Analogic Corporation

Sergey Simanovsky

Carl R Crawford

Ram Naidu

Ibrahim M Bechwati

Zhengrong Ying

Muzaffer Hiraoglu

Andrew Litvin

Matthew Waggener Hirsch

Julia Pavlovich

David A Schafer

Sorin Marcovici

David Lieblich

Aleksander Roshi

Chitra Subramanian

Muzzafer Hiraoglu

Daniel Abenaim

Douglas Q Abraham

David Rozas

Basak Ulker Karbeyaz

Seemeen S Karimi

Gregory L Larson

Kyle Guilbert

Purvin Desai

Seemee S Karimi

Dong-Yueh Liang

Among other things, one or more systems and/or techniques for classifying an item disposed within an object are provided herein. A three-dimensional image of the object (e.g., a bag) is segmented into a set of item representations (e.g., laptop, thermos, etc.). An item is identified from the set of item representations based upon item features of the item, such as the laptop that could be used to conceal an item of interest such as an explosive. A region comprising a three-dimensional image of the item is divided into a set of sub-regions (e.g., a first sub-region encompassing a screen, a second sub-region encompassing a motherboard, etc.). The item is classified as a potential first type of item (e.g., an explosive laptop) when any sub-region has a number of voxels, with computed tomography (CT) values within a range of known CT values for a first type of item, exceeding a threshold.

Item classification using localized CT value distribution analysis

A detector array such as for use in a radiation imaging modality is provided. The detector array includes a first pixel () having a first scintillator (). The first scintillator has a first detection surface () and a first light emission surface (). The first detection surface extends along a first detection surface plane and the first light emission surface extends along a first light emission surface plane. The detector array includes a second pixel () having a second scintillator (). The second scintillator has a second detection surface () and a second light emission surface (). The second detection surface extends along a second detection surface plane and the second light emission surface extends along a second light emission surface plane. At least one of the first detection surface plane is not coplanar with the second detection surface plane or the first light emission surface plane is not coplanar with the second light emission surface plane.

Detector array for imaging modality

Among other things, one or more techniques and/or systems are described for correcting projection data generated from a computed tomography (CT) examination of an object and/or for computing or updating a CT value of the object from the projection data. An image generator is configured to generate a CT image of an object under examination. Using this CT image, a set of actions are performed to correct projection data from which the CT image was generated and/or to update a CT value of one or more voxels within the CT image. In this way, the projection data and/or CT image is adjusted to reduce image artifacts and/or otherwise improve image quality and/or object detection.

Projection data correction and computed tomography value computation

Among other things, one or more systems and/or techniques for segmenting a representation of a sheet object from an image are provided herein. To identify elements of an image (e.g., pixels and/or voxels) representative of sheet objects, a constant false alarm rate (CFAR) score and a topological score are computed for respective elements being analyzed. The CFAR score indicates a relationship between an element and a neighborhood of elements when viewed as a collective unit. The topological score indicates a relationship between the element and a neighborhood of elements when viewed neighbor-by-neighbor. When the CFAR score is within a specified range of CFAR scores and the topological score is within a specified range of topological scores, the element is labeled as being associated with a sheet object. A connected component labeling (CCL) approach may be used to group elements labeled as being associated with a sheet object.

Segmentation of sheet objects from image generated using radiation imaging modality

A CT scanning system may include a multi-pixel x-ray source, and a detector array. The multi-pixel x-ray source may have a plurality of pixels that are disposed along a z-axis, and that are sequentially activated so as to controllably emit x-rays in response to incident electrons. The detector array may have one or more rows of x-ray detectors that detect the x-rays that are emitted from the pixels and have traversed an object, and generate data for CT image reconstruction system. In third generation CT scanning systems, the number of detector rows may be reduced. Multi-pixel x-ray source implementation of saddle curve geometry may render a single rotation single organ scan feasible. Using a multi-pixel x-ray source in stationary CT scanning systems may allow x-ray beam design with a minimal coverage to satisfy mathematical requirements for reconstruction.

CT scanning systems and methods using multi-pixel x-ray sources

Among other things, one or more systems and/or techniques for identifying an occlusion region in an image representative of an object subjected to examination is provided for herein. Such systems and/or techniques may find particular application in the context of object recognition analysis. An image is generated of the object and an orientation of the object is determined from the image. Based upon the determined orientation of the object relative to the direction the object is translated during examination, one or more parameters utilized for segmenting a second image of the object, identifying features in the image, and/or determining if the image comprises an occlusion region may be adjusted. In this way, the parameters utilized may be a function of the determined orientation of the object, which may mitigate false positives of detected occlusion regions.

