Apparatus and method for detecting concealed objects in computed tomography data

• US 6,195,444 B1
• Filed: 01/12/1999
• Issued: 02/27/2001
• Est. Priority Date: 01/12/1999
• Status: Expired due to Term

• Current Assignee: Analogic Corporation
• Original Assignee: Analogic Corporation
• Inventors: Crawford, Carl R., Simanovsky, Sergey, Bechwati, Ibrahim M., Hiraoglu, Muzzafer
• Primary Examiner(s): Johns, Andrew W.
• Application Number:
  US09/228,380
• Time in Patent Office: 777 Days
• Field of Search: 382/100, 382/131, 382/154, 382/173, 382/256, 382/270, 378/4, 378/8, 378/20, 378/54, 378/57, 378/62, 378/901
• US Class Current: 382/100
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 2201/05 Recognition of patterns representing particular kinds of hidden objects, e.g. weapons, explosives, drugs

Abstract

A method and apparatus for detecting concealed objects in computed tomography data are disclosed. Sheet-shaped objects such as sheet explosives can be detected by a CT scanning system, in particular, a CT baggage scanning system. The invention analyzes CT voxels in a subregion in proximity to the sheet object to determine if the sheet object is concealed in an electronic device or is “sandwiched” within an item such as a book or magazine. To detect electronic concealment, the number of voxels in a subregion that contains the object having a density above a predetermined threshold is counted and the ratio of that number of voxels to the number of object voxels is computed. If the ratio exceeds a threshold, then it is concluded that the object is concealed in electronics. In response, the CT scanning system can alter discrimination parameters to allow the object to be classified as a threat. For “sandwich” concealment, layers on opposite sides of a sheet object are examined. The mean and standard deviation of density values for the voxels are computed. Where the mean density exceeds a predetermined threshold and the standard deviation is below a different threshold, for at least one of the layers, then it is concluded that the sheet object is sandwiched within an innocuous object such as a magazine or a book.

Claims

1. A method of processing computed tomography (CT) data for a region comprising:

identifying a plurality of volume elements in the CT data for the region, each volume element being associated with a density value;

automatically identifying volume elements in the CT data associated with an object in the region;

automatically identifying at least one subregion in proximity to the object;

automatically calculating at least one property of the subregion; and

processing data for the object based on the calculated property of the subregion.

2. The method of claim 1 wherein processing the object based on the calculated property of the subregion comprises altering a discrimination parameter used to classify the object as to whether it poses a threat.

3. The method of claim 2 wherein an altered discrimination parameter is a mass threshold.

4. The method of claim 1 wherein processing the object based on the calculated property of the subregion comprises classifying the object as to whether it poses a threat.

5. The method of claim 1 wherein the at least one subregion contains the object.

6. The method of claim 1 wherein the at least one subregion is a rectangular subregion containing the object.

7. The method of claim 1 wherein the at least one subregion is a rectangular subregion.

8. The method of claim 7 wherein the rectangular subregion has planar edges that are parallel to planes containing the orthogonal axes of a rectangular coordinate system of a CT scanning system used to obtain the CT data for the region.

9. The method of claim 7 wherein the rectangular subregion has planar edges that are parallel to planes containing the orthogonal axes of a rectangular coordinate system of the object.

10. The method of claim 9 wherein the rectangular coordinate system of the object is determined using a principal component analysis.

11. The method of claim 7 wherein the rectangular subregion is the smallest rectangular subregion that contains the object.

12. The method of claim 7 wherein the rectangular subregion is larger than the object.

13. The method of claim 1 wherein identifying at least one subregion comprises expanding a volume of the object.

14. The method of claim 1 wherein the object has one dimension that is substantially smaller than the other two dimensions.

15. The method of claim 14 wherein the at least one subregion is adjacent to the object along the smallest dimension of the object.

16. The method of claim 15 further comprising:

calculating the mean of the density values of volume elements associated with the at least one subregion; and

calculating the standard deviation of the density values of volume elements associated with the at least one subregion.

17. The method of claim 16 further comprising:

comparing the calculated mean of the density values to a first threshold;

comparing the calculated standard deviation of the density values to a second threshold; and

processing data for the object based on the comparisons.

18. The method of claim 17 wherein processing data for the object comprises altering a discrimination parameter used to classify the object as to whether it poses a threat.

19. The method of claim 17 wherein the object is classified as a threat if the calculated standard deviation is below the second threshold.

20. The method of claim 19 wherein the object is classified as a threat if the calculated mean is above the first threshold.

21. The method of claim 1 further comprising counting the number of volume elements in the at least one subregion that have density values within a predetermined range of density values.

