Cross entropy: A new solver for markov random field modeling and applications to medical image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3749 LNCS, Issue , 2005, Pages 229-237

  Wu, Jue ^a   Chung, Albert C S ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY; FUNCTION EVALUATION; GRAPH THEORY; MARKOV PROCESSES; MATHEMATICAL MODELS; MEDICAL IMAGING; RANDOM PROCESSES; SIMULATED ANNEALING;

CROSS ENTROPY (CE); MARKOV RANDOM FIELD; STOCHASTIC PROPERTY;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ENTROPY; EVALUATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; MAGNETIC RESONANCE ANGIOGRAPHY; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMPUTER SIMULATION; ENTROPY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE ANGIOGRAPHY; MARKOV CHAINS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 33744823563     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11566465_29     Document Type: Conference Paper

References (18)

1. N.Villain, Y.Goussard, J.Idler, M.Allain: Three-dimensional edge-preserving image enhancement for computed tomography. IEEE TMI 22 (2003) 1275-1287
2. Y.Chen, A.Amini: A map framework for tag line detection in spamm data using markov random fields on the b-spline solid. IEEE TMI 21 (2002) 1110-1122
3. M.Svensen, F.Kruggel, D.Cramon: Probabilistic modeling of single-trial fmri data. IEEE TMI 19 (2000) 25-35
4. A.C.S.Chung, J.A.Noble, P.Summers: Vascular segmentation of pcmra based on statistical mixture modeling and local phase coherence. IEEE TMI 23 (2004) 1490-1507
5. Y.Boykov, O.Veksler, R.Zabin: Fast approximate energy minimization via graph cuts. IEEE on PAMI 23 (2001) 1222-1239
6. S.Kirkpatrick, D.Gellatt, M.Vecchi: Optimization by simulated annealing. Science 220 (1983) 671-680
7. J.Besag: On the statistical analysis of dirty pictures. J. of the Roy. Stat. Soc. Series B 48 (1986) 259-302
8. S.Z.Li, ed.: Markov Random Field Modeling in Image Analysis. Springer-Verlag Tokyo (2001)
9. J.S.Yedidia, W.T.Freeman, Y.Weiss: Understanding belief propagation and its generalizations. Technical Report, Mitsubishi Electric Research TR-2001-22 (2001)
10. S.Zhu, Y.Wu, D.Mumford: Frame: filters, random fields, and minimax entropy towards a unified theory for texture modeling. In: IEEE CVPR. Volume 6. (1996) 686-693
11. V.Kolmogorov, R.Zabih: What energy functions can be minimized via graph cuts? IEEE Trans. PAMI 26 (2004) 147-159
12. R.Rubinstein: Optimization of computer simulation models with rare events. European Journal of Operations Research 99 (1997) 89-112
13. M.Tappen, W.Freeman: Comparison of graph cuts with belief propagation for stereo, using identical mrf parameters. In: IEEE ICCV. (2003) 900-906
14. R.Rubinstein, D.Kroese, eds.: The Cross-Entropy Method: A Unified Approach to Cominatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer-Verlag, New York (2004)
15. K.Held, et al: Markov random field segmentation of brain mr images. IEEE TMI 16 (1997) 878-886
16. S.Ruan, et al: Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE TMI 19 (2000) 1179-1187
17. BrainWeb: (http://www.bic.mni.mcgill.ca/brainweb/)
18. IBSR: (http://www.cma.mgh.harvard.edu/ibsr/)