Information Technology Reference
In-Depth Information
12. Huang X, Metaxas D, Chen T (2004) