold data. 
     In the example environment  100 , a three-dimensional segmentation component  128  may be configured to receive the three-dimensional image data indicative of the sub-objects  158  and segment the three-dimensional image data indicative of the sub-object  158  to identify secondary sub-objects. The three-dimensional segmentation component  128  may also be configured to generate three-dimensional image data  160  indicative of the identified secondary sub-objects and/or the sub-objects (e.g., identified by the compound object splitting system  126 ). It will be appreciated that if no secondary sub-objects are identified, the three-dimensional image data  169  output by the three-dimensional segmentation component  128  may be indicative of the sub-objects. 
     In the example environment  100 , a threat determiner  130  can receive image data for an object, which may comprise image data indicative of sub-objects and/or image data indicative of secondary sub-objects. The threat determiner  130  can be configured to compare the image data to one or more pre-determined thresholds, corresponding to one or more potential threat objects. It will be appreciated that the systems and techniques provided herein are not limited to utilizing a threat determiner, and may be utilized for separating compound objects without a threat determiner. For example, image data for an object may be sent to a terminal  132  wherein an image of the object  110  under examination may be displayed for human observation. 
     Information concerning whether a scanned object is potentially threat containing and/or information concerning sub-objects  162  can be sent to a terminal  132  in the example environment  100 , for example, comprising a display that can be viewed by security personal at a luggage screening checkpoint. In this way, in this example, real-time information can be retrieved for objects subjected to scanning by the object scanning apparatus  102 . 
     In the example environment  100 , a controller  134  is operably coupled to the terminal  132 . The controller  134  receives commands from the terminal  132  and generates instructions for the object scanning apparatus  102  indicative of operations to be performed. For example, a human operator may want to rescan the object  110  and the controller  134  may issue an instruction instructing the examination surface  108  to reverse direction (e.g., bringing the object back into an examination region of the object scanning apparatus  102 ). 
       FIG. 2  is a component block diagram illustrating one embodiment  200  of an entry control  124 , which can be configured to identify a potential compound object based on an object&#39;s features. The entry control  124  can comprise a feature threshold comparison component  202 , which can be configured to compare the respective one or more feature values  154  to a corresponding feature threshold  250 . 
     In one embodiment, image data  152  for an object in question can be sent to the entry control  124 , along with one or more corresponding feature values  154 . In this embodiment, feature values  154  can include, but not be limited to, an object&#39;s shape properties, such as an Eigen-box fill ratio (EBFR) for the object in question. As an example, objects having a large EBFR typically comprise a more uniform shape; while objects having a small EBFR typically demonstrate irregularities in shape. In this embodiment, the feature threshold comparison component  202  can compare one or more object feature values with a threshold value for that object feature, to determine which of the one or more features indicate a compound object for the object in question. In another embodiment, the feature values  154  can include properties related to the average density of the object and/or the standard deviation of densities of portions of the object. The feature threshold comparison component  202  may compare the standard deviation of the densities to a threshold value to determine whether a compound object may be present. 
     In the example embodiment  200 , the entry control  124  can comprise an entry decision component  204 , which can be configured to identify a potential compound object based on results from the feature threshold comparison component  202 . In one embodiment, the entry decision component  204  may identify a potential compound object based on a desired number of positive results for respective object features, the positive results comprising an indication of a potential compound object. As an example, in this embodiment, a desired number of positive results may be one hundred percent, which means that if one of the object features indicates a non-compound object, the object may not be sent to be separated  160 . However, in this example, if the object in question has the desired number of positive results (e.g., all of them) then the image data for the potential compound object can be sent for separation  156 . In another example, the entry decision component  204  may identify a potential compound object when the standard deviation exceeds a predefined threshold at the feature threshold comparison component  202 . 
       FIG. 3  is a component block diagram of one example embodiment  300  of a compound object splitting system  126 , which can be configured to generate three-dimensional image data  158  indicative of sub-objects from three-dimension image data  156  indicative of a potential compound object. 
