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

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.