Segmentation priors from local image properties: without using bias field correction, location-based templates, or registration.

ABSTRACT: We present a novel approach for generating information about a voxel's tissue class membership based on its signature--a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach

SUBMITTER: Vovk A 

PROVIDER: S-EPMC3031751 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature