Technical Program

Paper Detail

Paper:	FR-PM-PS1.10
Session:	Atlas and Model Based Image Segmentation
Time:	Friday, April 7, 13:30 - 14:50
Presentation:	Poster
Title:	A Statistical Appearance Model Based on Intensity Quantile Histograms
Authors:	Robert Broadhurst; University of North Carolina at Chapel Hill
	Joshua Stough; University of North Carolina at Chapel Hill
	Stephen M. Pizer; University of North Carolina at Chapel Hill
	Edward Chaney; University of North Carolina at Chapel Hill
Abstract:	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.

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