     The example embodiment of the compound object splitter system  126  comprises a projector  302  configured to receive the three-dimensional image data  156  indicative of the potential compound object. The projector is also configured to convert that three-dimensional image data  156  indicative of the potential compound object into a two-dimensional manifold projection  350  indicative of the potential compound object and record a correspondence  351  between the three-dimensional image data and the two-dimensional manifold projection. That is, one or more voxels of the three-dimensional image data are recorded as being represented by, or associated with, a pixel of the two-dimensional manifold projection  350  indicative of the potential compound object. Such a recording may be beneficial during back-projection from two-dimensional manifold projection space to three-dimensional image space so that properties of the voxels (e.g., densities of the voxels, atomic numbers identified by the voxels, etc.) are not lost during the projection and back-projection, for example. It will be appreciated that while the projector  302  records the correspondence  351  in this embodiment, in other embodiments, another component of the object splitter system  126  and/or the other components of the example environment  100  may record the correspondence  351 . 
     It will be understood to those skilled in the art that a manifold is a two-dimensional representation of a three-dimensional function. For example, a two-dimensional atlas of a portion of a globe may be considered a two-dimensional manifold projection. While a point on the two-dimensional atlas may not correspond perfectly with a corresponding point on a three-dimensional globe when viewed in the context of neighboring points (e.g., because of the lack of a z-dimension, for example), when viewed individually, or locally (e.g., not in context with neighboring points), the point is a substantially perfect representation of the corresponding point on the three-dimensional globe. 
     In one example, the two-dimensional manifold projection  350  is mapped to Euclidean space. In this way, the manifold&#39;s dimension is a topological invariant, and thus the two-dimensional manifold projection  350  maintains topological properties of a given space in the three-dimensional image data  156 . 
     A pixel in the two-dimensional manifold projection  350  represents one or more voxels of the three-dimensional image data  156 . The number of voxels that are represented by a given voxel may depend upon the number of object voxels that are “stacked” in a dimension of the three-dimensional image data  156  that is not included in the two-dimensional manifold projection  350 . For example, if at a given x and z coordinate, three voxels are stacked in y-dimension of the three-dimensional image data  156 , a pixel corresponding to the given x and z coordinate may represent three voxels in the two-dimensional manifold projection  350 . Similarly, a pixel adjacent to the pixel may represent five voxels if at a second x and z coordinate, five voxels are stacked in the y-dimension (e.g., the compound object has a larger y-dimension at the x, z coordinates of the adjacent pixel than it does at the pixel). The number of voxels represented by a pixel may be referred to as a “pixel value”. 
     In the example embodiment  300 , the compound object splitter system  126  further comprises a manifold projection eroder  304 , which is configured to receive the two-dimensional manifold projection  350 . The manifold projection eroder  304  is also configured to erode the two-dimensional manifold projection  350 , and thus reveal one or more sub-objects of the potential compound object. In one example, the manifold projection eroder  304  uses an adaptive erosion technique to erode one or more pixels of the two-dimensional manifold projection  350 , and the sub-objects are revealed based upon spaces, or gaps, within the compound object. It will be appreciated that an “adaptive erosion technique” as used herein refers to a technique that adjusts criteria, or thresholds, for determining which pixels to erode as a function of characteristics of one or more (neighboring) pixels. That is, the threshold is not constant, but rather changes according to the properties, or characteristics of the pixels. 
     In one example of an adaptive erosion technique, the manifold projection eroder  304  determines whether to erode a first pixel by comparing pixels values for pixels neighboring the first pixel to determine an erosion threshold for the first pixel. Once the erosion threshold for the first pixel is determined, the threshold is compared to respective pixel values of the neighboring pixels. If a predetermined number of respective pixel values are below the threshold, the first pixel is eroded (e.g., a value of the pixel is set to zero or some value not indicative of an object). The manifold projection eroder  304  may repeat a similar adaptive erosion technique on a plurality of pixels to identify spaces, or divides, in the compound object. In this way, one or more portions of the compound object may be divided to reveal one or more sub-objects (e.g., each “group” of pixels corresponding to a sub-object). It will be appreciated that other adaptive techniques and/or static techniques (e.g., where the threshold remains constant during the erosion of a plurality of pixels) known to those skilled in the art are also contemplated. 
     The compound object splitting system  126  further comprises a two-dimensional segmentation component  306  configured to receive the eroded manifold projection  352  from the manifold projection eroder  304  and to segment the two-dimensional manifold projection to generate a two-dimensional, segmented, manifold projection  354 . As an example, segmentation may include binning the pixels into bins corresponding to a respective sub-object and/or labeling pixels associated with identified sub-objects. For example, before erosion, the pixels may have been labeled with number “1”, indicative of (compound) object “1”. However, after erosion, one or more sub-objects of the (compound) object “1” may be identified and a first group of pixels may be labeled according to a value (e.g., “1”) assigned to a first identified sub-object, a second group of pixels may be labeled according to a value (e.g., “2”) assigned to a second identified sub-object, etc. In this way, respective sub-objects may be identified as distinct objects in the image, rather than a single compound object. 
     In the example embodiment  300 , the compound object splitter system  126  further comprises a pruner  308  that is configured to receive the two-dimensional, segmented, manifold projection  354 . The pruner is also configured to prune pixels of the two-dimensional segmented manifold projection  354  that are indicative of sub-objects that do not meet predetermined criteria (e.g., the sub-object is represented by too few pixels to be considered a threat, the mass of the sub-object is not great enough to be a threat, etc.). In one embodiment, pruning comprises relabeling pixels indicative of the sub-objects that do not meet predetermined criteria as background (e.g., labeling the pixels as “0”), or otherwise discarding the pixels. As an example, a sub-object that is represented by three pixels may be immaterial to achieving the purpose of the examination (i.e., threat detection), and the pruner may discard the sub-object by altering the pixels. 
     The compound object splitting system  126  further comprises a back-projector  310  configured to receive the pruned and segmented manifold projection  356  and to project the two-dimensional manifold projection  356  into three-dimensional image data indicative of the sub-objects  158 . That is, the back-projector  310  is configured to reverse map the data from two-dimensional manifold space into three-dimensional image space utilizing the correspondence  351  between the three-dimensional image data and the two dimensional manifold projection. In this way, voxels of the three-dimensional data indicative of the potential compound object  156  may be relabeled according to the labels assigned to corresponding pixels in the two-dimensional manifold projection  356  to generate the three-dimensional image data indicative of the sub-objects  158 . For example, voxels originally labeled as indicative of compound object “1” may be relabeled; a portion of the voxels relabeled as indicative of sub-object “1” and a portion of the voxels relabeled as indicative of sub-object “2.” It will be appreciated that by relabeling the voxels of the three-dimensional data indicative of the potential compound object  156 , properties of the voxels (and therefore of the object) may be retained. Stated differently, by using such a technique, the properties of the object may not be lost during the projection into manifold projection space and the projection from manifold projection space into three-dimensional image space. 
     It will appreciated that in one embodiment, the three-dimensional image data indicative of the sub-objects  158  is segmented by a three-dimensional segmentation component (e.g.,  128  in  FIG. 1 ) that further refines that object, or rather detects secondary sub-objects that were not identified by the compound object splitting system  126  to generate three-dimensional data indicative of one or more secondary sub-objects. For example, where two objects substantially overlap in the y-dimension and are connected to a third object, the compound object splitting system  126  may not recognize the substantially overlapping objects if the manifold projection depicts that x and z dimensions. Therefore, the compound object splitting system  126  may separate the three objects of the compound object into two sub-objects. A first sub-object may comprise the two substantially overlapping objects and the second sub-object may comprise the third sub-object. The three-dimensional segmentation component  128  may recognize a gap in the y-dimension between the two sub-objects and separate the first sub-object into two secondary sub-objects, for example. Thus, the compound object splitting system  126  splits the compound object into two objects and the three-dimensional segmentation component  128  splits the two objects into three objects. It will be appreciated a three-dimensional segmentation component  128  placed before the compound object splitting system  126  may not recognize the two substantially overlapping objects as two objects because both were connected to the third object, and therefore the three-dimensional segmentation component  128  would not have identified the gap in the y-dimension between the two overlapping objects. 
     The three-dimensional image data indicative of the sub-objects  158  and/or three-dimensional data indicative of the secondary sub-objects (e.g.,  160  in  FIG. 1 ) may be displayed on a monitor of a terminal (e.g.,  132  in  FIG. 1 ) and/or transmitted to a threat determiner (e.g.,  130 ) that is configured to identify threats according to the properties of an object. Because the compound object has been divided into sub-objects, the threat determiner may better discern the characteristics of an object and thus may more accurately detect threats, for example. 
     A method may be devised for separating a compound object into sub-objects in an image generated by an imaging apparatus. In one embodiment, the method may be used by a threat determination system in a security checkpoint that screens passenger luggage for potential threat items. In this embodiment, an ability of a threat determination system to detect potential threats may be reduced if compound objects are introduced, as computed properties of the compound object may not be specific to a single physical object. Therefore, one may wish to separate the compound object into distinct sub-objects of which it is comprised. 
       FIG. 4  is a flow chart diagram of an example method  400 . Such an example method  400  may be useful for splitting a potential three-dimensional compound object, for example. The method begins at  402  and involves projecting three-dimensional image data indicative of a potential compound object under examination onto a two-dimensional manifold projection of the potential compound object and a correspondence between the three-dimensional image data and the two-dimensional manifold projection is recorded at  404 . That is, the image data is mapped from three-dimensional image space to two-dimensional manifold projection space and voxel data one or more voxels of the image space are recorded as being associated with a pixel of the two-dimensional manifold projection. In one embodiment, the two-dimensional manifold projection space is Euclidean space. 
     It will be appreciated that before the three-dimensional image data is projected into two-dimensional manifold projection space, it may be useful to first identify whether an object is likely to be a potential compound object. In this way, the acts herein described may not be performed unless it is probably that an identified object is a compound object. In one example, the probability that an object is a potential compound object is determined by calculating the average density and/or atomic number (i.e., if the scanner is a dual energy scanner) and a standard deviation. If the standard deviation is above a predefined threshold, the object may be considered a potential compound object and thus the acts herein described may be performed to split the potential compound object into one or more sub-objects. 
       FIG. 5  is a graphical representation of three-dimensional image data of a compound object  500  being projected  502  onto a two-dimensional manifold projection  504 . As illustrated, the compound object  500  is collapsed into a two-dimensional plane that retains two dimensions (e.g., an x-dimension and a z-dimension) of the compound object  500  (e.g., the y-dimension is lost during the projection). 
     Because a dimension is lost when projecting from three-dimensional space to two-dimensional space, pixels of the two-dimensional manifold projection are assigned a value (herein referred to as a “pixel value”) based upon the number of voxels represented by the pixel. For example, if a y-dimension of the image data is lost during the projection, the pixel is assigned a value corresponding to the number of voxels in the y-dimension that the pixel represents. 
       FIG. 6  illustrates an enlargement  600  of a portion of two-dimensional manifold projection  506  in  FIG. 5 . The squares  602  represent pixels in the two-dimensional manifold. Pixels above a diagonal line  604  (e.g., an edge of a rectangular portion  508  of the object  500  in  FIG. 5 ) are representative of the rectangular portion  508 . Pixels below an arched line  606  (e.g., an edge of an oval portion  510  of the compound object  500  in  FIG. 5 ) are representative of the oval portion  510 . As illustrated, respective pixels are assigned a pixel value  608  (e.g., a number) corresponding to the number of voxels represented by the pixel. For example, pixels representative of the rectangular portion  508  have a pixel value of nine because the rectangular portion  508  was represented by nine voxels in the y-dimension  512  (at all x and z dimensions of the rectangular portion  508 ). Similarly pixels representative of the oval portion  510  have a pixel value of three because the oval portion  510  was represented by three voxels in the y-dimension  514  (at all x and z dimensions of the oval portion  510 ). It will be appreciated that pixels that are representative of both of the oval portion  510  and the rectangular portion  508  (e.g., pixels that are situated between the diagonal line  604  and the arched line  606 ) may be assigned a pixel value corresponding to the portion of the object represented by a larger number of voxels (e.g., the rectangular portion  508 ). 
     Returning to  FIG. 4 , at  406  the two-dimensional manifold projection is eroded. That is, connections between two or more objects are removed so that the objects are defined as a plurality of objects rather than a single, compound object. Typically, eroding involves setting pixels identified with the connection to a value (e.g., zero) indicative of no object, or rather indicative of background. 
