Generalized multiresolution hierarchical shape models via automatic landmark clusterization

Published

Conference Paper

Point Distribution Models (PDM) are some of the most popular shape description techniques in medical imaging. However, to create an accurate shape model it is essential to have a representative sample of the underlying population, which is often challenging. This problem is particularly relevant as the dimensionality of the modeled structures increases, and becomes critical when dealing with complex 3D shapes. In this paper, we introduce a new generalized multiresolution hierarchical PDM (GMRH-PDM) able to efficiently address the high-dimension-low-sample-size challenge when modeling complex structures. Unlike previous approaches, our new and general framework extends hierarchical modeling to any type of structure (multi- and single-object shapes) allowing to describe efficiently the shape variability at different levels of resolution. Importantly, the configuration of the algorithm is automatized thanks to the new agglomerative landmark clustering method presented here. Our new and automatic GMRH-PDM framework performed significantly better than classical approaches, and as well as the state-of-the-art with the best manual configuration. Evaluations have been studied for two different cases, the right kidney, and a multi-object case composed of eight subcortical structures. © 2014 Springer International Publishing.

Full Text

Duke Authors

Cited Authors

  • Cerrolaza, JJ; Villanueva, A; Reyes, M; Cabeza, R; González Ballester, MA; Linguraru, MG

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 8675 LNCS / PART 3

Start / End Page

  • 1 - 8

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 13 (ISBN-13)

  • 9783319104423

Digital Object Identifier (DOI)

  • 10.1007/978-3-319-10443-0_1

Citation Source

  • Scopus