Vessel centerline determination

• US 8,494,240 B2
• Filed: 07/23/2012
• Issued: 07/23/2013
• Est. Priority Date: 01/15/2004
• Status: Active Grant

• Current Assignee: Philips Medical Systems Technologies Ltd. (Koninklijke Philips N.V.)
• Original Assignee: Algotec Systems Ltd. (Koninklijke Philips N.V.)
• Inventors: Milstein, Ido, Akerman, Shmuel, Miller, Gad, Cohen, Laurent
• Primary Examiner(s): IPPOLITO, NICOLE MARIE
• Application Number:
  US13/555,314
• Publication Number:
  US 20120288181A1
• Time in Patent Office: 365 Days
• Field of Search: 382/128, 382/129, 382/130, 382/131, 382/132, 382/159, 382/173, 382/177, 382/181
• US Class Current: 382/128
• CPC Class Codes: A61B 6/506 for diagnosis of nerves
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/20044 Skeletonization; Medial axis transform
• CPC Class Codes: G06T 2207/20101 Interactive definition of point of interest, landmark or seed
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/181 involving edge growing; involving edge linking
• CPC Class Codes: G06T 7/64 of convexity or concavity

Abstract

A method of centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.

Claims

1. A method of propagating parameterization in a digital medical image data set, comprising:

providing an initial parameterization in said digital medical image data set along at least one path;

propagating a parameterization from said path, wherein said propagation is limited to being in a direction substantially parallel to said at least one path and to have a gradient which is substantially perpendicular to a gradient of a path cost associated with said propagation.

2. A method according to claim 1 in which the propagating a parameterization from said path comprises taking a local curvature of the path into account.

3. A method according to claim 1, comprising limiting an angle between (a) a spatial vector defined between a starting point of the parameterization along said path and ending at a current point of propagation of parameterization and (b) said path, to being close to perpendicular.

4. A method according to claim 3, wherein said limiting comprises reducing leakage of a segmentation by said limiting.

5. A method according to claim 1, in which the propagating a parameterization from said path comprises collecting statistics about data point values added to said path.

6. A method according to claim 1, in which the propagating a parameterization from said path comprises collecting propagation statistics for different parameterization values of data points added to said path.

7. A method according to claim 6, and further comprising limiting propagation of at least one parameterization value based on said statistics.

8. A method according to claim 1, wherein said medical image data set is a three-dimensional data set.

9. A method according to claim 1, wherein said medical image data set is a medical image data set of one or more blood vessels.

10. A method of segmenting a digital medical image data set, comprising:

providing data points along an initial path;

associating the data points with a value of a parameterization;

partitioning the data points of initial path into a plurality of segments;

propagating parameterization in the digital medical image data set according to the method of claim 1; and

adjusting boundaries of the segments based, at least in part, on the parameterization.

11. A method according to claim 10 and further comprising:

prior to segmenting, initializing a heap with the data points along the initial path;

in each segment, producing a local histogram of data point values;

deciding whether to stop based on a stopping condition; and

if the decision is to stop then stopping, else propagating parameterization to data points neighboring data points in the heap.

12. A method according to claim 11 in which the propagating parameterization to data points neighboring data points in the heap comprises:

drawing a point from the heap;

selecting a set of neighboring points;

calculating a cost for a neighboring point;

propagating the parameterization to the neighboring point by associating the neighboring point with a value of the parameterization;

deciding whether to add the neighboring point into the heap;

if the decision is to add the neighboring point then adding the neighboring point into the heap.

13. A method according to claim 12 in which the drawing a point comprises drawing a point with a lowest associated cost.

14. A method according to claim 12 in which the calculating a cost for a neighboring point comprises calculating a local cost.

15. A method according to claim 12 in which if the decision is not to add the neighboring point into the heap then if the neighboring point is in the heap then updating a cost of the heap.

16. A method according to claim 11 in which the stopping condition is based, at least in part, on a number of points which were considered.

17. A method according to claim 11 in which at least one portion has a different stopping condition from at least one other portion.

18. A method according to claim 10 in which the partitioning is configured to produce portion sizes which are related to an expected diameter of an object in the medical image data set.

19. A method according to claim 10 in which the partitioning is configured to produce portion sizes which are related to an expected curvature of an object in the medical image data set.