TY - GEN
T1 - Stochastic modeling of volume images with a 3-D hidden Markov model
AU - Li, Jia
AU - Joshi, Dhiraj
AU - Wang, James Z.
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=20444501384&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20444501384&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2004.1421574
DO - 10.1109/ICIP.2004.1421574
M3 - Conference contribution
AN - SCOPUS:20444501384
SN - 0780385543
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2359
EP - 2362
BT - 2004 International Conference on Image Processing, ICIP 2004
T2 - 2004 International Conference on Image Processing, ICIP 2004
Y2 - 18 October 2004 through 21 October 2004
ER -