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

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

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.