Abstract
We present a novel approach to statistically characterize histograms of model-relative image regions. A multiscale model is used as an aperture to define image regions at multiple scales. We use this image description to define an appearance model for deformable model segmentation. Appearance models measure the likelihood of an object given a target image. To determine this likelihood we compute pixel intensity histograms of local model-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 histograms 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 appearance model, out-performance of statistically modeled histograms over simple histogram measurements, and advantages of regional histograms at a fixed local scale over a fixed global scale.
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Broadhurst, R.E., Stough, J., Pizer, S.M., Chaney, E.L. (2005). Histogram Statistics of Local Model-Relative Image Regions. In: Fogh Olsen, O., Florack, L., Kuijper, A. (eds) Deep Structure, Singularities, and Computer Vision. DSSCV 2005. Lecture Notes in Computer Science, vol 3753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577812_7
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DOI: https://doi.org/10.1007/11577812_7
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