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Abstract #0133

Segmentation Priors from Local Image Properties, Not Location-Based Templates

Ziad Serhal Saad1, Andrej Vovk2, Janez Stare3, Dusan Suput2, Robert W. Cox1

1SSCC, NIMH/NIH, Bethesda, MD, USA; 2Institute of Pathophysiology, University of Ljubljana, Ljubljana, Slovenia; 3Institute for Biostatistics & Medical Informatics, University of Ljubljana, Ljubljana, Slovenia

We present a novel approach for generating a voxel's tissue class membership based on its signature; a collection of spatial texture statistics calculated over a set of spherical neighborhoods around that voxel. We produce tissue class priors that can initialize and regularize image segmentation much in the way population-based priors do as a function of spatial location in standard template space. The signature-based approach is a distinct departure from location-based methods by not requiring population-derived spatial template, registration to template's space, and bias field estimation. It is also suitable where location-based templates are not available or appropriate.