Reconstruction of high-resolution T2W MR images of the prostate using maximum a posteriori approach and Markov random field regularization

2017 ◽  
Author(s):  
Jakub Jurek ◽  
Marek Kocinski ◽  
Andrzej Materka ◽  
Are Losnegard ◽  
Lars Reisater ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Field ◽  
Markov Random Field ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Mr Images ◽  
Markov Random

scholarly journals Markov random field segmentation of brain MR images

1997 ◽  
Vol 16 (6) ◽  
pp. 878-886 ◽  
Author(s):  
K. Held ◽  
E.R. Kops ◽  
B.J. Krause ◽  
W.M. Wells ◽  
R. Kikinis ◽  
...  
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Mr Images ◽  
Markov Random ◽  
Brain Mr Images

Automatic Segmentation of Breast MR Images Through a Markov Random Field Statistical Model

2014 ◽  
Vol 33 (10) ◽  
pp. 1986-1996 ◽  
Author(s):  
S. Ribes ◽  
D. Didierlaurent ◽  
N. Decoster ◽  
E. Gonneau ◽  
L. Risser ◽  
...  
Keyword(s):  
Statistical Model ◽  
Random Field ◽  
Markov Random Field ◽  
Automatic Segmentation ◽  
Mr Images ◽  
Markov Random ◽  
Breast Mr

Vision Monitoring Algorithm for Work Condition of NC Processing

2009 ◽  
Vol 16-19 ◽  
pp. 920-924
Author(s):  
Yuan Zhang ◽  
Shao Chen Kang ◽  
Peng Wang ◽  
Shi Wei Yin
Keyword(s):  
Markov Random Field ◽  
Video Analysis ◽  
High Speed ◽  
Image Sequence ◽  
Maximum A Posteriori ◽  
Work Piece ◽  
A Posteriori ◽  
Work Condition ◽  
Markov Random ◽  
Monitoring Algorithm

Aim at the problem of work condition monitoring in high speed NC processing, a new method is proposed based on video analysis technology. It puts forward a segmentation model with maximum a posteriori and Markov random field for NC processing image sequence. Moreover, the best segmentation is obtained by optimization immunity algorithm to monitor tool and work piece effectively. The experiment shows that this method can achieve accurate and real time monitoring and demonstrate the validity and robust of the solution for barrier work piece.

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

2012 ◽  
Vol 209 (2) ◽  
pp. 280-289 ◽  
Author(s):  
Zahra Shahvaran ◽  
Kamran Kazemi ◽  
Mohammad Sadegh Helfroush ◽  
Nassim Jafarian ◽  
Negar Noorizadeh
Keyword(s):  
Random Field ◽  
Level Set ◽  
Markov Random Field ◽  
Mr Images ◽  
Field Modeling ◽  
Markov Random ◽  
Variational Level Set
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