Download artificial intelligence technologies for smart infrastructure

ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR SMART
INFRASTRUCTURE SYSTEMS
Tong Zhang ([email protected]) Supervisors: Prof. (David) Dagan Feng & Dr. Yong Xia
School of Information Technologies
FACULTY OF ENGINEERING & INFORMATION TECHNOLOGIES
SMART INFRASTRUCTURE SYSTEMS
CASE1 AI FOR SMART HEALTHCARE
Hidden Markov random field model based brain MR image segmentation using
clonal selection algorithm and Markov chain Monte Carlo method
 Statistical models and model estimation methods for MR image segmentation are
investigated.
Smart
Healthcare
Intelligent
Transport
Systems
Smart
Grids
 Proposed a HMRF based method that can jointly segment MR images and
correct bias fields.
 The HMRF is stepwise learned by MCMC-based voxel labelling and CSA-based
model estimation.
 The results of the proposed algorithm are very promising and robust to image
artefacts.
Smart
Infrastructure
Systems
Results on Brainweb dataset
Smart
Energy
Supply
Smart
Structures
Smart
Telecom
CASE2 AI FOR SMART STRUCTURES
Finite element model updating using estimation of
distribution algorithms (EDAs)
(a) 88th transverse slice in the simulated study (with 7% noise and 40% INU); (b) INU corrected
image; (c) Estimated INU; (d) Result of the HMRF-EM algorithm; (e) Result of the D-C algorithm;
(f) Result of the SPM package; (g) Result of the GA-EM algorithm; (h) Result of the eHMRF
algorithm; (i) Result of the proposed HMRF-CSA algorithm; (j) Ground truth
Vibration Test
Parameters
Numerical
Modeling
Parameters
Optimised
by
EDAs
Model updating results
1.2
Results on IBSR (Version 2.0) dataset
1
0.8
0.6
0.4
0.2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
True value 1 0.91 1 1 1 1 1 0.72 1 1 1 1 1 1 1
J1
0.970.960.951.020.971.070.960.71 1.1 0.931.04 1 1.020.971.04
J2
1.010.910.980.981.04 1 0.960.750.991.020.990.991.020.971.01
J3
1.020.89 1 1.01 1 0.99 1 0.711.021.040.971.021.010.951.04
REFERENCE
[1] T. Zhang, Y. Xia and D. Feng (2013) “Hidden Markov
Random Field Model Based Brain MR Image Segmentation
Using Clonal Selection Algorithm and Markov Chain Monte
Carlo Method," Biomedical Signal Processing and Control,
Available Online
[2] Y. Wang and T. Zhang (2013) “Finite element model
updating using estimation of distribution algorithms” 6th
International Conference on Structural Health Monitoring of
Intelligent Infrastructure, Hong Kong, China, 2013
FSL
D-C Algorithm
SPM
GA-EM
Proposed
Overall
75.06%
75.02%
81.20%
74.97%
82. 95%
GM
77.35%
73.80%
84.42%
77.90%
84.92%
WM
87.08%
88.41%
87.38%
87.23%
83.88%
CSF
16.19%
33.04%
20.31%
14.90%
55.45%
CONCLUSION
In Case 1, we incorporate CSA and MCMC methods into HMRF model estimation, and
thus propose the HMRF–CSA algorithm for brain MR image segmentation to overcome
the drawback of traditional HMRF-based segmentation approaches. Our results show
that the proposed algorithm is robust to image artefacts and can differentiate major
brain structures more accurately than other three algorithms.
In Case 2, EDAs are applied to finite element model updating process for structural
damage detection. The results show that the performances of EDAs for model updating
are efficient and reliable.
ACKNOWLEDGEMENTS
The work of Case 1 was supported by the Australian Research Council (ARC) grants.
The simulated brain MR data sets and the ground truth were provided by the
McConnell Brain Imaging Center of the Montreal Neurological Institute at the McGill
University. The clinical MR brain data sets and their manual segmentations were
provided by the Center for Morphometric Analysis at Massachusetts General Hospital.