Magnetic resonance image analysis by information theoretic criteria and stochastic site models

@article{Wang2001MagneticRI,
  title={Magnetic resonance image analysis by information theoretic criteria and stochastic site models},
  author={Yue Joseph Wang and T{\"u}lay Adali and Jianhua Xuan and Zsolt Szabo},
  journal={IEEE Transactions on Information Technology in Biomedicine},
  year={2001},
  volume={5},
  pages={150-158}
}
Quantitative analysis of magnetic resonance (MR) images is a powerful tool for image-guided diagnosis, monitoring, and intervention. The major tasks involve tissue quantification and image segmentation where both the pixel and context images are considered. To extract clinically useful information from images that might be lacking in prior knowledge, the authors introduce an unsupervised tissue characterization algorithm that is both statistically principled and patient specific. The method… CONTINUE READING
Highly Cited
This paper has 33 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses

IEEE/ACM Transactions on Computational Biology and Bioinformatics • 2014
View 2 Excerpts

Segmentation of MR images using multispectral fusion approach : A study and an evaluation

International Conference on Education and e-Learning Innovations • 2012
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 29 references

MRF modelbased algorithms for image segmentation

R. C. Dubes, A. K. Jain, S. G. Nadabar, C. C. Chen
Proc. Pattern Recognition , vol. 1, 1990, pp. 808–814. • 1990
View 6 Excerpts
Highly Influenced

Random field models in image analysis

R. C. Dubes, A. K. Jain
J. Appl. Stat. , vol. 16, no. 2, pp. 131–164, 1989. • 1989
View 7 Excerpts
Highly Influenced

On the Foundations of Relaxation Labeling Processes

IEEE Transactions on Pattern Analysis and Machine Intelligence • 1983
View 3 Excerpts
Highly Influenced

A new look at the statistical model identification

H. Akaike
IEEE Trans. Automat. Contr. , vol. AC-19, no. 6, pp. 716–723, Dec. 1974. • 1974
View 8 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…