A stochastic approach to 3-D image modeling

Dhiraj Joshi, Jia Li, James Z. Wang

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

Abstract

Statistical modeling methods have been success-fully used to segment, classify, and annotate digital images, over the years. In this paper, we present a 3-D hidden Markov model (HMM) for volume image modeling. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Potential applications to 3-D biomedical image analysis are also discussed.

Original languageEnglish (US)
Title of host publication2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States
Duration: Jul 13 2006Jul 14 2006

Publication series

Name2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

Other

Other2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
CountryUnited States
CityBethesda, MD
Period7/13/067/14/06

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All Science Journal Classification (ASJC) codes

  • Health(social science)
  • Assessment and Diagnosis
  • Medicine(all)
  • Health Information Management
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Signal Processing

Cite this

Joshi, D., Li, J., & Wang, J. Z. (2006). A stochastic approach to 3-D image modeling. In 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 [4015811] (2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006). https://doi.org/10.1109/LSSA.2006.250410