     In one example, an adaptive erosion technique is used to erode the two-dimensional manifold projection. A determination of whether to erode pixels is dynamic (e.g., the erosion characteristics are not constant) and is based upon characteristics of pixels neighboring the pixel being considered for erosion. That is, a threshold for determining whether to erode a pixel or not to erode a pixel is based upon characteristics of neighboring pixels and the same threshold may not be used for each pixel that is being considered for erosion. An adaptive erosion technique may be beneficial over other erosion techniques known to those skilled in the art to preserve portions of the object (e.g.,  500  in  FIG. 5 ) that are located towards the interior of the object, or rather sub-objects, and portions of the object that are strongly connected based on probability analysis (using a Markov random field model), for example. 
     As an example, the adaptive erosion technique used to determine whether to erode a first pixel may comprise comparing pixel values (e.g.,  608  in  FIG. 6 ) for pixels neighboring the first pixel to determine an erosion threshold for the first pixel. Once the erosion threshold for the first pixel has been determined, it may be compared to respective pixel values of the neighboring pixels. If a predetermined number of respective pixel values of neighboring pixels are below the erosion threshold, the first pixel may be eroded. These acts may be repeated to determine an erosion threshold for a second pixel and to determine whether to erode the second pixel. 
       FIG. 7  illustrates the enlargement  600  in  FIG. 6  after the two-dimensional manifold has been eroded. As illustrated, pixels were eroded if at least four neighboring (e.g., in this case adjacent) pixels did not exceed the erosion threshold for the pixel under consideration for erosion. The eroded pixels (e.g.,  702 ) are represented by a pixel value of zero. The pixels that were not eroded maintained the pixel value that was assigned to them before the two-dimensional manifold projection was eroded. 
       FIG. 8  illustrates the two-dimensional manifold projection  504  after erosion (e.g., an eroded manifold projection). It will be appreciated that sub-objects of the compound object  500  have been defined and are no longer in contact with one another (e.g., there is space  802  between sub-objects). This may allow a two-dimensional segmentation component (e.g.,  306  in  FIG. 3 ) to more easily segment the compound object into sub-objects, for example. 
     Returning to  FIG. 4 , at  408  the two-dimensional manifold projection is segmented to generate a two-dimensional, segmented manifold projection indicative of one or more sub-objects. Segmentation generally involves binning (e.g., grouping) pixels representative of a sub-object together and/or labeling pixels to associate the pixels with a particular object. For example, a suitcase may have a plurality of objects (each object identified by a label in the three dimensional image data). One object, identified by label “5” may be considered a potential compound object and thus image data of the potential object may be converted to manifold projection space and each pixel may be identified by the label “5” (e.g., corresponding to the object being examined). After the manifold projection is eroded, two sub-objects may be identified and the pixels may be relabeled (e.g., segmented). A first sub-object may be labeled “5,” for example, and a second sub-object may be labeled “6.” In this way, two sub-objects may be identified from a single potential compound object. 
       FIG. 9  illustrates a two-dimensional segmented, manifold projection  900  indicative of three objects. Pixels indicative of a rectangular sub-object  902  are labeled with a first label, pixels indicative of an oval sub-object  904  are labeled with a second label, and pixels indicative of a circular sub-object  906  are labeled with a third label. Stated different, pixels of the two-dimensional manifold  505  that were originally indicative of a single potential compound object  500  are now indicative of three sub-objects. It will be appreciated that the shading in  FIG. 9  is only intended to represent the recognition of sub-objects, rather than a single compound object, and is not intended to represent coloring or shading of the manifold projection  900 . 
     At  410  in  FIG. 4 , pixels indicative of sub-objects of the two-dimensional segmented, manifold projection  900  that not meet predetermined criteria are pruned (e.g., the pixels are set to a background value). The predetermined criteria may include a pixel count for the sub-object (e.g., a number of pixels representative of the sub-object), that mass of the sub-object, and/or another criteria that would help determine whether the sub-object is valuable to the examination and therefore should not be pruned. For example, pixels that are indicative of a sub-object that is unlikely to be a threat because of the size of the sub-object may be removed so that time is not consumed backprojecting the pixels into three-dimensional space. In  FIG. 10 , the circular sub-object  906  of the two-dimensional segmented, manifold projection  900  is pruned  1002  because the number of pixels representing the circular sub-object  906  were too few to indicate that the sub-object was a security threat, for example. 
