Systems and methods for tubular object processing

• US 6,690,816 B2
• Filed: 04/09/2001
• Issued: 02/10/2004
• Est. Priority Date: 04/07/2000
• Status: Expired due to Fees

• Current Assignee: University of North Carolina At Chapel Hill (University of North Carolina System)
• Original Assignee: University of North Carolina At Chapel Hill (University of North Carolina System)
• Inventors: Pizer, Stephen M., Bullitt, Elizabeth, Aylward, Stephen R., Fritsch, Daniel
• Primary Examiner(s): Patel, Jayanti K.
• Assistant Examiner(s): Tabatabai, Abolfazl
• Application Number:
  US09/828,200
• Publication Number:
  US 20030053697A1
• Time in Patent Office: 1,037 Days
• Field of Search: 382/128, 382/129, 382/130, 382/131, 382/154, 382/173, 382/180, 128/922, 128/206.26, 128/856, 600/407, 600/209, 600/425, 600/426, 600/429
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20044 Skeletonization; Medial axis transform
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 2207/30172 Centreline of tubular or elongated structure
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/64 of convexity or concavity

Abstract

Systems and methods are disclosed for processing tubular objects in multi-dimensional images. The processing includes generating representations of tubular objects which enable subsequent analysis. Systems and methods for processing tubular objects in a multi-dimensional image include establishing a seed points in a multi-dimensional image, searching for an extremum point corresponding to a tubular object having a central curve of intensity extrema, and extracting a one-dimensional central track corresponding to the tubular object and extents corresponding to a plurality of cross-sections along the tubular object. A number of embodiments are presented which apply the systems and methods to a variety of imaging applications.

Claims

1. A method for processing at least one tubular object in a multi-dimensional image, comprising:

(a) establishing a seed point in a multi-dimensional image;

(b) searching for an extremum point corresponding to a tubular object having a central curve of intensity extrema;

(c) extracting;

a) a one-dimensional central track corresponding to the tubular object, and b) extents corresponding to a plurality of cross-sections along the tubular object, wherein the plurality of cross-sections intersects the one-dimensional central track;

(d) convolving intensities of image elements within a vicinity of the extremum point with a filter to create the central curve of intensity extrema;

(e) computing a first set of normal vectors at a first position corresponding to the extremum point, the first set of normal vectors being substantially orthogonal to the one-dimensional central track;

(f) computing a first tangent vector at the first position, the first tangent vector being substantially parallel to the one-dimensional central track;

(g) traversing to a second position by stepping from the first position in the direction along the tangent vector using a step size;

(h) computing a second set of normal vectors at the second position, the second set of normal vectors being substantially orthogonal to the one-dimensional central track;

(i) computing a second tangent vector at the second position, the second tangent vector being substantially parallel to the one-dimensional central track;

(j) determining a proximity of the first position and the second position;

(k) searching for an intensity maximum in a space defined by the first set of normal vectors when the second position is not substantially coincident with the central curve of intensity extrema; and

(l) determining whether at least one stop traversing criteria is met.

2. The method of claim 1, wherein the determining a proximity further includes:

(a) reducing the step size; and

(b) repeating the establishing a second position, the computing a second set of normal vectors, and the computing a second tangent vector.

3. The method of claim 1, wherein the step size is less than a dimensional extent represented by each one of the image elements.

4. The method of claim 1, wherein the searching for an intensity maximum further includes:

(a) computing a third position corresponding to the intensity maximum; and

(b) replacing the second position with the third position.

5. The method of claim 1, further comprising:

(a) replacing the first position by the second position; and

(b) repeating the extracting based on the at least one stop traversing criteria not being met.

6. The method of claim 1, wherein the computing the first and second set of normal vectors, the computing the first and second tangent vectors, and the determining whether at least one termination criteria is met includes computing the eigenvectors and eigenvalues of a Hessian.

7. The method of claim 1, wherein the filter is a Gaussian with a dynamic scale, and wherein the method further comprises:

(a) computing an extent of the cross-section corresponding to the tubular object at the second position when the second position is substantially coincident with the one-dimensional central track; and

(b) varying a dynamic scale based upon the computed extent.

8. The method of claim 7, further comprising:

(a) computing a plurality of extents of cross-sections corresponding to the tubular object at points along the one-dimensional central track, based upon the at least one stop traversing criteria not being met; and

(b) producing an ordered set of values representing locations of the one-dimensional central track in a reference associated with the multi-dimensional image.

9. The method of claim 8, wherein the cross-sections are substantially perpendicular to the one-dimensional central track.

10. The method of claim 9, further comprising:

generating a surface rendering illustration of a representation of the tubular object for display, the surface generating utilizing the one-dimensional central track and the extents of cross-sections.

11. The method of claim 9, further comprising:

restricting the visualization of the elements of the image to one of inside and outside the tubular objects using the one-dimensional central track and the extents of cross-sections.

12. The method of claim 9, further comprising:

positioning one of a volume and a surface rendering for display utilizing the one-dimensional central track.

13. The method of claim 12, wherein the central track is used to define a sequence of positions, wherein the order of the sequence defines a directionality of the one-dimensional central track.

