Analogic Corporation

Carl R Crawford

Sergey Simanovsky

Ibrahim M Bechwati

Zhengrong Ying

Muzaffer Hiraoglu

Christopher C Ruth

Ram Naidu

Matthew Waggener Hirsch

David A Schafer

Seemeen S Karimi

Eric M Bailey

Steven Neil Urchuk

Chitra Subramanian

Gregory L Larson

Muzzafer Hiraoglu

Kyle Guilbert

Hans J Weedon

John M Dobbs

Bernard M Gordon

Ruvin Deych

David Rozas

Basak Ulker Karbeyaz

Anton M Deykoon

Govindarajan T Srinivasan

Anuradha Aiyer

Nima Shokrollahi

Seemee S Karimi

Iouri Bachilov

Purvin Desai

Alexander I Greenberg

A method of and a system for automatic object display of volumetric CT data for fast on-screen threat resolution are disclosed, wherein the CT data includes a CT image in a single energy CT scanner, or a CT image and a Z image in a multi-energy CT scanner, and a label image defining each object as a plurality of voxels of the volumetric CT data. The method comprises generating volumetric CT image data corresponding to a scanned bag; performing automatic threat detection to generate a label image; processing the volumetric CT data and the label image to obtain visualization parameters for each object; automatically generating display images for each object using corresponding visualization parameters; and displaying the generated display images for on-screen threat resolution.

Method of and system for automatic object display of volumetric computed tomography images for fast on-screen threat resolution

A method of and a system for identifying objects using local distribution features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The local distribution features are first and second order statistics of the local distributions of the density and atomic number measurements of different portions of a segmented object. The local distributions are the magnitude images of the first order derivative of the CT image and the Z image. Each segmented object is also divided into different portions to provide geometrical information for discrimination. The method comprises preprocessing the CT and Z images, segmenting images into objects, computing local distributions of the CT and Z images, computing local distribution histograms, computing local distribution features from the said local distribution histograms, classifying objects based on the local distribution features.

Method of and system for classifying objects using local distributions of multi-energy computed tomography images

A method of and a system for variable pitch CT scanning for baggage screening and variable pitch image reconstruction are disclosed. The method comprises decelerating conveyor belt speed when additional time is needed to render a decision on a complex bag; accelerating conveyor belt speed to its normal speed when decisions are reached on undecided bags; generating cone-beam projection data at variable scanning pitch corresponding to variable conveyor belt speed; computing a tilt angle and a distance offset for each tilted slice using the pitch values at which the cone-beam projection data is acquired for that tilted slice; generating fan-beam projection data for each tilted slice using the tilted angle and the distance offset; generating correction projection data to compensate for the error between the source trajectory and the tilted reconstruction plane; generating the corrected fan-beam projection data by adding the correction projection data to the fan-beam projection data; reconstructing tilted slices using the corrected fan-beam projection data; and interpolating the reconstructed tilted slices into axial slices.

Method of and system for variable pitch computed tomography scanning for baggage screening

A method of and a system for displaying volumetric multi-energy CT images are disclosed, wherein a CT image, a Z image, and a label image from an automatic explosive detection are provided, are disclosed. The method comprises generating an index image through a nonlinear transformation of the CT image, the Z image, and the label image, rotating and coloring the index image as desired, and rendering and displaying the rotated and colored image.

Method of and system for 3D display of multi-energy computed tomography images

A method of and a system for splitting a compound object using multi-energy CT data including a density and an atomic number measurements are provided. The method comprises: compound object detection; computing a two-dimensional DZ distribution of a compound object; identifying clusters within the DZ distribution; assigning a component label to each object voxel based on the DZ distribution clusters; and post-processing the set of voxels identified as belonging to each component.

Method of and system for splitting compound objects in multi-energy computed tomography images

A method of and a system for identifying objects using histogram segment features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The method comprises: computing a density histogram for each potential threat object; smoothing the density histogram using a low-pass filter; identifying peaks in the smoothed density histogram; assigning a segment to each peak; computing histogram segment features for each segment; classifying each potential threat object into a threat or a non-threat using computed features.

Method of and system for classifying objects using histogram segment features of multi-energy computed tomography images

A method of and a system for destreaking the photoelectric image in multi-energy computed tomography are provided, wherein the photoelectric projections are generated from the projection data acquired using at least two x-ray spectra for scanned objects; wherein a neighboring scheme is provided; the method comprises computing the statistics including mean and standard deviation using the neighboring scheme; calculating an upper limit and a lower limit from the computed statistics; detecting outliers in the photoelectric projections using the upper and lower limits; and replacing values of outliers using the upper or lower limit.

Method of and system for destreaking the photoelectric image in multi-energy computed tomography

A method of and a system for detecting anomalies in projection images generated by CT scanners are provided. One type of anomaly of particular interest is bright or/and dark dots in projection images, which correspond to streak artifacts in the CT images. The method for detecting such bright or/and dark dots in projection images comprises: generating projection images; computing a CFAR distance map; computing a preliminary dot map; generating dot histograms; and detecting bright dots or/and dark dots based on the generated histograms.

Method of and system for detecting anomalies in projection images generated by computed tomography scanners

A method of and a system for extracting 3D bag images from continuously reconstructed 2D image slices are provided. The method detects the boundaries of baggage in the reconstructed images, and provides better flexibilities for threat detection and displaying. The method comprises detecting starting and ending slices using multiple slices, counting bag slices, splitting 3D bag images when maximum number of slices of a 3D bag image is reached, and creating overlapping slices for the split 3D bag images.

Method of and system for extracting 3D bag images from continuously reconstructed 2D image slices in computed tomography

A method of and a system for sharp object detection using computed tomography images are provided. The method comprises identifying voxels corresponding to individual objects; performing eigen-analysis and generating eigen-projection of an identified object; computing an axial concavity ratio of the identified object; computing a pointness measurement of the identified object; computing a flat area of the identified object; calculating a sharpness score of the identified object; and declaring the identified object as a threat if the sharpness score is greater than a pre-defined threshold.

Method of and system for sharp object detection using computed tomography images

A method of and system for detecting threat objects represented in 3D CT data uses knowledge of one or more predefined shapes of the threat objects. An object represented by CT data for a region is identified. A two-dimensional projection of the object along a principal axis of the object is generated. A contour of the object boundary in the projection image is computed. A shape histogram is computed from the extracted contour. A difference measure between the extracted shape histogram and a set of pre-computed threat shape histograms is computed. A declaration of a threat object is made if the difference measure is less than a pre-defined threshold.

Method and system for detecting threat objects using computed tomography images

A method of and a system for spectral correction in multi-energy computed tomography are provided to correct reconstructed images, including high-energy CT images and Z (effective atomic number) images, for spectral variations, which include time variations on a scanner due to HVPS drift and scanner to scanner variations due to the beamline component differences. The method uses a copper filter mounted on the detector array for tracking the spectral changes. The method comprises: generating copper ratios; computing working air tables; calculating scales and offsets; and correcting high-energy CT images and Z images using calculated scales and offsets. The method further includes an off-line calibration procedure to generate necessary parameters for the online correction.

Method of and system for X-ray spectral correction in multi-energy computed tomography

A method of performing bone correction on a computerized tomography (CT) image includes the steps of:

Adaptive iterative bone correction in computerized tomography

A method of decomposition of projection data is provided, wherein such projection data includes input projection data acquired using at least two x-ray spectra for a scanned object, including low energy projection data (P) and high energy projection data (P); the method comprises solving the projections Pand Pto determine a photoelectric line integral (A) component of attenuation and a Compton line integral (A) component of attenuation of the scanned object using a multi-step fitting procedure and constructing a Compton image Iand a photoelectric image Ifrom the Compton line integral and photoelectric line integral.

Decomposition of multi-energy scan projections using multi-step fitting

A method of and a system for computing Z (effective atomic number) images from projection data are provided, wherein the projections are acquired using at least two x-ray spectra for a set of scanned objects, including a set of low energy projections and a set of high energy projections; the method comprises decomposing the low energy projections and high energy projections into photoelectric projections, reconstructing the photoelectric projections into photoelectric images, reconstructing one of the two sets of projections into CT images, and computing Z images from the CT images and the photoelectric images with parameters obtained from a calibration procedure.

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Brookline, MA, United States of America

Carl R Crawford