Systems and methods for image processing

1. A computing-device implemented method for identifying an item in an x-ray image using at least one computing device equipped with a processor, the method comprising:

executing, using the at least one computing device, at least one machine-learned model, the at least one machine-learned model trained with at least one training data set of x-ray images, the at least one training data set including a first set of x-ray images of items containing threats and a second set of x-ray images of items not containing threats, the at least one machine-learned model is a convolutional neural network (CNN) and the first set of x-ray images or the second set of x-ray images include one or more volumetric x-ray images or one or more projection x-ray images;

receiving, with the at least one computing device, at least one rendered x-ray image that includes an item, the x-ray image having a 2-D TRI-MAT format that identifies items in the image as organic, inorganic or metallic;

identifying, with the at least one computing device, the item using the at least one model; and

generating, with the at least one computing device, an automated detection indication associated with the item.

2. The method of claim 1 , wherein the at least one training data set includes a first set of x-ray images containing a specified item and second set of x-ray images not containing the specified item.

3. The method of claim 1 , wherein the automated detection indication is based at least in part on machine-learned features of the item that are based on the at least one training data set.

4. The method of claim 1 , wherein the machine-learned model includes weights, coefficients, and forms learned by analysis of the at least one training data set.

5. The method of claim 1 , wherein the automated detection indication includes a visual indication of a location of the item within the at least one rendered x-ray image.

6. The method of claim 1 , wherein the automated detection indication includes a label within the at least one rendered x-ray image identifying the item.

7. The method of claim 1 , further comprising:

determining, with the at least one computing device, whether the item contains a threat or does not contain a threat based on the at least one machine-learned model; and

generating, with the at least one computing device, the automated detection indication when the item contains a threat.

8. The method of claim 7 , wherein the automated detection indication includes at least one of a visual indication and an auditory alert when the item contains a threat.

9. The method of claim 1 , further comprising:

determining, with the at least one computing device, whether the item contains a threat or does not contain a threat based on the at least one model; and

generating, with the at least one computing device, the automated detection indication when the item does not contain a threat.

10. The method of claim 1 , further comprising:

transmitting, with the at least one computing device, the automated detection indication to at least one of a baggage handling system or an operator in real-time or near real-time.

11. The method of claim 1 , wherein the at least one training data set includes x-ray images of items obtained using an x-ray screening machine.

12. The method of claim 1 , wherein the at least one rendered x-ray image is based on scanning at least one object at a security checkpoint in a facility.

13. The method of claim 1 , wherein the at least one rendered x-ray image is at least one 2-D projection image.

14. The method of claim 1 , wherein the at least one rendered x-ray image is at least one 3-D image of density, stopping power, or atomic composition.

15. The method of claim 1 , wherein the item is a laptop, a bottle, a shoe, or a personal electronic device.

16. The method of claim 1 , further comprising:

determining, with the at least one computing device, that the item is a common false-alarm object for explosive detection systems.

17. The method of claim 1 , further comprising:

training, with the at least one computing device, the machine learning algorithm using a result of a previously-executed explosive detection system.

18. A system for identifying an item in an x-ray image, the system comprising:

a scanner configured to render at least one x-ray image;

a computing device equipped with a processor in communication with the scanner, the computing device configured to:

execute at least one machine-learned model, the at least one machine-learned model trained with at least one training data set of x-ray images, the at least one training data set including a first set of x-ray images of items containing threats and a second set of x-ray images of items not containing threats, the at least one machine-learned model being is a convolutional neural network (CNN), and the first set of x-ray images or the second set of x-ray images include one or more volumetric x-ray images or one or more projection x-ray images;

receive the at least one rendered x-ray image from the scanner, the at least one rendered x-ray image includes an item, the x-ray image having a 2-D TRI-MAT format that identifies items in the image as organic, inorganic or metallic;

identify the item using the at least one model; and

generate an automated detection indication associated with the item.

19. The system of claim 18 , wherein the at least one training data set includes a first set of x-ray images containing a specified item and second set of x-ray images not containing the specified item.

20. The system of claim 18 , wherein the automated detection indication is based at least in part on machine-learned features of the item that are based on the at least one training data set.

21. The system of claim 18 , wherein the machine-learned model includes weights, coefficients, and forms learned by analysis of the at least one training data set.

22. The system of claim 18 , wherein the automated detection indication includes a visual indication of a location of the item within the at least one rendered x-ray image.

23. The system of claim 18 , wherein the automated detection indication includes a label within the at least one rendered x-ray image identifying the item.

24. The system of claim 18 , the computing device further configured to:

determine whether the item contains a threat or does not contain a threat based on the at least one machine-learned model; and

generate the automated detection indication when the item contains a threat.

