Bibliography

1

J. Besag.
Spatial interaction and the statistical analysis of lattice systems (with discussion).
J. of Royal Statist. Soc., series B, 36(2):192-326, 1974.

2

J. Besag.
On the statistical analysis of dirty pictures (with discussion).
J. of Royal Statist. Soc., series B, 48(3):259-302, 1986.

3

J.C. Bezdek, L.O. Hall, and L.P. Clarke.
Review of MR image segmentation techniques using pattern recognition.
Medical Physics, 20(4):1033-1048, 1993.

4

C. M. Bishop.
Neural Networks for Pattern Recognition.
Oxford University Press, 1995.

5

M. Bomans, K. H. Hohne, L. A. Kramer, and J. M. Fletcher.
3-D segmentation of MR images of the head for 3-D display.
IEEE Trans. Medical Imaging, 18(1):25-34, 1990.

6

C. R. Brice and C. L. Fennema.
Scene analysis using regions.
Artificial Intelligence, 1(3):205-226, 1970.

7

M. E. Brummer.
Optimized intensity thresholds for volumetric analysis of magnetic resonance imaging data.
Proceedings of the SPIE, 1808:299-310, 1992.

8

L.P. Clarke, R.P. Velthuizen, M.A. Camacho, J.J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher, and M.L. Silbiger.
MRI segmentation: methods and applications.
Magnetic Resonance Imaging, 13(3):343-368, 1995.

9

H. E. Cline, C. L. Dumoulin, H. R. Hart, W. E. Lorense, and S. Ludke.
3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm.
Magnetic Resonance Imaging, 5:345-352, 1987.

10

S. Dellepiane.
Image segmentation: Errors, sensitivity, and uncertainty.
Proc. 13th IEEE-Eng. Med. Bio. Society, 13:253-254, 1991.

11

A. P. Dempster, N. M. Laird, and D. B. Bubin.
Maximum likelihood from incomplete data via EM algorithm.
J. of Royal Statist. Soc., series B, 39(1):1-38, 1977.

12

S Geman and D Geman.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.
IEEE Trans. Pattern Anal. Machine Intell., 6(6):721-741, 1984.

13

R Guillemaud and J. M. Brady.
Estimating the bias field of MR images.
IEEE Trans. Medical Imaging, 16(3):238-251, 1997.

14

K. Held, E. R. Kops, B. J. Krause, W. M. Wells, R. Kikinis, and H. Muller-Gartner.
Markov random field segmentation of brain MR images.
IEEE Trans. Medical Imaging, 16(6):878-886, 1997.

15

E. F. Jackson, P. A. Narayana, J. S. Wolinsky, and T. J. Doyle.
Accuracy and reproducibility in volumetric analysis of multiple sclerosis lesions.
J. Comput. Assist. Tomogr., 17:200-205, 1993.

16

T. Kapur, E. L. Grimson, R. Kikinis, and W. M. Wells.
Enhanced spatial priors for segmentation of magnetic resonance imagery.
Lecture Notes in Computer Science, 1496:148-157, 1998.

17

R. Kikinis, M. Shenton, F. A. Jolesz, G. Gerig, J. Martin, M. Anderson, and D. Metcalf.
Quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging.
J. Magn. Res. Imag., pages 619-629, 1992.

18

A. Kundu.
Local segmentation of biomedical images.
Comput. Med. Imaging Graph, 14:173-183, 1990.

19

A. Kundu, Y. He, and P. Bahl.
Recognition of handwritten word: First and second order hidden Markov model based approach.
Pattern Recognition, 22(3):283-297, 1989.

20

K. V. Leemput, F. Maes, D. Vandermeulen, A. Colchester, and P. Suetens.
Automated segmentation of MS lesions from multi-channel MR images.
Lecture Notes in Computer Science, 1679:11-21, 1999.

21

T. Lei and W. Sewchand.
Statistical approach to x-ray ct imaging and its applications in image analysis-part ii: A new stochastic model-based image segmentation technique for x-ray ct image.
IEEE Trans. Medical Imaging, 11(1):62-69, 1992.

22

S. Z. Li.
Markov Random Field Modeling in Computer Vision.
Springer-Verlag, 1995.

23

K. O. Lim and A. Pfefferbaum.
Segmentation of MR brain images into cerebrospinal fluid spaces, with and gray matter.
J. Computer Assisted Tomography, 13(4):588-593, 1989.

24

J. R. Mitchell, S. J. Karlik, D. H. Lee, and A. Fenster.
Computer-assisted identification and quantification of multiple sclerosis lesions in MR imaging volumes in the brain.
J. Magnetic Resonance Imaging, 4:197-208, 1994.

25

N. Otsu.
A threshold selection method from gray-level histogram.
IEEE Trans. Syst. Man Cybern., 9(1):62-66, 1979.

26

L. R. Rabiner.
A tutorial on hidden Markov models and selected applications in speech recognition.
Proceedings of the IEEE, 77(2):257-286, 1989.

27

J. C. Rajapakse and F. Kruggel.
Segmentation of MR images with intensity inhomogeneities.
Image and Vision Computing, 16(3):165-180, 1998.

28

R. A. Robb.
Visualization Methods for Analysis of Multimodality Images, chapter Functional Neuroimaging: Technical Foundations, pages 181-190.
Orlando, FL: Academic Press, 1994.

29

P. Schroeter, J.-M. Vesin, T. Langenberger, and R. Meuli.
Robust parameter estimation of intensity distributions for brain magnetic resonance images.
IEEE Trans. Medical Imaging, 17(2):172-186, 1998.

30

J.A. Vlontzos and S.Y. Kung.
Hidden Markov models for character recognition.
IEEE Trans. Image Processing, 1(4):539-543, 1992.

31

W. M. Wells, E. L. Grimson, R. Kikinis, and F. A. Jolesz.
Adaptive segmentation of MRI data.
IEEE Trans. Medical Imaging, 15(4):429-442, 1996.

32

C. F. J. Wu.
On the convergence properties of the EM algorithm.
The Annals of Statistics, 11:95-103, 1983.

33

J. Zhang.
The mean field theory in EM procedures for blind markov random field image restoration.
IEEE Trans. Image Processing, 2(1):27-40, 1993.

34

S. C. Zhu and A. Yuille.
Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation.
IEEE Trans. Pattern Anal. Machine Intell., 18(9):884-900, 1996.