Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation

A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The evolving contour interior is modeled by a 2nd-order translation and rotation invariant Markov-Gibbs random field of object/background labels with analytically estimated potentials. Experiments with kidney CT images confirm robustness and accuracy of the proposed approach.

[1]  Rachid Deriche,et al.  Unifying boundary and region-based information for geodesic active tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  David N. Levin,et al.  Brownian strings: segmenting images with stochastically deformable contours , 1994, Other Conferences.

[3]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[4]  Georgy L. Gimel'farb,et al.  Robust image segmentation using learned priors , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Wei Li,et al.  A Novel Level Set Based Shape Prior Method for Liver Segmentation from MRI Images , 2008, MIAR.

[6]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[7]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

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

[10]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  B. S. Manjunath,et al.  EdgeFlow: a technique for boundary detection and image segmentation , 2000, IEEE Trans. Image Process..

[12]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[13]  Georgy L. Gimel'farb,et al.  EM Based Approximation of Empirical Distributions with Linear Combinations of Discrete Gaussians , 2007, 2007 IEEE International Conference on Image Processing.

[14]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.