25. The system of claim 24 , wherein the automated detection indication includes at least one of a visual indication and an auditory alert when the item contains a threat.

26. The system of claim 18 , the computing device further configured to:

determine whether the item contains a threat or does not contain a threat based on the at least one model; and

generate the automated detection indication when the item does not contain a threat.

27. The system of claim 18 , the computing device further configured to transmit the automated detection indication to at least one of a baggage handling system or an operator in real-time or near real-time.

28. The system of claim 18 , wherein the at least one training data set includes x-ray images of items obtained using an x-ray screening machine.

29. The system of 19 , wherein the at least one rendered x-ray image is based on scanning at least one object at a security checkpoint in a facility.

30. The system of claim 18 , wherein the at least one rendered x-ray image is at least one 2-D projection image.

31. The system of claim 18 , wherein the at least one rendered x-ray image is at least one 3-D image of density, stopping power, or atomic composition.

32. The system of claim 18 , wherein the item is a laptop, a bottle, a shoe, or a personal electronic device.

33. The system of claim 18 , the computing device further configured to determine that the item is a common false-alarm object for explosive detection systems.

34. The system of claim 18 , the computing device further configured to train the machine learning algorithm using a result of a previously-executed explosive detection system.

35. A non-transitory computer readable medium storing instructions executable by a processor, wherein execution of the instructions causes the processor to implement a method for identifying an item in an x-ray image, the method comprising:

executing at least one machine-learned model, the at least one machine-learned model trained with at least one training data set of x-ray images, the at least one training data set including a first set of x-ray images of items containing threats and a second set of x-ray images of items not containing threats, the at least one machine-learned model is a convolutional neural network (CNN) and the first set of x-ray images or the second set of x-ray images include one or more volumetric x-ray images or one or more projection x-ray images;

receiving at least one rendered x-ray image that includes an item, the x-ray image having a 2-D TRI-MAT format that identifies items in the image as organic, inorganic or metallic;

identifying the item using the at least one model; and

generating an automated detection indication associated with the item.

36. The non-transitory computer readable medium of 35 , wherein the at least one training data set includes a first set of x-ray images containing a specified item and second set of x-ray images not containing the specified item.

37. The non-transitory computer readable medium of 35 , wherein the automated detection indication is based at least in part on machine-learned features of the item that are based on the at least one training data set.

38. The non-transitory computer readable medium of 35 , wherein the machine-learned model includes weights, coefficients, and forms learned by analysis of the at least one training data set.

39. The non-transitory computer readable medium of claim 35 , wherein the automated detection indication includes a visual indication of a location of the item within the at least one rendered x-ray image.

40. The non-transitory computer readable medium of claim 35 , wherein the automated detection indication includes a label within the at least one rendered x-ray image identifying the item.

41. The non-transitory computer readable medium of claim 35 , the execution of the instructions further causes the processor to:

determine whether the item contains a threat or does not contain a threat based on the at least one machine-learned model; and

generate the automated detection indication when the item contains a threat.

42. The non-transitory computer readable medium of claim 41 , wherein the automated detection indication includes at least one of a visual indication and an auditory alert when the item contains a threat.

43. The non-transitory computer readable medium of claim 35 , the execution of the instructions further causes the processor to:

determine whether the item contains a threat or does not contain a threat based on the at least one model; and

generate the automated detection indication when the item does not contain a threat.

44. The non-transitory computer readable medium of claim 35 , wherein the execution of the instructions further causes the processor to transmit the automated detection indication to at least one of a baggage handling system or an operator in real time or near real time.

45. The non-transitory computer readable medium of claim 35 , wherein the at least one training data set includes x-ray images of items obtained using an x-ray screening machine.

46. The non-transitory computer readable medium of claim 35 , wherein the at least one rendered x-ray image is based on scanning at least one object at a security checkpoint in a facility.

47. The non-transitory computer readable medium of claim 35 , wherein the at least one rendered x-ray image is at least one 2-D projection image.

48. The non-transitory computer readable medium of claim 35 , wherein the at least one rendered x-ray image is at least one 3-D image of density, stopping power, or atomic composition.

49. The non-transitory computer readable medium of claim 35 , wherein the item is a laptop, a bottle, a shoe, or a personal electronic device.

50. The non-transitory computer readable medium of claim 35 , wherein the execution of the instructions further causes the processor to determine that the item is a common false-alarm object for explosive detection systems.

51. The non-transitory computer readable medium of claim 35 , wherein the execution of the instructions further causes the processor to train the machine learning algorithm using a result of a previously-executed explosive detection system.