An Oriented Flux Symmetry Based Active Contour Model for Three Dimensional Vessel Segmentation

This paper proposes a novel approach to segment three dimensional curvilinear structures, particularly vessels in angiography, by inspecting the symmetry of image gradients. The proposed method stresses the importance of simultaneously considering both the gradient symmetry with respect to the curvilinear structure center, and the gradient antisymmetry with respect to the object boundary. Measuring the image gradient symmetry remarkably suppresses the disturbance introduced by rapid intensity changes along curvilinear structures. Meanwhile, considering the image gradient antisymmetry helps locate the structure boundary. The gradient symmetry and the gradient antisymmetry are evaluated based on the notion of oriented flux. By utilizing the aforementioned gradient symmetry information, an active contour model is tailored to perform segmentation. On the one hand, by exploiting the symmetric image gradient pattern observed at structure centers, the contours expand along curvilinear structures even through there exists intensity fluctuation along the structures. On the other hand, measuring the antisymmetry of the image gradient conveys strong detection responses to precisely drive contours to the structure boundaries, as well as avoiding contour leakages. The proposed method is capable of delivering promising segmentation results. This is validated in the experiments using synthetic data and real vascular images of different modalities, and through the comparison to two well founded and published methods for curvilinear structure segmentation.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[3]  Jordi Vitrià,et al.  Eigensnakes for vessel segmentation in angiography , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[5]  Ken Masamune,et al.  Effective Statistical Edge Integration Using a Flux Maximizing Scheme for Volumetric Vascular Segmentation in MRA , 2007, IPMI.

[6]  Guido Gerig,et al.  Multiscale detection of curvilinear structures in 2-D and 3-D image data , 1995, Proceedings of IEEE International Conference on Computer Vision.

[7]  Ross T. Whitaker,et al.  A Level-Set Approach to 3D Reconstruction from Range Data , 1998, International Journal of Computer Vision.

[8]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[9]  Kaleem Siddiqi,et al.  Flux maximizing geometric flows , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Isabelle Bloch,et al.  A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes , 2009, Medical Image Anal..

[11]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..

[12]  Olivier D. Faugeras,et al.  CURVES: Curve evolution for vessel segmentation , 2001, Medical Image Anal..

[13]  Maxime Descoteaux,et al.  Geometric Flows for Segmenting Vasculature in MRI: Theory and Validation , 2004, MICCAI.

[14]  Max W. K. Law,et al.  Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux , 2008, ECCV.

[15]  Ashraf A. Kassim,et al.  Segmentation of volumetric MRA images by using capillary active contour , 2006, Medical Image Anal..