Hierarchical active shape models, using the wavelet transform

Christos Davatzikos, Xiaodong Tao, Dinggang Shen

Research output: Contribution to journalArticle

166 Citations (Scopus)

Abstract

Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.

Original languageEnglish
Pages (from-to)414-423
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume22
Issue number3
DOIs
Publication statusPublished - 2003 Mar
Externally publishedYes

Keywords

  • Active shape model
  • Deformable contours
  • The wavelet transform

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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