14. The method of claim 8, wherein the computing includes utilizing a medialness functions centered on a point of interest on the one-dimensional central track.

15. The method of claim 14, wherein the medialness function utilizes boundary operators which include a plurality of pairs of spherical operators located at ends of a plurality of radii, the radii being distributed about the cross-section of the tubular object and having a common starting point at the point of interest one the one-dimensional central track.

16. The method of claim 14, further comprising:

(a) computing a plurality of extents of the tubular object, each orthogonal to the one-dimensional central track, at a plurality of locations on the one-dimensional central track on either side of the point of interest on the one-dimensional central track; and

(b) combining the plurality of extents of the tubular object to compute a measure of the extent at the point of interest.

17. The system of claim 1, wherein the filter is a Gaussian with a dynamic scale, and wherein the system further comprises:

(a) computing an extent of the cross-section corresponding to the tubular object at the second position when the second position is substantially coincident with the one-dimensional central track; and

(b) varying a dynamic scale based upon the computed extent.

18. The system of claim 17, further comprising additional instructions to be executed by the computer processor for:

(a) computing a plurality of extents of cross-sections corresponding to the tubular object at points along the one-dimensional central track, based upon the at least one stop traversing criteria not being met; and

(b) producing an ordered set of values representing locations of the one-dimensional central track in a reference associated with the multi-dimensional image.

19. The system of claim 18, wherein the cross-sections are substantially perpendicular to the one-dimensional central track.

20. The system of claim 19, further comprising additional instructions to be executed by the computer processor for:

generating a surface rendering illustration of a representation of the tubular object for display, the surface generating utilizing the one-dimensional central track and the extents of cross-sections.

21. The system of claim 19, further comprising additional instructions to be executed by the computer processor for:

restricting the visualization of the elements of the image to one of inside and outside the tubular objects using the one-dimensional central track and the extents of cross-sections.

22. The system of claim 19, further comprising:

positioning one of a volume and a surface rendering for display utilizing the one-dimensional central track.

23. The system of claim 22, wherein the central track is used to define a sequence of positions, wherein the order of the sequence defines a directionality of the one-dimensional central track.

24. The system of claim 18, wherein the computing includes utilizing a medialness functions centered on a point of interest on the one-dimensional central track.

25. The system of claim 24, further comprising additional instructions to be executed by the computer processor for:

(a) computing a plurality of extents of the tubular object, each orthogonal to the one-dimensional central track, at a plurality of locations on the one-dimensional central track on either side of the point of interest on the one-dimensional central track; and

(b) combining the plurality of extents of the tubular object to compute a measure of the extent at the point of interest.

26. The system of claim 24, wherein the medialness function utilizes boundary operators which include a plurality of pairs of spherical operators located at ends of a plurality of radii, the radii being distributed about the cross-section of the tubular object and having a common starting point at the point of interest one the one-dimensional central track.

27. A system for processing at least one tubular object in a multi-dimensional image, comprising:

(a) a computer processor; and

(b) a memory functionally coupled to the computer processor, wherein the memory stores a multi-dimensional image and instructions to be executed by the computer processor, for;

(i) establishing a seed point in the multi-dimensional image;

(ii) searching for an extremum point corresponding to a tubular object having a central curve of intensity extrema

(iii) extracting;

a) a one-dimensional central track corresponding to the tubular object, and b) extents corresponding to a plurality of cross-sections along the tubular object, wherein the plurality of cross-sections intersects the one-dimensional central track;

(iv) convolving intensities of image elements within a vicinity of the extremum point with a filter to create the central curve of intensity extrema;

(v) computing a first set of normal vectors at a first position corresponding to the extremum point, the first set of normal vectors being substantially orthogonal to the one-dimensional central track;

(vi) computing a first tangent vector at the first position, the first tangent vector being substantially parallel to the one-dimensional central track;

(vii) traversing to a second position by stepping from the first position in the direction along the tangent vector using a step size;

(viii) computing a second set of normal vectors at the second position, the second set of normal vectors being substantially orthogonal to the one-dimensional central track;

(ix) computing a second tangent vector at the second position, the second tangent vector being substantially parallel to the one-dimensional central track;

(x) determining a proximity of the first position and the second position;

(xi) searching for an intensity maximum in a space defined by the first set of normal vectors when the second position is not substantially coincident with the central curve of intensity extrema; and

(xii) determining whether at least one stop traversing criteria is met.

28. The system of claim 27, wherein the determining a proximity further includes:

(a) reducing the step size; and

(b) repeating the establishing a second position, the computing a second set of normal vectors, and the computing a second tangent vector.

29. The system of claim 27, wherein the step size is less than a dimensional extent represented by each one of the image elements.

30. The system of claim 27, wherein the searching for an intensity maximum further includes:

(a) computing a third position corresponding to the intensity maximum; and

(b) replacing the second position with the third position.

31. The system of claim 27, further comprising additional instructions to be executed by the computer processor for:

(a) replacing the first position by the second position; and

(b) repeating the extracting based on the at least one stop traversing criteria not being met.

32. The system of claim 27, wherein the computing the first and second set of normal vectors, the computing the first and second tangent vectors, and the determining whether at least one termination criteria is met includes computing the eigenvectors and eigenvalues of a Hessian.