System and Method for Detection of Lesions in Three-Dimensional Digital Medical Image

• US 20100220913A1
• Filed: 02/26/2010
• Published: 09/02/2010
• Est. Priority Date: 02/27/2009
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Medicsight PLC (MGT Capital Investments Incorporated)
• Inventors: Ye, Xujiong, Siddique, Muhammad Musib, Boyes, Richard Graham, SLABAUGH, Gregory Gibran, Douiri, Abdel
• Granted Patent: US 9,014,447 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30032 Colon polyp
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A system and method for characterizing a point in a three-dimensional digital medical image includes determining third order derivatives of the three-dimensional digital medical image at one or more points in the three-dimensional digital medical image. At each of the one or more points, one or more invariant or semi-invariant features that characterize a local geometry at the point is determined using the third order derivatives of the three-dimensional digital medical image. The invariant or semi-invariant features are used for automatic detection of lesions, abnormalities, and other anatomical structures of interest in the three-dimensional digital medical image.

Claims

1. A method for characterizing a point in a three-dimensional digital medical image, the method comprising:

determining, on a computing device, third order derivatives of the three-dimensional digital medical image at one or more points within the three-dimensional digital medical image;

determining on the computing device, at each of the one or more points, using the third order derivatives of the three-dimensional digital medical image, one or more invariant features that characterize a local geometry at the point; and

characterizing each of the one or more points using the one or more determined invariant features.

2. The method of claim 1, wherein said determining one or more invariant features comprises determining covariant derivatives using the third order derivatives of the three-dimensional digital medical image.

3. The method of claim 1, further comprising, for each of the one or more points, determining the local geometry using an implicit iso-surface.

4. The method of claim 1, wherein said determining the one or more invariant features comprises calculating first, second, and third order derivatives of the three-dimensional digital medical image.

5. The method of claim 1, further comprising identifying an anatomical structure in the three-dimensional digital medical image using the one or more invariant features.

6. The method of claim 1, further comprising identifying a lesion in the three-dimensional digital medical image using the one or more invariant features.

7. The method of claim 6, wherein said identifying a lesion in the three-dimensional digital medical image includes using the one or more invariant features to reduce false positives.

8. The method of claim 1, further comprising using the one or more invariant features to segment the three-dimensional digital medical image into a plurality of labels.

9. The method of claim 1, further comprising deriving, using the one or more invariant features, a spatial transformation for aligning the three-dimensional digital medical image with one or more further image.

10. The method of claim 9, further comprising applying the spatial transformation to the three-dimensional digital medical image to align the three-dimensional digital medical image with the one or more further image.

11. A method for characterizing a point in a three dimensional digital medical image, the method comprising:

determining, on a computing device, third order derivatives of the three-dimensional digital medical image, at one or more points within the three-dimensional digital medical image;

determining on the computing device, at each of the one or more points, using the third order derivatives of the three-dimensional digital medical image, one or more semi-invariant features that characterize a local geometry at the point; and

characterizing each of the one or more points using the one or more determined semi-invariant features.

12. The method of claim 11, wherein said determining one or more semi-invariant features comprises determining derivatives of the three-dimensional digital medical image in Cartesian coordinates.

13. The method of claim 11, further comprising, for each of the one or more points, determining the local geometry using an implicit iso-surface.

14. The method of claim 11, wherein said determining one or more semi-invariant features comprises calculating first, second, and third order derivatives of the three-dimensional digital medical image.

15. The method of claim 11, further comprising identifying an anatomical structure in the three-dimensional digital medical image using the one or more semi-invariant features.

16. The method of claim 11, further comprising identifying a lesion in the three-dimensional digital medical image using the one or more semi-invariant features.

17. The method of claim 16, wherein said identifying a lesion in the three-dimensional digital medical image includes using the one or more semi-invariant features to reduce false positives.

18. The method of claim 11, further comprising using the one or more semi-invariant features to segment the three-dimensional digital medical image into a plurality of labels.

19. The method of claim 11, further comprising deriving, using the one or more semi-invariant features, a spatial transformation for aligning the three-dimensional digital medical image with one or more further image.

20. The method of claim 19, further comprising applying the spatial transformation to the three-dimensional digital medical image to align the three-dimensional digital medical image with the one or more further image.

21. An article of manufacture including a computer readable storage medium having stored thereon computer-executable instructions, execution of which by a computing device causes the computing device to perform processes comprising:

determining third order derivatives of a three-dimensional digital medical image, at one or more points within the three-dimensional digital medical image;

determining, at each of the one or more points, one or more invariant or semi-invariant features that characterize a local geometry at the point using the third order derivatives of the three-dimensional digital medical image; and

characterizing each of the one or more points using the one or more determined invariant features.

22. An apparatus comprising:

means for determining third order derivatives of a three-dimensional digital medical image, at one or more points within the three-dimensional digital medical image; and

means for determining, at each of the one or more points, one or more invariant or semi-invariant features that characterize a local geometry at the point using the third order derivatives of the three-dimensional digital medical image.