Histogram Statistics of Local Image Regions for Object Segmentation

We present a novel approach, based on local image histograms, for statistically characterizing the appearance of deformable models. In deformable model segmentation, appearance models measure the likeli- hood of an object given a target image. To determine this likelihood we compute pixel intensity histograms of local object-relative image regions from a 3D image volume near the object boundary. We use a Gaussian model to statistically characterize the variation of non-parametric his- tograms mapped to Euclidean space using the Earth Mover's Distance. The new method is illustrated and evaluated in a deformable model segmentation study on CT images of the human bladder, prostate, and rectum. Results show improvement over a previous profile based appear- ance model, out-performance of statistically modeled histograms over simple histogram measurements, and advantages of local image regions over global regions.

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

[2]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[3]  Robert E. Broadhurst.  TR 05-009 Simplifying Texture Classification , 2004 .

[4]  Tao Zhang,et al.  Model-based segmentation of medical imagery by matching distributions , 2005, IEEE Transactions on Medical Imaging.

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

[6]  Paul A. Yushkevich,et al.  Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.

[7]  Timothy F. Cootes,et al.  Improving Appearance Model Matching Using Local Image Structure , 2003, IPMI.

[8]  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.

[9]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[12]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[13]  Andrew Thall,et al.  A method and software for segmentation of anatomic object ensembles by deformable m-reps. , 2005, Medical physics.

[14]  Edward L. Chaney,et al.  Clustering on image boundary regions for deformable model segmentation , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).