Stochastic Modeling of Volume Images with a 3-D Hidden Markov Model
Jia Li, Dhiraj Joshi, James Z. Wang
Penn State University
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-DHMM
outperforms Gaussian mixture model based clustering by an order of
magnitude in accuracy.
Full Paper in Color
Jia Li, Dhiraj Joshi and James Z. Wang, ``Stochastic Modeling of
Volume Images with a 3-D Hidden Markov Model,'' Proc. IEEE
International Conference on Image Processing (ICIP), Singapore,
pp. 2359-2362, IEEE, October 2004.
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July 12, 2004