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

• US 7,302,083 B2
• Filed: 07/01/2004
• Issued: 11/27/2007
• Est. Priority Date: 07/01/2004
• Status: Active Grant

• Current Assignee: Analogic Corporation
• Original Assignee: Analogic Corporation
• Inventors: Crawford, Carl R., Simanovsky, Sergey, Ying, Zhengrong, Larson, Gregory L.
• Primary Examiner(s): Mehta; Bhavesh M
• Assistant Examiner(s): KIM, CHONG R
• Application Number:
  US10/883,199
• Publication Number:
  US 20060002585A1
• Time in Patent Office: 1,244 Days
• Field of Search: None
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30112 Baggage; Luggage; Suitcase
• CPC Class Codes: G06T 7/0004 Industrial image inspection

Abstract

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.

Claims

1. A method of sharp object detection represented in three-dimensional reconstructed computed tomography data, wherein the three-dimensional tomography data includes a plurality of voxels, comprising:

A. Identifying voxels corresponding to individual objects;

B. Performing an eigen-analysis and generating an eigen-projection of the voxels corresponding to an identified object;

C. Calculating a sharpness score of the identified object; and

D. Declaring the identified object as a sharp object if the sharpness score is greater than a pre-defined threshold.

2. The method of claim 1, wherein Step C includes calculating a pointness measurement of the identified object.

3. The method of claim 2, wherein calculating a pointness measurement of the identified object comprises:

A. Calculating a one-dimensional profile from the eigen-projection;

B. Computing a cumulative profile;

C. Calculating an extent of the one-dimensional profile;

D. Calculating two intercepts relative to the profile extent corresponding to 25% and 75% of the cumulative profile; and

E. Calculating the pointness measurement by finding the larger of the two intercepts.

4. The method of claim 1, wherein Step C includes calculating a flat area of the identified object.

5. The method of claim 4, wherein calculating a flat area of identified object comprises:

A. Calculating a minimal coordinate matrix and a maximal coordinate matrix;

B. Filtering the minimal and maximal coordinate matrices;

C. Computing the extent projection from the filtered minimal and maximal coordinate matrices; and

D. Computing a flat area from the extent projection.

6. The method of claim 1, wherein Step C includes calculating an axial concavity ratio of the identified object.

7. The method of claim 6, wherein calculating an axial concavity ratio of the identified object comprises:

A. Binarizing the eigen-projection image;

B. Calculating an object area;

C. Calculating an included area;

D. Calculating an axial concave area; and

E. Computing the axial concavity ratio.

8. A system for sharp object detection represented in three-dimensional reconstructed computed tomography data, wherein the three-dimensional tomography data includes a plurality of voxels, comprising:

A. A subsystem constructed and arranged so as to identify voxels corresponding to individual objects;

B. A subsystem constructed and arranged so as to perform an eigen-analysis and generate an eigen-projection of the voxels corresponding to an identified object;

C. A subsystem constructed and arranged so as to calculate a sharpness score of the identified object; and

D. A subsystem constructed and arranged so as to declare the identified object as a sharp object if the sharpness score is greater than a pre-defined threshold.

9. The system of claim 8, wherein the subsystem C includes a module constructed and arranged so as to calculate a pointness measurement of the identified object.

10. The system of claim 9, wherein the module for calculating a pointness measurement of the identified object comprises:

A. An element constructed and arranged so as to calculate a one-dimensional profile from the eigen-projection;

B. An element constructed and arranged so as to compute a cumulative profile;

C. An element constructed and arranged so as to calculate an extent of the one-dimensional profile;

D. An element constructed and arranged so as to calculate two intercepts relative to the profile extent corresponding to 25% and 75% of the cumulative profile; and

E. An element constructed and arranged so as to calculate the pointness measurement by finding the larger of two intercepts.

11. The system of claim 8, wherein the subsystem C includes a module constructed and arranged so as to calculate a flat area of the identified object.

12. The system of claim 11, wherein the module for calculating a flat area of identified object comprises:

A. An element constructed and arranged so as to calculate a minimal coordinate matrix and a maximal coordinate matrix;

B. An element constructed and arranged so as to filter the minimal and maximal coordinate matrices;

C. An element constructed and arranged so as to compute the extent projection from the filtered minimal and maximal coordinate matrices; and

D. An element constructed and arranged so as to compute a flat area from the extent projection.

13. The system of claim 8, wherein the subsystem C includes a module constructed and arranged so as to calculate an axial concavity ratio of the identified object.

14. The system of claim 13, wherein the module for calculating an axial concavity ratio of the identified object comprises:

A. An element constructed and arranged so as to binarize the eigen-projection image;

B. An element constructed and arranged so as to calculate an object area;

C. An element constructed and arranged so as to calculate an included area;

D. An element constructed and arranged so as to calculate an axial concave area; and

E. An element constructed and arranged so as to compute the axial concavity ratio.