Stochastic Modeling of Volume Images with a 3-D Hidden Markov Model (TP-P8)

Author(s) : Jia Li (Pennsylvania State University, USA) Dhiraj Joshi (Pennsylvania State University, USA) James Wang (Pennsylvania State University, USA)

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.

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