Image segmentation based on the integration of markov random fields and deformable models

Ting Chen, Dimitris Metaxas

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

23 Scopus citations

Abstract

This paper proposes a new methodology for image segmentation based on the integration of deformable and Markov Random Field models. Our method makes use of Markov Random Field theory to build a Gibbs Prior model of medical images with arbitrary initial parameters to estimate the boundary of organs with low signal to noise ratio (SNR). Then we use a deformable model to fit the estimated boundary. The result of the deformable model fit is used to update the Gibbs prior model parameters, such as the gradient threshold of a boundary. Based on the updated parameters we restart the Gibbs prior models. By iteratively integrating these processes we achieve an automated segmentation of the initial images. By careful choice of the method used for the Gibbs prior models, and based on the above method of integration with deformable model our segmentation solution runs in close to real time. Results of the method are presented for several examples, including some MRI images with significant amount of noise.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2000 - 3rd International Conference, Proceedings
EditorsScott L. Delp, Anthony M. DiGoia, Branislav Jaramaz
PublisherSpringer Verlag
Pages256-265
Number of pages10
ISBN (Print)3540411895, 9783540411895
DOIs
StatePublished - 2000
Externally publishedYes
Event3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000 - Pittsburgh, United States
Duration: Oct 11 2000Oct 14 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1935
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000
Country/TerritoryUnited States
CityPittsburgh
Period10/11/0010/14/00

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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