An adaptive-focus deformable model using statistical and geometric information

Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

An active contour (snake) model is presented, with emphasis on medical imaging applications. There are three main novelties in the proposed model. First, an attribute vector is used to characterize the geometric structure around each point of the snake model; the deformable model then deforms in a way that seeks regions with similar attribute vectors. This is in contrast to most deformable models, which deform to nearby edges without considering geometric structure, and it was motivated by the need to establish point-correspondences that have anatomical meaning. Second, an adaptive-focus statistical model has been suggested which allows the deformation of the active contour in each stage to be influenced primarily by the most reliable matches. Third, a deformation mechanism that is robust to local minima is proposed by evaluating the snake energy function on segments of the snake at a time, instead of individual points. Various experimental results show the effectiveness of the proposed model.

Original languageEnglish (US)
Pages (from-to)906-913
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume22
Issue number8
DOIs
StatePublished - Dec 1 2000

Keywords

  • Active contour
  • Adaptive focus deformable model
  • Snake
  • Statistical shape models

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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