System and method for detecting items of interest through mass estimation

1. A method for estimating a mass of an object, said method comprising:

acquiring image data including a plurality of image elements;

calculating a histogram based on the image data;

calculating a computed tomography (CT) number of the object using an anisotropic erosion operator;

determining a perimeter of the object;

calculating an estimated mass of the object using the CT number and a first subset of image elements of the plurality of image elements, the first subset of image elements defined by the perimeter of the object; and

outputting at least one of the estimated mass of the object and an image including the object.

2. A method in accordance with claim 1 , further comprising segmenting the acquired image data into a plurality of image segments.

3. A method in accordance with claim 2 , wherein segmenting the image data comprises comparing each image element of the plurality of image elements to a selected threshold value.

4. A method in accordance with claim 1 , wherein calculating a histogram based on the image data comprises applying a beam hardening correction factor to the histogram.

5. A method in accordance with claim 1 , wherein calculating a CT number of the object comprises applying the anisotropic erosion operator using at least one iteration, a number of iterations is based on at least one of a mean pixel size of the plurality of pixels and a size of an X-ray detector having generated the image data.

6. A method in accordance with claim 1 , wherein determining a perimeter of the object comprises determining a second subset of image elements of the plurality of image elements, the perimeter defined by a boundary between the first subset of image elements and the second subset of image elements.

7. A method in accordance with claim 6 , further comprising:

applying a dilation operator to the first subset of image elements; and

calculating a CT number of the second subset of image elements.

8. A method in accordance with claim 6 , wherein determining a perimeter of the object further comprises:

determining a third subset of image elements of the plurality of image elements, each image element of the third subset of image elements is influenced by partial volume effects due to at least one of the first subset of image elements and the second subset of image elements;

comparing each image element of the third subset of image elements to the histogram; and

redefining each image element of the third subset of image elements as an image element of one of the first subset of image elements and the second subset of image elements.

9. A system for estimating a mass of an object within a container, said system comprising:

a data collection system; and

a post-detection classification system operatively coupled to said data collection system, said post-detection classification system configured to:

acquire image data representing an image, the image data including a plurality of image elements;

calculate a histogram based on the image data;

calculate a computed tomography (CT) number of the object using an anisotropic erosion operator;

determine a perimeter of the object;

calculate an estimated mass of the object using the CT number and a first subset of image elements of the plurality of image elements, the first subset of image elements defined by the perimeter of the object; and

output at least one of the estimated mass of the object and an image including the object.

10. A system in accordance with claim 9 , wherein said post-detection classification system is further configured to segment the acquired image data into a plurality of image segments.

11. A system in accordance with claim 9 , wherein said post-detection classification system is further configured to calculate a histogram based on the image data by applying a beam hardening correction factor to the histogram.

12. A system in accordance with claim 9 , wherein said post-detection classification system is further configured to calculate the CT number by applying the anisotropic erosion operator using at least one iteration, a number of iterations is based on a mean image element size of the plurality of image elements and a size of an X-ray detector having generated the image data.

13. A system in accordance with claim 9 , wherein said post-detection classification system is further configured to:

determine a perimeter of the object by determining a second subset of pixels of the plurality of image elements, the perimeter defined by a boundary between the first subset of image elements and the second subset of image elements, the second subset of image elements is determined by applying a dilation operator to the first subset of image elements; and

calculate a CT number of the second subset of image elements.

14. A system in accordance with claim 13 , wherein said post-detection classification system is further configured to:

determine a third subset of image elements of the plurality of image elements, each image element of the third subset of image elements is influenced by partial volume effects due to at least one of the first subset of image elements and the second subset of image elements;

compare each image element of the third subset of image elements to the histogram; and

redefine each image element of the third subset of image elements as an image element of one of the first subset of image elements and the second subset of image elements.

15. A computer program embodied on a non-transitory computer-readable medium, said computer program comprising a code segment that configures a processor to:

acquire image data representing an image, the image data including a plurality of calculate a histogram based on the image data;

calculate a computed tomography (CT) number of the object using an anisotropic erosion operator;

determine a perimeter of the object;

calculate an estimated mass of the object using the CT number and a first subset of image elements of the plurality of image elements, the first subset of image elements defined by the perimeter of the object; and

output at least one of the estimated mass of the object and an image including the object.

16. A computer program embodied on a non-transitory computer-readable medium in accordance with claim 15 , wherein the code segment further configures the processor to segment the acquired image data into a plurality of image segments.

17. A computer program embodied on a non-transitory computer-readable medium in accordance with claim 15 , wherein the code segment further configures the processor to calculate a histogram based on the image data by applying a beam hardening correction factor to the histogram.

18. A computer program embodied on a non-transitory computer-readable medium in accordance with claim 15 , wherein the code segment further configures the processor to calculate the CT number by applying the anisotropic erosion operator using at least one iteration, a number of iterations is based on a mean image element size of the plurality of image elements and a size of an X-ray detector having generated the image data.

19. A computer program embodied on a non-transitory computer-readable medium in accordance with claim 15 , wherein the code segment further configures the processor to:

determine a perimeter of the object by determining a second subset of image elements of the plurality of image elements, the perimeter defined by a boundary between the first subset of image elements and the second subset of image elements, the second subset of image elements is determined by applying a dilation operator to the first subset of image elements; and

calculate a CT number of the second subset of image elements.

20. A computer program embodied on a non-transitory computer-readable medium in accordance with claim 19 , wherein the code segment further configures the processor to:

determine a third subset of image elements of the plurality of image elements, each image element of the third subset of image elements is influenced by partial volume effects of at least one of the first subset of image elements and the second subset of image elements;

compare each image element of the third subset of image elements to the histogram; and

redefine each image element of the third subset of image elements as an image element of one of the first subset of image elements and the second subset of image elements.