     At  412  in  FIG. 4 , the two-dimensional segmented projection is projected into three-dimensional image data indicative of the sub-objects utilizing the correspondence between the three-dimensional image data and the two-dimensional manifold projection. In one example, this includes relabeling voxels of the three-dimensional image data (e.g.,  156  in  FIG. 1 ) indicative of the potential compound object according to the labels of corresponding pixels in the two-dimensional, segmented manifold projection. For example, if voxels of the potential compound object were labeled as belonging to object “5” in a suitcase, the voxels may be relabeled so that some of the voxels are indicative of a rectangular object (labeled “5”) and some of the voxels are indicative of a circular object (labeled “6”). In this way, data that is determined to be indicative of a compound object it split into a plurality sub-objects. 
       FIG. 11  provides a graphical representation of the two-dimensional segmented, manifold projection  900  being projected  1102  into three-dimensional image data indicative of one or more sub-objects  1104 . As illustrated by the shading, the rectangular object  1106  is recognized as a first object and the oval object  1108  is recognized as a second object (e.g., the objects are no longer recognized as parts of a compound object  500 ). 
     In one embodiment, the three-dimensional image data indicative of the sub-objects may be segmented to further segment the sub-objects and identify one or more secondary sub-objects. A segmentation in three-dimensional image data after the two-dimensional segmentation may be useful to identify an object that substantially overlays a second object in the dimension that was lost when the three-dimensional image was converted to two-dimensional space (e.g., the y-dimension), and thus, could not be segmented in the two-dimensional space. 
       FIG. 12  depicts a series of representations of a three-dimension potential compound object being split through the acts herein describe. The first representation  1202  represents the potential compound object before the image data is converted to manifold projection space and/or two-dimensional segmentation has occurred. As illustrated, a first rectangular object  1204 , a second rectangular object  1206 , and a third rectangular object  1208  are part of the compound object. 
     The second representation  1210  represents the compound object after the manifold projection data has been segmented and projected back into three-dimensional image space. As illustrated, during segmentation in the two-dimensional manifold space, the compound object was recognized to be two-sub-objects. The first rectangular object  1204  (e.g., illustrated in a first shading pattern) was recognized as a different object that the second  1206  and third  1208  rectangular objects (e.g., illustrated in a second shading pattern). However, because the second  1206  and third  1208  rectangular objects were “stacked” on top of one another (e.g., the objects lie in the same x and z dimensions), the two-dimensional segmentation could not identify a “gap,” or weak connection, between the second  1206  and third  1208  rectangular objects. Therefore, voxels associated with the second rectangular object  1206  and voxels associated with the third rectangular object  1208  were labeled as being as a single sub-object. It will be appreciated that a three-dimensional segmentation prior to the two-dimensional segmentation would also not recognize the second  1206  and third  1208  rectangular objects as separate objects because they were both joined to the first rectangular object  1204 . 
     The third representation  1212  represents the compound object after a three-dimensional segmentation has occurred. Because the first rectangular object  1204  was recognized as a separate sub-object of the compound object during two-dimensional manifold segmentation, a three-dimension segmentation may identify a “gap” between the second  1206  and third  1208  rectangular objects and split the sub-object into two secondary sub-objects (e.g., each object is represented by a different shading pattern). Thus, the single compound object (e.g., represented by the first representation  1202 ) is split into three objects by performing a two-dimensional segmentation and a three-dimensional segmentation. 
     Returning to  FIG. 4 , the method ends at  414 . 
     Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein. An example computer-readable medium that may be devised in these ways is illustrated in  FIG. 13 , wherein the implementation  1300  comprises a computer-readable medium  1302  (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data  1304 . This computer-readable data  1304  in turn comprises a set of computer instructions  1306  configured to operate according to one or more of the principles set forth herein. In one such embodiment  1300 , the processor-executable instructions  1306  may be configured to perform a method  1308 , such as the example method  400  of  FIG. 4 , for example. In another such embodiment, the processor-executable instructions  1306  may be configured to implement a system, such as at least some of the exemplary scanner  100  of  FIG. 1 , for example. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with one or more of the techniques presented herein. 
     Moreover, the words “example” and/or “exemplary” are used herein to mean serving as an example, instance, or illustration. Any aspect, design, etc. described herein as “example” and/or “exemplary” is not necessarily to be construed as advantageous over other aspects, designs, etc. Rather, use of these terms is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated example implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”