Stochastic modeling of volume images with a 3-D hidden Markov model

Jia Li, Dhiraj Joshi, James Wang

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

5 Scopus citations

Abstract

Over the years, researchers in the image analysis community have successfully used various statistical modeling methods to segment, classify, and annotate digital images. In this paper, we propose a 3-D hidden Markov model (HMM) for volume image modeling. A computationally efficient algorithm is developed to estimate the model. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Experiments have demonstrated that 3-D HMM outperforms Gaussian mixture model based clustering by an order of magnitude in accuracy.

Original languageEnglish (US)
Title of host publication2004 International Conference on Image Processing, ICIP 2004
Pages2359-2362
Number of pages4
DOIs
StatePublished - Dec 1 2004
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: Oct 18 2004Oct 21 2004

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume4
ISSN (Print)1522-4880

Other

Other2004 International Conference on Image Processing, ICIP 2004
CountrySingapore
Period10/18/0410/21/04

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Li, J., Joshi, D., & Wang, J. (2004). Stochastic modeling of volume images with a 3-D hidden Markov model. In 2004 International Conference on Image Processing, ICIP 2004 (pp. 2359-2362). (Proceedings - International Conference on Image Processing, ICIP; Vol. 4). https://doi.org/10.1109/ICIP.2004.1421574