Image segmentation combining level sets and principal component analysis

Chengtian Song*, Keyong Wang, Lian Zheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An new enhancement method is proposed to the Stochastic Active Contour Scheme (STACS) for image segmentation using Principle Component Analysis(PCA). STACS is a method developed for segmentation of cardiac Magnetic Resonance Imaging(MRI) images and is based on the level set method in which the contour is driven by the minimization of a function of four terms-region based, edge based, shape prior, and curvature. STACS derives each of these forces from the original image that is to be segmented. In our method, PCA is performed on the entire set of eight images of the same slice of the heart taken at different instants of time in the cardiac cycle and then segment each image separately. The various terms in the energy functional in this new scheme are obtained from different principal components(Eigenvectors). Thus, STACS is improved by emphasizing each term in the energy functional with the help of the principal component that gives the most accurate result. Experimental results are presented with the proposed scheme.

Original languageEnglish
Title of host publicationMIPPR 2007
Subtitle of host publicationMedical Imaging, Parallel Processing of Images, and Optimization Techniques
DOIs
Publication statusPublished - 2007
EventMIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques - Wuhan, China
Duration: 15 Nov 200717 Nov 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6789
ISSN (Print)0277-786X

Conference

ConferenceMIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Country/TerritoryChina
CityWuhan
Period15/11/0717/11/07

Keywords

  • Cardiac magnetic resonance imaging(cardiac MRI)
  • Image segmentation
  • Level set
  • Principle component analysis
  • Stochastic active contour

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