Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI.

Published

Journal Article

The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.

Full Text

Duke Authors

Cited Authors

  • Cerrolaza, JJ; Villanueva, A; Cabeza, R

Published Date

  • March 2012

Published In

Volume / Issue

  • 31 / 3

Start / End Page

  • 713 - 724

PubMed ID

  • 22194238

Pubmed Central ID

  • 22194238

Electronic International Standard Serial Number (EISSN)

  • 1558-254X

International Standard Serial Number (ISSN)

  • 0278-0062

Digital Object Identifier (DOI)

  • 10.1109/tmi.2011.2175940

Language

  • eng