Bibliography

1

National Institute of Health supported Internet Brain Segmentation Repository (IBSR), http://www.cma.mgh.harvard.edu/ibsr.
2006.

2

National Library of Medicine Insight Segmentation and Registration Toolkit (ITK), http://www.itk.org.
2006.

3

D. Adalsteinsson and J. Sethian.
A fast level set method for propagating interfaces.
J. Comput. Phys., 118(2):269-277, 1995.

4

S. P. Awate, T. Tasdizen, and R. T. Whitaker.
Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics.
In Proc. European Conference on Computer Vision (ECCV), 2006.

5

S. P. Awate, T. Tasdizen, R. T. Whitaker, and N. L. Foster.
Adaptive, nonparametric markov modeling for unsupervised, MRI brain-tissue classification.
Medical Image Analysis, 10(5):726-739, 2006.

6

S. P. Awate and R. T. Whitaker.
Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering.
In Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 44-51, 2005.

7

S. P. Awate and R. T. Whitaker.
An interactive parallel multiprocessor level-set solver with dynamic load balancing.
Technical report, 2005.

8

S. P. Awate and R. T. Whitaker.
Nonparametric Neighborhood Statistics for MRI Denoising.
In Proc. Int. Conf. Information Processing in Medical Imaging (IPMI), Springer, Lect. Notes in Comp. Sci., volume 3565, pages 677-688, 2005.

9

S. P. Awate and R. T. Whitaker.
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration.
IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 28(3):364-376, March 2006.

10

D. Barash and D. Comaniciu.
A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift.
Image Vision Comput., 22(1):73-81, 2004.

11

S. Basu, P. T. Fletcher, and R. T. Whitaker.
Rician noise removal in diffusion tensor mri.
In Med. Imag. Computing and Comp. Assist. Intervention, pages 117-125, 2006.

12

K. N. Berk.
A central limit theorem for m-dependent random variables with unbounded m.
Annals of Prob., 1(2):352-354, 1973.

13

M. Bertalmio, L. Vese, G. Sapiro, and S. Osher.
Simultaneous structure and texture image inpainting.
IEEE Trans. Image Proc., 12:882-889, 2003.

14

J. Besag.
Spatial interaction and the statistical analysis of lattice systems.
Journal of the Royal Statistical Society, series B, 36(2):192-236, 1974.

15

J. Besag.
Statistical Analysis of Non-Lattice Data.
Journal of the Royal Statistical Society, series B, 24(3):179-195, 1975.

16

J. Besag.
On the statistical analysis of dirty pictures.
Journal of the Royal Statistical Society, series B, 48:259-302, 1986.

17

S. J. Besson, M. Barlaud, and G. Aubert.
Dream2s: Deformable regions driven by an eulerian accurate minimization method for image and video segmentation.
In Proc. European Conf. on Computer Vision-Part III, pages 365-380, 2002.

18

J. Bigun, G. H. Granlund, and J. Wiklund.
Multidimensional orientation estimation with applications to texture analysis and optical flow.
IEEE Trans. Pattern Anal. Mach. Intell., 13(8):775-790, 1991.

19

J. Bilmes.
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models.
Technical report, University of Berkeley, 1997.

20

A. Blake and A. Zisserman.
Visual Reconstruction.
MIT Press, 1987.

21

R. Boomgaard and J. Weijer.
Robust estimation of orientation for texture analysis.
In 2nd Int. Workshop on Texture Analysis and Synthesis, 2002.

22

A. Buades, B. Coll, and J. M. Morel.
A non-local algorithm for image denoising.
In IEEE Int. Conf. Comp. Vis. Pattern Recog., volume 2, pages 60-65, 2005.

23

A. Buades, B. Coll, and J. M. Morel.
A review of image denoising algorithms, with a new one.
Multiscale Model. Simul., 4(2):490-530, 2005.

24

G. Casella.
An introduction to empirical bayes analysis.
American Statistician, 39(2):83-87, 1985.

25

K. Castleman.
Digital image processing.
Prentice Hall, 1996.

26

T. Chan, J. Shen, and L. Vese.
Variational pde models in image processing, 2003.

27

Y. Cheng.
Mean shift, mode seeking, and clustering.
IEEE Trans. Pattern Anal. Mach. Intell., 17(8):790-799, 1995.

28

D. Chopp and J. Sethian.
Motion by intrinsic laplacian of curvature.
Interfaces and Free Boundaries, 1:107-123, 1999.

29

Y. Chow, S. Geman, and L. Wu.
Consistant cross-validated density estimation.
Annals of Statistics, 11(1):25-38, 1983.

30

C. Cocosco, A. Zijdenbos, and A. Evans.
A fully automatic and robust brain MRI tissue classification method.
Medical Image Analysis, 7(4):513-527, 2003.

31

D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans.
Design and construction of a realistic digital brain phantom.
IEEE Trans. Med. Imag., 17(3):463-468, 1998.

32

D. Comaniciu and P. Meer.
Mean shift: A robust approach toward feature space analysis.
IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603-619, 2002.

33

C. B. Cordy and D. R. Thomas.
Deconvolution of a distribution function.
Journal of the American Statistical Association, 92(440):1459-1465, 1997.

34

T. M. Cover and J. A. Thomas.
Elements of Information Theory.
Wiley, 1991.

35

M. D. Craene, A. du Bois d'Aische, B. Macq, and S. K. Warfield.
Multi-subject registration for unbiased statistical atlas construction.
In MICCAI, pages 655-662, 2004.

36

R. V. Cristerna, V. M. Banuelos, and O. Y. Suarez.
Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation.
IEEE Trans. Biomedical Engineering, 51(3):459-470, 2004.

37

M. B. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J. Villemure, and J. Thiran.
Atlas-based segmentation of pathological MR brain images using a model of lesion growth.
IEEE Transactions on Medical Imaging, 23(10):1301- 1314, October 2004.

38

C. Davatzikos and J. Prince.
An active contour model for mapping the cortex.
IEEE Trans. Medical Imaging, 14(1):65-80, 1995.

39

J. S. de Bonet and P. Viola.
Texture recognition using a non-parametric multi-scale statistical model.
In Proc. IEEE Conf. on Comp. Vision and Pattern Recog., pages 641-647, 1998.

40

V. de Silva and G. Carlsson.
Topological estimation using witness complexes.
Symp. on Point-Based Graphics, 2004.

41

J. DeBonet and P. Viola.
A non-parametric multi-scale statistical model for natural images.
In Proc. Conf. on Adv. in Neural Info. Proc. Systems, pages 773-779, 1998.

42

F. Dellaert.
The expectation maximization algorithm.
Technical report, 2002.

43

A. P. Dempster, N. M. Laird, and D. B. Rubin.
Maximum likelihood from incomplete data via the EM algorithm.
Journal of the Royal Statistical Society, B(39):1-38, 1977.

44

L. R. Dice.
Measures of the amount of ecologic association between species.
Ecology, 26(3):297-302, 1945.

45

D. Donoho.
Denoising via soft threhsholding.
IEEE Trans. Info. Theory, 41(3):613-627, 1995.

46

G. Doretto, D. Cremers, P. Favaro, and S. Soatto.
Dynamic texture segmentation.
In Proc. Int. Conf. Computer Vision, pages 1236-1242, 2003.

47

E. Dougherty.
Random Processes for Image and Signal Processing.
Wiley, 1998.

48

R. Duda, P. Hart, and D. Stork.
Pattern Classification.
Wiley, 2001.

49

R. P. W. Duin.
On the choice of smoothing parameters for parzen estimators of probability density functions.
IEEE Trans. on Computers, C-25(11):1175-1179, 1976.

50

A. A. Efros and T. K. Leung.
Texture synthesis by non-parametric sampling.
In Int. Conf. Computer Vision, page 1033, 1999.

51

V. Elangovan and R. T. Whitaker.
From sinograms to surfaces: A direct approach to the segmentation of tomographic data.
In Proceedings of the 4th Int. Conf. Med. Imag. Comp. and Comp.-Assisted Intervention, pages 213-223. Springer-Verlag, 2001.

52

I. M. Elfadel and R. W. Picard.
Gibbs random fields, cooccurrences, and texture modeling.
IEEE Trans. Pattern Anal. Mach. Intell., 16(1):24-37, 1995.

53

S. Esedoglu, S. Ruuth, and R. Tsai.
Threshold dynamics for shape reconstruction and disocclusion.
In Proc. Int. Conf. Image Processing, pages 502-505, 2005.

54

S. Esedoglu and Y.-H. R. Tsai.
Threshold dynamics for the piecewise constant mumford-shah functional.
Number CAM-04-63, 2004.

55

M. Everett and J. Mancuso.
Reptiles, Stanford University, 2001.

56

A. Fan, W. Wells, J. Fisher, M. Çetin, S. Haker, R. Mulkern, C. Tempany, and A. Willsky.
A unified variational approach to denoising and bias correction in mr.
In Info. Proc. Med. Imag., pages 148-159, 2003.

57

M. Fashing and C. Tomasi.
Mean shift is a bound optimization.
IEEE Trans. Pattern Anal. Mach. Intell., 27(3):471-474, 2005.

58

N. Foster and R. Fedkiw.
Practical animations of liquids.
In E. Fiume, editor, SIGGRAPH 2001, Computer Graphics Proceedings, pages 23-30. ACM Press / ACM SIGGRAPH, 2001.

59

W. T. Freeman and E. H. Adelson.
The design and use of steerable filters.
IEEE Trans. Pattern Anal. Mach. Intell., 13(9):891-906, 1991.

60

K. Fukunaga and L. Hostetler.
The estimation of the gradient of a density function, with applications in pattern recognition.
IEEE Trans. Info. Theory, 21(1):32-40, 1975.

61

S. Geman and D. Geman.
Stochastic relaxation, gibbs distributions and the bayesian restoration of images.
IEEE Trans. Pattern Anal. Mach. Intell., 6:721-741, 1984.

62

S. Geman and C. Graffigne.
Markov random field image models and their applications to computer vision.
In Proc. Int. Congress of Mathematicians, pages 1496-1517, 1986.

63

S. Geman and C. R. Hwang.
Nonparametric maximum likelihood estimation by method of sieves.
Annals of Statistics, 10(2):401-414, 1982.

64

G. Gerig, O. Kubler, R. Kikinis, and F. A. Jolesz.
Nonlinear anisotropic filtering of MRI data.
IEEE Tr. Med. Imaging, 11(2):221-232, 1992.

65

R. C. Gonzalez and R. E. Woods.
Digital Image Processing.
Prentice Hall, 2001.

66

I. J. Good.
A nonparametric roughness penalty for probability densities.
Nature (London), 229:29-30, 1971.

67

I. J. Good and R. A. Gaskins.
Nonparametric roughness penalties for probability densities.
Biometrika, 58(2):255-277, 1971.

68

U. Grenander.
Abstract Inference.
Wiley, 1975.

69

R. Guillemaud and M. Brady.
Estimating the bias field of mr images.
IEEE Tr. Med. Imaging, 16(3):238-251, 1997.

70

P. Hall.
Cross-validation in density estimation.
Biometrika,, 69(2):382-390, 1982.

71

H. O. Hartley.
Maximum likelihood estimation from incomplete data.
Biometrics, 14:174-194, 1958.

72

R. Hartley.
Transmission of information.
Bell System Technical Journal, page 535, 1928.

73

W. Hastings.
Monte carlo sampling methods using markov chains and their applications.
Biometrika, 57:97-109, 1970.

74

S. Haykin, editor.
Unsupervised Adaptive Filtering.
Wiley, 2000.

75

D. Healy and J. Weaver.
Two applications of wavelet transforms in magnetic resonance imaging.
IEEE Trans. Info. Theory, 38(2):840-860, 1992.

76

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

77

M. Hilton, T. Ogden, D. Hattery, G. Jawerth, and B. Eden.
Wavelet denoising of functional MRI data.
pages 93-114. 1996.

78

W. Hoeffding and H. Robbins.
The central limit theorem for dependent random variables.
Duke Math J., 15:773-780, 1948.

79

J. Huang and D. Mumford.
Statistics of natural images and models.
In Proc. IEEE Comp. Vis. Pattern Recog., pages 1541-1547, 1999.

80

A. Hyvärinen, P. Hoyer, and E. Oja.
Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation.
Neural Computation, 11(7):1739-1768, 1999.

81

A. K. Jain.
Fundamentals of digital image processing.
Prentice-Hall, Inc., 1989.

82

B. Julesz.
Visual pattern discrimination.
IRE Trans. Info. Theory, IT(8):84-92, 1962.

83

T. Kadir and M. Brady.
Unsupervised non-parametric region segmentation using level sets.
In Proc. of IEEE Int. Conference on Computer Vision, pages 1267-1274, 2003.

84

J. N. Kapur.
Measures of Information and their Applications.
John Wiley, 1994.

85

J. N. Kapur and H. K. Kesavan.
Entropy Optimization Principles with Applications.
Academic Press, Inc., 1992.

86

T. Kapur, W. E. L. Grimson, W. M. Wells, and R. Kikinis.
Segmentation of brain tissue from magentic resonance images.
Med. Im. An., 1:109-127, 1996.

87

J. Kim, J. W. Fisher, A. J. Yezzi, M. Cetin, and A. S. Willsky.
A nonparametric statistical method for image segmentation using information theory and curve evolution.
IEEE Trans. Image Processing, 14(10):1486-1502, 2005.

88

B. Kimia, A. Tannebaum, and S. Zucker.
Shapes, shocks, and deformations I: the components of two-dimensional shape and the reaction-diffusion space.
Int. J. Comput. Vision, 15:189-224, 1995.

89

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi.
Optimization by simulated annealing.
Science, Number 4598, 13 May 1983, 220,4598:671-680, 1983.

90

N. Laird.
Nonparametric maximum likelihood estimation of a mixing distribution.
Journal of the American Statistical Association, 73(364):805-811, 1978.

91

A. Lee, K. Pedersen, and D. Mumford.
The nonlinear statistics of high-contrast patches in natural images.
Int. J. Comput. Vision, 54(1-3):83-103, 2003.

92

S. Lee and M. Vannier.
Post-acquisition correction of mr inhomogeneities.
Magnetic Resonance in Medicine, 36(2):275-286, 1996.

93

K. V. Leemput, F. Maes, D. Vandermeulen, and P. Seutens.
Automated model-based bias field correction of mr images of the brain.
IEEE Tr. Med. Imag., 18:885-896, 1999.

94

K. V. Leemput, F. Maes, D. Vandermeulen, and P. Seutens.
Automated model-based tissue classification of mr images of the brain.
IEEE Tr. Med. Imaging, 18:897-908, 1999.

95

A. E. Lefohn, J. E. Cates, and R. T. Whitaker.
Interactive, gpu-based level sets for 3d segmentation.
Medical Image Computing andComputer Assisted Intervention, pages 564-572, 2003.

96

A. E. Lefohn, J. M. Kniss, C. D. Hansen, and R. Whitaker.
Interactive deformation and visualization of level set surfaces using graphics hardware.
IEEE Visualization 2003, pages 75-82, October 2003.

97

M. Leventon, E. Grimson, and O. Faugeras.
Statistical shape influence in geodesic active contours.
In CVP'00, 2000.

98

E. Levina.
Statistical issues in texture analysis.
Ph.D. Dissertation, Department of Statistics, University of California, Berkeley, 1997.

99

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

100

M. Lysaker, A. Lundervold, and X. Tai.
Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time.
IEEE Trans. Imag. Proc., 2003.

101

R. Malladi and J. Sethian.
A unified approach to noise removal, image enhancement, and shape recovery.
IEEE Trans. Image Processing, 1996.

102

S. Mallat.
A Wavelet Tour of Signal Processing.
Academic Press Inc., 1999.

103

J. Mangin.
Entropy minimization for automatic correction of intensity nonuniformity.
In IEEE MMBIA, pages 162-169, 2000.

104

G. J. McLachlan.
The EM Algorithm and Extensions.
John Wiley, 1997.

105

B. Merriman, J. K. Bence, and S. Osher.
Motion of multiple junctions: A level set approach,.
Technical Report CAM-93-19, Dept. Mathematics, UCLA, 1993.

106

N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller.
Equations of state calculations by fast computing machines.
Journal of Chemical Physics, 21:1087-1092, 1953.

107

J. Miller and C. Stewart.
Muse: Robust surface fitting using unbiased scale estimates.
In Proc. of the Conf. Computer Vision and Pattern Recog., pages 300-306, June 1996.

108

T. Minka.
Expectation maximization as lower bound maximization.
1998.

109

G. Motta, E. Ordentlich, I. Ramirez, G. Seroussi, and M. J. Weinberger.
The dude framework for continuous tone image denoising.
In Int. Conf. Imag. Proc, pages III: 345-348, 2005.

110

D. Mumford and J. Shah.
Optimal approximations by piecewise smooth functions and associated variational problems.
Com. of Pure and Applied Math., 42:577-685, 1989.

111

R. Neal and G. Hinton.
A new view of the EM algorithm that justifies incremental and other variants.
Technical report, Department of Computer Science, University of Toronto, 1993.

112

R. Neal and G. Hinton.
A view of the EM algorithm that justifies incremental, sparse, and other variants.
In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers, 1998.

113

L. Nocera and J. Gee.
Robust partial volume tissue classification of cerebral mri scans.
In Proc. SPIE Medical Imaging: Image Processing, pages 312-322, 1997.

114

K. Nordstrom.
Biased anisotropic diffusion: a unified regularization and diffusion approach to edge detection.
Image Vision Comput., 8(4):318-327, 1990.

115

R. Nowak.
Wavelet-based rician noise removal for magnetic resonance imaging.
IEEE Trans. Imag. Proc., 8:1408-1419, 1999.

116

H. Nyquist.
Certain factors affecting telegraph speed.
Bell System Technical Journal, page 324, 1924.

117

S. Osher and R. Fedkiw.
Level Set Methods and Dynamic Implicit Surfaces.
Springer, 2003.

118

S. Osher and J. Sethian.
Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations.
Journal of Computational Physics, 79(1):12-49, 1988.

119

A. Owen.
Discussion of Ripley.
Canadian J. Statistics, 14:106-110, 1986.

120

A. Owen.
Image segmentation via iterated conditional expectations.
Technical report, 1989.

121

C. Pachai, Y. M. Zhu, C. Guttmann, R. Kikinis, F. A. Jolesz, G. Gimenez, J.-C. Froment, C. Confavreux, and S. K. Warfield.
Unsupervised and adaptive segmentation of multispectral 3d magnetic resonance images of human brain: a generic approach.
In Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention, pages 1067-1074, 2001.

122

R. D. Paget and D. Longstaff.
A nonparametric multiscale markov random field model for synthesising natural textures.
Fourth International Symposium on Signal Processing and its Applications, 2:744-747, 1996.

123

A. Papoulis and S. U. Pillai.
Probability, Random Variables, and Stochastic Processes.
McGraw-Hill, 4th edition, 2001.

124

N. Paragios and R. Deriche.
Geodesic active regions and level set methods for supervised texture segmentation.
Int. J. Comput. Vision, 46(3):223-247, 2002.

125

E. Parzen.
On the estimation of a probability density function and the mode.
Annals of Math. Stats., 33:1065-1076, 1962.

126

D. Peng, B. Merriman, H. Zhao, S. Osher, and M. Kang.
A pde based fast local level set method, 1999.

127

P. Perona and J. Malik.
Scale-space and edge detection using anisotropic diffusion.
IEEE Trans. Pattern Anal. Mach. Intell., 12(7):629-639, July 1990.

128

A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy.
A joint inter and intrascale statistical model for bayesian wavelet based image denoising.
IEEE Trans. Imag. Proc., 11:545-557, 2002.

129

A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy.
A versatile wavelet domain noise filtration technique for medical imaging.
IEEE Trans. Med. Imaging, 22(3):323-331, 2003.

130

K. Pohl, W. E. L. Grimson, S. Bouix, and R. Kikinis.
Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework.
In International Symposium on Biomedical Imaging, pages 81-84, 2004.

131

K. Popat and R. Picard.
Cluster based probability model and its application to image and texture processing.
IEEE Trans. Image Processing, 6(2):268-284, 1997.

132

J. Portilla.
Full blind denoising through noise covariance estimation using gaussian scale mixtures in the wavelet domain.
In Proc. IEEE Int. Conf. Image Proc., pages 1217-1220, October 2004.

133

J. Portilla, V. Strela, M. Wainwright, and E. Simoncelli.
Image denoising using scale mixtures of gaussians in the wavelet domain.
IEEE Trans. Imag. Proc., 12(11):1338-1351, 2003.

134

M. Prastawa, J. H. Gilmore, W. Lin, and G. Gerig.
Automatic segmentation of neonatal brain MRI.
In Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention, pages 10-17, 2004.

135

J. Principe, J. F. III, and D. Xu.
Information theoretic learning.
In S. Haykin, editor, Unsupervised Adaptive Filtering, pages 265-319. Wiley, 2000.

136

J. Rajapakse, J. Giedd, and J. Rapoport.
Statistical approach to segmentation of single-channel cerebral MR images.
IEEE Transactions on Medical Imaging, 16(2):176-186, 1997.

137

S. S. Rao.
Engineering Optimization, Theory and Practice.
Wiley, 1996.

138

A. Renyi.
On measures of information and entropy.
In Proc. 4th Berkeley Symposium on Mathematics, Statistics and Probability, pages 547-561, 1960.

139

D. E. Rex, J. Q. Ma, and A. W. Toga.
The LONI pipeline processing environment.
NeuroImage, 19:1033-1048, 2003.

140

H. Robbins.
The empirical bayes approach to statistical decision problems.
Annals of Mathematical Statistics, 35(1):1-20, 1964.

141

H. Robbins.
An empirical bayes approach to statistics.
In Proc. Third Berkeley Symp. Math. Stat. Prob., pages 157-164, 1964.

142

T. Rohlfing and C. R. M. Jr.
Multi-classifier framework for atlas-based image segmentation.
In IEEE Int. Conf. Comp. Vis. Pattern Recog., pages 255-260, 2004.

143

B. Romeny, editor.
Geometry-Driven Diffusion in Computer Vision.
Kluwer Academic Publishers, 1994.

144

M. Rousson, T. Brox, and R. Deriche.
Active unsupervised texture segmentation on a diffusion based feature space.
In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 699-706. IEEE Computer Society, 2003.

145

L. Rudin, S. Osher, and E. Fatemi.
Nonlinear total variation based noise removal algorithms.
Physica D, 60:259-268, 1992.

146

A. Ruf, H. Greenspan, and J. Goldberger.
Tissue classification of noisy mr brain images using constrained gmm.
In Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention, pages 790-797, 2005.

147

M. Rumpf and R. Strzodka.
Level set segmentation in graphics hardware.
In IEEE Int. Conf. Imag. Proc., pages 1103-1106, 2001.

148

C. Sagiv, N. A. Sochen, and Y. Y. Zeevi.
Texture segmentation via a diffusion segmentation scheme in the gabor feature space.
In 2nd Int. Workshop on Texture Analysis and Synthesis, 2002.

149

B. Sandberg, T. Chan, and L. Vese.
A level-set and gabor-based active contour algorithm for segmenting textured images.
Technical Report CAM-02-39, Dept. Mathematics, UCLA, 2002.

150

D. W. Scott.
Multivariate Density Estimation.
Wiley, 1992.

151

D. W. Scott and L. E. Factor.
Monte carlo study of three data-based nonparametric probability density estimators.
Journal of the American Statistical Association, 76(373):9-15, 1981.

152

L. Sendur and I. Selesnick.
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency.
IEEE Trans. Signal Proc., 50:2744-2756, 2002.

153

J. Sethian.
Level Set Methods and Fast Marching Methods.
Cambridge Univ. Press, 1999.

154

C. Shannon.
A mathematical theory of communication.
Bell System Tech. Journal, 27:379-423, July 1948.

155

B. Silverman.
Density Estimation for Statistics and Data Analysis.
Chapman and Hall, 1986.

156

J. S. Simonoff.
Smoothing Methods in Statistics.
Springer, 1996.

157

J. G. Sled, A. P. Zijdenbos, and A. C. Evans.
A nonparametric method for automatic correction of intensity nonuniformity in mri data.
IEEE Trans. Medical Imaging, 17:87-97, 1998.

158

D. L. Snyder, M. Miller, and T. Schultz.
Constrained probability-density estimation from noisy data.
In Proc. 22nd Annual Conference on Information Sciences and System, pages 170-172, 1988.

159

W. Snyder, Y. Han, G. Bilbro, R. Whitaker, and S. Pizer.
Image relaxation: Restoration and feature extraction.
IEEE Trans. Pattern Anal. Mach. Intell., 17(6):620-624, 1995.

160

J. Starck, E. Candes, and D. Donoho.
The curvelet transform for image denoising.
IEEE Trans. Imag. Proc., 11(6), 2000.

161

H. Stark and J. W. Woods.
Probability and random processes with applications to signal processing.
Prentice Hall, 2001.

162

M. Studeny and J. Vejnarova.
The multiinformation function as a tool for measuring stochastic dependence.
In M. I. Jordan, editor, Learning in Graphical Models, pages 261-297. Kluwer Academic Publishers, 1998.

163

T. Tasdizen, S. P. Awate, R. T. Whitaker, and N. L. Foster.
Mri tissue classification with neighborhood statistics: A nonparametric, entropy-minimizing approach.
In Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 3750, pages 517-525, 2005.

