Statistical deformable model-based segmentation of image motion

We present a statistical method for the motion-based segmentation of deformable structures undergoing nonrigid movements. The proposed approach relies on two models describing the shape of interest, its variability, and its movement. The first model corresponds to a statistical deformable template that constrains the shape and its deformations. The second model is introduced to represent the optical flow field inside the deformable template. These two models are combined within a single probability distribution, which enables to derive shape and motion estimates using a maximum likelihood approach. The method requires no manual initialization and is demonstrated on synthetic data and on a medical X-ray image sequence.

[1]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[2]  Nicholas Ayache,et al.  Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Charles Kervrann,et al.  A hierarchical statistical framework for the segmentation of deformable objects in image sequences , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Demetri Terzopoulos,et al.  Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion , 1988, Artif. Intell..

[5]  Michael J. Black,et al.  Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Ulf Grenander,et al.  Hands: A Pattern Theoretic Study of Biological Shapes , 1990 .

[7]  Patrick Bouthemy,et al.  Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[9]  Charles Kervrann,et al.  Statistical model-based segmentation of deformable motion , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[10]  Sridhar Lakshmanan,et al.  Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Geir Storvik,et al.  A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  François G. Meyer,et al.  TRACKING MYOCARDIAL DEFORMATION USING SPATIALLY-CONSTRAINED VELOCITIES , 1995 .

[13]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[15]  Michael I. Miller,et al.  REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .

[16]  Mário A. T. Figueiredo,et al.  Bayesian estimation of ventricular contours in angiographic images , 1992, IEEE Trans. Medical Imaging.

[17]  José M. N. Leitão,et al.  Adaptive B-splines and boundary estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Edward H. Adelson,et al.  Probability distributions of optical flow , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.