Deep Structure of Images in Populations Via Geometric Models in Populations

We face the question of how to produce a scale space of image intensities relative to a scale space of objects or other characteristic image regions filling up the image space, when both images and objects are understood to come from a population. We argue for a schema combining a multi-scale image representation with a multi-scale representation of objects or regions. The objects or regions at one scale level are produced using soft-edged apertures, which are subdivided into sub-regions. The intensities in the regions are represented using histograms. Relevant probabilities of region shape and inter-relations between region geometry and of histograms are described, and the means is given of inter-relating the intensity probabilities and geometric probabilities by producing the probabilities of intensities conditioned on geometry.

[1]  Conglin Lu,et al.  Estimating the Statistics of Multi-object Anatomic Geometry Using Inter-object Relationships , 2005, DSSCV.

[2]  Xavier Pennec,et al.  Probabilities and statistics on Riemannian manifolds: Basic tools for geometric measurements , 1999, NSIP.

[3]  G. Gerig,et al.  Profile scale spaces for statistical image match in bayesian segmentation , 2004 .

[4]  Stephen M. Pizer,et al.  Multi-figure Anatomical Objects for Shape Statistics , 2005, IPMI.

[5]  Luc Florack,et al.  Deep Structure, Singularities, and Computer Vision, First International Workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, Revised Selected Papers , 2005, DSSCV.

[6]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[7]  Edward L. Chaney,et al.  Histogram Statistics of Local Model-Relative Image Regions , 2005, DSSCV.

[8]  Conglin Lu,et al.  Statistical Multi-Object Shape Models , 2007, International Journal of Computer Vision.

[9]  T. Lindeberg,et al.  Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales , 1994 .

[10]  D. Kendall A Survey of the Statistical Theory of Shape , 1989 .

[11]  P. Thomas Fletcher,et al.  Principal geodesic analysis for the study of nonlinear statistics of shape , 2004, IEEE Transactions on Medical Imaging.

[12]  Bart M. ter Haar Romeny,et al.  Front-End Vision and Multi-Scale Image Analysis , 2003, Computational Imaging and Vision.

[13]  Luc Florack,et al.  The Topological Structure of Scale-Space Images , 2000, Journal of Mathematical Imaging and Vision.

[14]  Ulf Grenander Pattern Synthesis: Lectures in Pattern Theory , 1976 .

[15]  Guido Gerig,et al.  Profile Scale-Spaces for Multiscale Image Match , 2004, MICCAI.

[16]  Conglin Lu,et al.  Automatic male pelvis segmentation from CT images via statistically trained multi-object deformable m-rep models , 2004 .