A statistical appearance model based on intensity quantile histograms

We present a novel histogram method 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 quantile histograms 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 histograms understood in Euclidean space via the Mallows distance. The probability of gas and bone tissue intensities 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.

• Type: Conference
• Year: 2006
• Where: ISBI
• Authors: Robert E. Broadhurst, Joshua Stough, Stephen M. Pizer, Edward L. Chaney
• Keywords: Appearance Model, Deformable Model Segmentation, Intensity Quantile Histograms, ISBI 2006, Medical Imaging