Automatic occlusion region identification using radiation imaging modality

Among other things, computed tomography (CT) systems and/or techniques for generating projections images of an object(s) under examination via a CT system are provided. A surface about which the projection image is focused is defined and data yielded from vertical rays of radiation intersecting the surface and data yielded from non-vertical rays intersecting the surface are used to generate the projection image. In some embodiments, the projection image is assembled from one or more projection lines, which are respectively associated with a line-path contacting the surface and extend in a direction parallel to an axis of rotation for a radiation source. A projection line is indicative of a degree of attenuation experienced by rays intersecting a line-path associated with the projection line and emitted while the radiation source was at a particular segment of a radiation source trajectory.

Projection image generation via computed tomography system

Z-effective (e.g., atomic number) values are generated for one or more sets of voxels in a CT density image using sparse (measured) multi-energy projection data. Voxels in the CT density image are assigned a starting z-effective value, causing a CT z-effective image to be generated from the CT density image. The accuracy of the assigned z-effective values is tested by forward projecting the CT z-effective image to generate synthetic multi-energy projection data and comparing the synthetic multi-energy projection data to the sparse multi-energy projection data. When the measure of similarity between the synthetic data and the sparse data is low, the z-effective value assigned to one or more voxels is modified until the measure of similarity is above a specified threshold (e.g., with an associated confidence score), at which point the z-effective values substantially reflect the z-effective values that would be obtained using a (more expensive) dual-energy CT imaging modality.

Determination of z-effective value for set of voxels using CT density image and sparse multi-energy data

One or more systems and/or techniques are provided to identify and/or classify objects of interest (e.g., potential granular objects) from a radiographic examination of the object. Image data of the object is transformed using a spectral transformation, such as a Fourier transformation, to generate image data in a spectral domain. Using the image data in the spectral domain, one or more one-dimensional spectral signatures can be generated and features of the signatures can be extracted and compared to features of one or more known objects. If one or more features of the signatures correspond (e.g., within a predetermined tolerance) to the features of a known object to which the feature(s) is compared, the object of interest may be identified and/or classified based upon the correspondence.

Object identification using sparse spectral components

Radiation flux can be adjusted 'on the fly' as an object () is being scanned in a security examination apparatus. Adjustments are made to the radiation flux based upon radiation incident on a first radiation detector () in an upstream portion () of an examination region. The object under examination is thus exposed to different radiation flux in coordination with a downstream motion () of the object relative to a second radiation detector (). The radiation flux is adjusted so that a sufficient number of x-rays (that traverse the object) are incident on the second radiation detector. Images of the object can then be generated based upon data from the second radiation detector, where these images are thus of a desired/higher quality.

Radiation modulation in a security examination apparatus

A projection image of an object is colored using three-dimensional image data. This may be particularly useful in radiographic imaging applications, for example. In one embodiment, a colored synthetic image is rendered from a colored three-dimensional image of an object, and color components of pixels of the synthetic image are used to determine color components, or color values, for corresponding pixels of a projection image depicting a similar view of the object as the synthetic image. In this way, the two-dimensional projection image is colored similarly to the colored three-dimensional image. For example, the projection image may be colored based upon density (if the three-dimensional image is colored based upon density) so aspects of the object that attenuate a similar amount of radiation but have different densities may be colored differently.

Two-dimensional colored projection image from three-dimensional image data

Representations of an object can comprise two or more separate sub-objects, producing a compound object. Compound objects can affect the quality of object visualization and threat identification. As provided herein, a compound object can be separated into sub-objects based on object morphological properties (e.g., an object's shape, surface area). Further, a potential compound object can be split into sub-objects, for example, eroding one or more outer layers of volume space (e.g., voxels) from the potential compound object. Additionally, a volume of a representation of the sub-objects in an image can be reconstructed, for example, by generating sub-objects that have a combined volume approximate to that of the compound object. Furthermore, sub-objects, which can be parts of a same physical object, but may have been erroneously split, can be identified and merged using connectivity and compactness based techniques.

Compound object separation

Representations of an object in an image generated by an imaging apparatus can comprise two or more separate sub-objects, producing a compound object. Compound objects can negatively affect the quality of object visualization and threat identification performance. As provided herein, a compound object can be separated into sub-objects. Topology score map data, representing topological differences in the potential compound object, may be computed and used in a statistical distribution to identify modes that may be indicative of the sub-objects. The identified modes may be assigned a label and a voxel of the image data indicative of the potential compound object may be relabeled based on the label assigned to a mode that represents data corresponding to properties of a portion of the object that the voxel represents to create image data indicative of one or more sub-objects.

Techniques and/or systems for generating a two-dimensional projection image of an object under examination from helical data are provided herein. An image plane and a distance, or height, of an examination line lying in a plane parallel to the image plane may be selected with or without user input. Using the selected image plane and examination line, data may be extracted from one or more views indicative of the object. The data that is extracted from the respective views is generally indicative of rays that traverse the examination line and have a trajectory that meets predetermined criteria. Using the extracted data from a plurality of views, one or more projection lines that are substantially parallel to a corresponding image slice are produced and a two-dimensional projection image is generated.

Growing community of inventors

Brookline, MA, United States of America

Sergey Simanovsky