22. The method of claim 21 further comprising:

comparing the number of volume elements in the at least one subregion that have density values within the predetermined range of density values to the number of volume elements in the object; and

processing data for the object based on the comparison.

23. The method of claim 22 wherein the predetermined range of density values is selected based on a known density of one or more components of electronic equipment.

24. The method of claim 22 wherein processing data for the object comprises altering a discrimination parameter used to classify the object as to whether it poses a threat.

25. The method of claim 24 wherein an altered discrimination parameter is a mass threshold.

26. The method of claim 22 wherein processing data for the object comprises classifying the object as to whether it poses a threat.

27. The method of claim 21 further comprising:

comparing the number of volume elements in the at least one subregion that have density values within the predetermined range of density values to the number of volume elements in the subregion; and

processing data for the object based on the comparison.

28. The method of claim 27 wherein the predetermined range of density values is selected based on a known density of one or more components of electronic equipment.

29. The method of claim 27 wherein processing data for the object comprises altering a discrimination parameter used to classify the object as to whether it poses a threat.

30. The method of claim 29 wherein an altered discrimination parameter is a mass threshold.

31. The method of claim 27 wherein processing data for the object comprises classifying the object as to whether it poses a threat.

32. An apparatus for processing computed tomography (CT) data for a region comprising:

a receiver for receiving the CT data for the region; and

a data processor adapted to;

automatically identify a plurality of volume elements in the CT data for the region, each volume element being associated with a density value;

automatically identify volume elements in the CT data associated with an object in the region;

automatically identify at least one subregion in proximity to the object;

calculate at least one property of the subregion; and

process data for the object based on the calculated property of the subregion.

33. The apparatus of claim 32 wherein, in processing data for the object, the processor alters a discrimination parameter used to classify the object as to whether it poses a threat.

34. The apparatus of claim 32 wherein, in processing data for the object, the processor classifies the object as to whether it poses a threat.

35. The apparatus of claim 32 wherein the at least one subregion contains the object.

36. The apparatus of claim 32 wherein the at least one subregion is a rectangular subregion containing the object.

37. The apparatus of claim 32 wherein the at least one subregion is a rectangular subregion.

38. The apparatus of claim 37 wherein the rectangular subregion has planar edges that are parallel to planes containing the orthogonal axis of a rectangular coordinate system of a CT scanning system used to obtain the CT data for the region.

39. The apparatus of claim 37 wherein the rectangular subregion has planar edges that are parallel to planes containing the orthogonal axis of a rectangular coordinate system of the object.

40. The apparatus of claim 39 wherein the rectangular coordinate system of the object is determined using a principle component analysis.

41. The apparatus of claim 37 wherein the rectangular subregion is the smallest rectangular subregion that contains the object.

42. The apparatus of claim 37 wherein the rectangular subregion is larger than the object.

43. The apparatus of claim 32 wherein the at least one subregion is identified by expanding a volume of the object.

44. The apparatus of claim 32 wherein the object has one dimension that is substantially smaller than the other two dimensions.

45. The apparatus of claim 44 wherein the at least one subregion is adjacent to the object along the smallest dimension of the object.

46. The apparatus of claim 45 wherein the data processor (i) calculates the mean of the density values of volume elements associated with the at least one subregion, and (ii) calculates the standard deviation of the density values of volume elements associated with the at least one subregion.

47. The apparatus of claim 46 wherein the data processor (i) compares the calculated mean of the density values to a first threshold, (ii) compares the calculated standard deviation of the density values to a second threshold, and (iii) processes data for the object based on the caparisons.

48. The apparatus of claim 47 wherein the data processor classifies the object as a threat if the calculated standard deviation is below the second threshold.

49. The apparatus of claim 48 wherein the data processor classifies the object as a threat if the calculated mean is above the first threshold.

50. The apparatus of claim 32 wherein the data processor counts the number of volume elements in the at least one subregion that have density values within a predetermined range of density values.

51. The apparatus of claim 50 wherein the data processor (i) compares the number of volume elements in the at least one subregion that have the density values within a predetermined range of density values to the number of volume elements in the object, and (ii) processes data for the object based on the comparison.

52. The apparatus of claim 51 wherein the predetermined range of density values is selected based on a known density of one or more components of electronic equipment.

53. The apparatus of claim 50 wherein the data processor (i) compares the number of volume elements in the at least one subregion that have the density values within a predetermined range of density values to the number of volume elements in the subregion and (ii) processes data for the object based on the comparison.

54. The apparatus of claim 53 wherein the predetermined range of density values is selected based on a known density of one or more components of electronic equipment.