Image analysis – Applications – Biomedical applications
Reexamination Certificate
2000-06-23
2004-04-06
Boudreau, Leo (Department: 2621)
Image analysis
Applications
Biomedical applications
Reexamination Certificate
active
06718054
ABSTRACT:
BACKGROUND OF THE INVENTION
The invention relates to the field of volumetric three-dimensional image data segmentation, and in particular to MRA segmentation.
Automatic and semi-automatic magnetic resonance angiography (MRA) segmentation techniques can potentially save radiologists large amounts of time required for manual segmentation and can facilitate further data analysis. It is desirable to develop computer vision techniques for the segmentation of medical images. Specifically, the segmentation of volumetric vasculature images is considered, such as the magnetic resonance angiography (MRA) image pictured in FIG.
1
.
As shown in
FIG. 1
, blood vessels appear in MRA images as bright curve-like patterns which may be noisy and have gaps. What is shown is a “maximum intensity projection”. The data is a stack of slices where most areas are dark, but vessels tend to be bright. This stack is collapsed into a single image for viewing by performing a projection through the stack that assigns to each pixel in the projection the brightest voxel over all slices. This image shows projections along three orthogonal axes.
Thresholding is one possible approach to this segmentation problem and works adequately on the larger vessels. The problem arises in detecting the small vessels. Thresholding cannot be used for the small vessels for several reasons. The voxels may have an intensity that is a combination of the intensities of vessels and background if the vessel is only partially inside the voxel. This sampling artifact is called partial voluming. Other imaging conditions can cause some background areas to be as bright as other vessel areas, complicating threshold selection. Finally, the images are often noisy, and methods using local contextual information can be more robust.
Mean curvature evolution schemes for segmentation, implemented with level set methods, have become an important approach in computer vision. This approach uses partial differential equations to control the evolution. The fundamental concepts from mathematics from which mean curvature schemes derive were explored several years earlier when smooth closed curves in 2D were proven to shrink to a point under mean curvature motion. Mean curvature flow of any hypersurface was framed as a level set problem. For application to image segmentation, a vector field was induced on the embedding space, so that the evolution could be controlled by an image gradient field or other image data. The same results of existence, uniqueness, and stability of viscosity solutions were obtained for the modified evolution equations for the case of planar curves, and experiments on real-world images demonstrated the effectiveness of the approach.
Curves evolving in the plane became surfaces evolving in space, called “minimal surfaces”. Although the theorem on planar curves shrinking to a point could not be extended to the case of surfaces evolving in 3D, the existence, uniqueness, and stability results of the level set formalism held analogously to the 2D case. Thus, the method was feasible for evolving both curves in 2D and surfaces in 3D. Beyond elegant mathematics, spectacular results on real-world data sets established the method as an important segmentation tool in both domains. One fundamental limitation to these schemes has been that they describe only the flow of hypersurfaces, i.e., surfaces of co-dimension 1.
The problem of curve-shortening flow for 3D curves has been studied, and the level set technique has been generalized to arbitrary manifolds in arbitrary dimension. Analogous results were provided and extend the level set evolution equation to account for an additional vector field induced on the space.
SUMMARY OF THE INVENTION
Accordingly, the invention presents the first implementation of geodesic active contours in 3D. Specifically, the system and method of the invention use these techniques for automatic segmentation of blood vessels in MRA images. The dimension of the manifold is 1, and its co-dimension is 2.
The invention utilizes the fact that the underlying structures in the image are indeed 3D curves and evolves an initial curve into the curves in the data (the vessels). In particular, the segmentation techniques of the invention are based on the concept of mean curvature flow, or curve-shortening flow, from the field of differential geometry. The proposed MRA segmentation method uses a mathematical modeling technique that is well-suited to the complicated curve-like structure of blood vessels. The segmentation task is defined as an energy minimization over all 3D curves and uses a level set method to search for a solution. The approach is an extension of previous level set segmentation techniques to higher co-dimension.
The invention thus sets forth a method of providing segmentation of volumetric three-dimensional image data such as MRA images. Initially, a three-dimensional MRA volume is provided. An initial surface S is then generated by thresholding input. A signed distance function v to S is then generated, where v=v(x,t) and S is the zero level set of v(·,0). The process proceeds by iteratively updating v according to
v
t
=
λ
(
∇
v
(
x
,
t
)
,
∇
2
v
(
x
,
t
)
)
+
g
′
g
∇
v
(
x
,
t
)
·
H
∇
I
&LeftBracketingBar;
∇
I
&RightBracketingBar;
,
the updating terminates at convergence or as determined by an operator. S′ is then defined to be the zero level set of the current distance function v′ and reinitialize v′ to be a distance function to S′. The volume is continually iteratively updated such that a final distance function v is obtained. The first output obtained from this volume is a segmentation of vessels in the MRA data, obtained by computing the zero level set of v.
REFERENCES:
patent: 4875165 (1989-10-01), Fencil et al.
patent: 5297550 (1994-03-01), Margosian
patent: 5368033 (1994-11-01), Moshfeghi
patent: 5644646 (1997-07-01), Du et al.
patent: 5647360 (1997-07-01), Bani-Hashemi et al.
patent: 5690106 (1997-11-01), Bani-Hashemi et al.
patent: 5699799 (1997-12-01), Xu et al.
patent: 5832134 (1998-11-01), Avinash et al.
patent: 5848121 (1998-12-01), Gupta et al.
patent: 5920319 (1999-07-01), Vining et al.
patent: 5923770 (1999-07-01), O'Donnell et al.
patent: 6047080 (2000-04-01), Chen et al.
patent: 6058218 (2000-05-01), Cline
patent: 6345112 (2002-02-01), Summers et al.
patent: 6535623 (2003-03-01), Tannenbaum et al.
Front propagation and level-set approach for geodesic active stereovision, Deriche, R.; Bouvin, C.; Faugeras, O.; Visual Surveillance, 1998. Proceedings., 1998 IEEE Workshop on , Jan 2, 1998 pp.: 56-63.*
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem, Faugeras, O.; Keriven, R.; Image Processing, IEEE Transactions on , vol.: 7 Issue: 3 , Mar. 1998 pp.: 336-344.*
Verdock B. et al., “Accurate Segmentation of Blood Vessels From 3D Medical Images”; Proceedings of the International Conference On Image Processing; (ICIP), US, New York, IEEE, Sep. 16, 1996 p. 312.
Kim D. K. et al., Boundary Segmentation and Model Fitting Using Affine Active Surfacemodel: Digital Signal Processing Application, US, New York; IEEE, Nov. 26, 1996; pp. 147-150.
Snell J.W. et al., “Model-Based Segmentation of the Brain From 3-D MRI Using Active Surfaces”; Proceedings of the Northeast Bioengineering Conference, US, New York, IEEE, Vol, Conf. 19, Mar. 18, 1993; pp. 164-165.
Faugeras Olivier
Grimson W. Eric L.
Keriven Renaud
Kikinis Ron
Lorigo Liana M.
Akhavannik Hussein
Boudreau Leo
Gauthier & Connors LLP
Massachusetts Institute of Technology
LandOfFree
MRA segmentation using active contour models does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with MRA segmentation using active contour models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and MRA segmentation using active contour models will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3269104