A learning framework for the automatic and accurate segmentation of cardiac tagged MRI images

Zhen Qian, Dimitris N. Metaxas, Leon Axel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model, and c) an Adaboost detection technique is used to initialize the segmentation. The set of boundary statistics learned by Adaboost is the weighted combination of all the useful appearance features, and results in more reliable and accurate image forces compared to using only edge or region information. Our experimental results show that given similar imaging techniques, our method can achieve a highly accurate performance without any human interaction.

Original languageEnglish (US)
Title of host publicationComputer Vision for Biomedical Image Applications - First International Workshop, CVBIA 2005, Proceedings
Pages93-102
Number of pages10
DOIs
StatePublished - 2005
Event1st International Workshop on Computer Vision for Biomedical Image Applications, CVBIA 2005 - Beijing, China
Duration: Oct 21 2005Oct 21 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3765 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Computer Vision for Biomedical Image Applications, CVBIA 2005
Country/TerritoryChina
CityBeijing
Period10/21/0510/21/05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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