Knowledge guided left ventricular segmentation - the Taguchi approach

Ding Horng Chen, Yung-Nien Sun

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

Abstract

The deformable model (snake) is widely applied in segmentation for medical images especially for cardiac image analysis. Although the model is flexible in the contour deformation process, the weights of associated energy in the model are always given empirically. The systematic (or automatic) weight assignment is still unavailable. In this paper, we propose a new method to determine the weights of snake properly for the detection of left ventricle from sonogram. The Taguchi method is adopted to obtain the weight assignment efficiently. To avoid the type one error in weight assignment, the ANOVA test is performed to verify the correctness of our assumption. The experimental results show that the proposed method successfully resolves the weight assignment problem in the deformable model and obtains satisfactory cardiac contour detection.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
Number of pages1
ISBN (Print)0780356756
Publication statusPublished - 1999 Dec 1
EventProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA
Duration: 1999 Oct 131999 Oct 16

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2
ISSN (Print)0589-1019

Other

OtherProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
CityAtlanta, GA, USA
Period99-10-1399-10-16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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