164

T. Tasdizen, R. Whitaker, P. Burchard, and S. Osher.
Geometric surface processing via normal maps.
ACM Trans. on Graphics, 2003.

165

A. Toga.
Brain Warping.
Academic Press, 1999.

166

C. Tomasi and R. Manduchi.
Bilateral filtering for gray and color images.
In Proc. Int. Conf. Comp. Vision, page 839. IEEE Computer Society, 1998.

167

K. S. Trivedi.
Probability and Statistics With Reliability, Queuing, and Computer Science Applications.
Wiley, 2001.

168

L. Vese and T. Chan.
A multiphase level set framework for image segmentation using the mumford and shah model.
Technical Report CAM-01-25, Dept. Mathematics, UCLA, 2001.

169

L. A. Vese and S. J. Osher.
Modeling textures with total variation minimization and oscillating patterns in image processing.
J. Sci. Comput., 19:553-572, 2003.

170

P. Viola and W. Wells.
Alignment by maximization of mutual information.
In Int. Conf. Comp. Vision, pages 16-23, 1995.

171

M. Wand and M. Jones.
Kernel Smoothing.
Chapman and Hall, 1995.

172

L. Wei and M. Levoy.
Order-independent texture synthesis.
Stanford University Computer Science Department Tech. Report TR-2002-01, 2002.

173

J. Weickert.
Anisotropic Diffusion in Image Processing.
Teubner-Verlag, 1998.

174

J. Weickert.
Coherence-enhancing diffusion filtering.
Int. J. Comp. Vis., 31:111-127, 1999.

175

T. Weissman, E. Ordentlich, G. Seroussi, S. Verdu, and M. Weinberger.
Universal discrete denoising: Known channel.
IEEE Trans. Info. Theory, 51(1):5-28, 2005.

176

W. M. Wells, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz.
Adaptive segmentation of MRI data.
IEEE Tr. Med. Imaging, 15(4):429-443, 1996.

177

R. Westermann, C. R. Johnson, and T. Ertl.
Topology preserving smoothing of vector fields.
IEEE Trans. Visualization and Computer Graphics, 2001.

178

R. Whitaker.
Reconstructing terrain maps from dense range data.
In IEEE Int. Conf. Image Processing, pages 165-168, October 2001.

179

R. T. Whitaker.
A level-set approach to 3d reconstruction from range data.
Int. J. Comput. Vision, 29(3):203-231, 1998.

180

R. T. Whitaker.
Reducing aliasing artifacts in iso-surfaces of binary volumes.
In Proceedings of the 2000 IEEE symposium on Volume visualization, pages 23-32. ACM Press, 2000.

181

R. T. Whitaker and D. T. Chen.
Embedded active surfaces for volume visualization.
In SPIE Medical Imaging 94, 1994.

182

D. Wolpert and W. Macready.
No free lunch theorems for optimization.
IEEE Trans. Evolutionary Computation, 1(1):67-82, 1997.

183

M. Yan and J. Karp.
An adaptive bayesian approach to three-dimensional MR brain segmentation.
In Proc. of Information processing in medical imaging, pages 201-213, 1995.

184

M. Yan and J. Karp.
Segmentation of 3D brain MR using an adaptive k-means clustering algorithm.
In Proc. of the 1994 Nuclear Science Symposium and Medical Imaging Conference, pages 1529-1533, 1995.

185

Yang, Duraiswami, Gumerov, and Davis.
Improved fast gauss transform and efficient kernel density estimation.
In Int. Conf. Comp. Vision, pages 464-471, 2003.

186

H. Zhang.
Image restoration: Flexible neighborhood systems and iterated conditional expectations.
Statistica Sinica, 3:117-139, 1993.

187

Y. Zhang, M. Brady, and S. Smith.
Segmentation of brain mr images through a hidden markov random field model and the expectation maximization algorithm.
IEEE Tr. Med. Imaging, 20:45-57, 2001.

188

S. C. Zhu and D. Mumford.
Prior learning and gibbs reaction-diffusion.
IEEE Trans. Pattern Analysis Machine Intell., 19(11):1236-1250, 1997.

189

S. C. Zhu, Y. Wu, and D. Mumford.
Frame: Filters, random field and maximum entropy: Towards a unified theory for texture modeling.
Int. J. Computer Vision, 27(2):107-126, 1998.