A STATISTICAL APPEARANCE MODEL BASED ON INTENSITY QUANTILES
Robert E. Broadhurst, Joshua V. Stough, Stephen M. Pizer, Edward L. Chaney · 2005
We present a novel histogram based approach for statistically characterizing the appearance of deformable models. In deformable model segmentation, appearance models measure the likelihood of an object given a target image. To determine this likelihood we compute pixel intensity quantiles of object-relative image regions from a weighted 3D image volume near the object boundary. We use a Gaussian model to statistically characterize the variation of quantiles understood in Euclidean space via the Mallows distance. The probability of intensities from gas and bone tissue are separately modeled to leverage a priori information on their expected distributions. The method is illustrated and evaluated in a segmentation study on CT images of the human left kidney. Results show improvement over a profile based appearance model and that the global maximum of the MAP estimate gives clinically acceptable segmentations in almost all of the cases studied.