Claims
- 1. A computer-implemented method for automatically detecting shapes in a medical image, comprising:
a) locating a surface in said medical image; b) generating a plurality of normal vectors to said surface; and c) identifying at least one intersection or near intersection of said normal vectors.
- 2. The method as set forth in claim 1, wherein said identifying further comprises identifying image voxels having large numbers of intersecting or nearly intersecting normal vectors.
- 3. The method as set forth in claim 1, wherein said medical image is a computed tomography image.
- 4. The method as set forth in claim 1, wherein said shapes are nodules.
- 5. The method as set forth in claim 1, wherein said shapes are lesions.
- 6. The method as set forth in claim 1, wherein said shapes are polyps.
- 7. The method as set forth in claim 1, wherein said shapes comprise pre-cancerous cells.
- 8. The method as set forth in claim 1, wherein said shapes are cancerous cells.
- 9. The method as set forth in claim 1, wherein said locating a surface further comprises pre-processing said medical image.
- 10. The method as set forth in claim 1, wherein said locating a surface further comprises segmenting said medical image.
- 11. The method as set forth in claim 1, wherein said generating a plurality of normal vectors further comprises applying gradient edge detection.
- 12. The method as set forth in claim 1, further comprising scaling of said plurality of normal vectors.
- 13. The method as set forth in claim 12, wherein said scaling comprises scaling the length of said plurality of normal vectors.
- 14. The method as set forth in claim 12, wherein said scaling comprises scaling the width of said plurality of normal vectors.
- 15. The method as set forth in claim 12, wherein said scaling is dependent on the type of said shapes.
- 16. The method as set forth in claim 12, wherein said scaling comprises a convolution of a gaussian distribution to said plurality of normal vectors.
- 17. The method as set forth in claim 1, wherein said detection of shapes is optimized for high detection sensitivity and high false positive elimination.
- 18. A computer-implemented method for automatically detecting shapes in a computed tomography medical image, comprising:
(a) locating a surface in said computed tomography medical image; (b) generating a plurality of normal vectors to said surface, wherein said plurality of normal vectors are scaled according to the type of said shapes; and (c) identifying at least one intersection or near intersection of said normal vectors.
- 19. The method as set forth in claim 1, wherein said identifying further comprises identifying image voxels having large numbers of intersecting or nearly intersecting normal vectors.
- 20. The method as set forth in claim 1, wherein said shapes are nodules.
- 21. The method as set forth in claim 1, wherein said shapes are lesions.
- 22. The method as set forth in claim 1, wherein said shapes are polyps.
- 23. The method as set forth in claim 1, wherein said shapes comprise pre-cancerous cells.
- 24. The method as set forth in claim 1, wherein said shapes are cancerous cells.
- 25. The method as set forth in claim 1, wherein said locating a surface further comprises pre-processing said computed tomography medical image.
- 26. The method as set forth in claim 1, wherein said locating a surface further comprises segmenting said computed tomography medical image.
- 27. The method as set forth in claim 1, wherein said generating a plurality of normal vectors further comprises applying gradient edge detection.
- 28. The method as set forth in claim 1, wherein said scaling comprises scaling the length of said plurality of normal vectors.
- 29. The method as set forth in claim 1, wherein said scaling comprises scaling the width of said plurality of normal vectors.
- 30. The method as set forth in claim 1, wherein said scaling comprises a convolution of a gaussian distribution to said plurality of normal vectors.
- 31. The method as set forth in claim 1, wherein said detection of shapes is optimized for high detection sensitivity and high false positive elimination.
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is cross-referenced to and claims priority from U.S. Provisional Applications No. 60/288,621 filed May 4, 2001 and No. 60/288,674 filed May 4, 2002, which are both hereby incorporated by reference. This application is also cross-referenced to co-pending U.S. patent application entitled “Method for characterizing shapes in medical images” filed with the USPTO on May 3, 2002, which is hereby incorporated by reference.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] The present invention was supported in part by grant number R01 CA72023 from the National Institutes of Health (NIH). The U.S. Government has certain rights in the invention.
Provisional Applications (2)
|
Number |
Date |
Country |
|
60288621 |
May 2001 |
US |
|
60288674 |
May 2001 |
US |