Stochastic Modeling of Volume Images with a 3-D Hidden Markov Model

Jia Li, Dhiraj Joshi, James Z. Wang
Penn State University
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-DHMM outperforms Gaussian mixture model based clustering by an order of magnitude in accuracy.


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Citation: 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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Last Modified: July 12, 2004
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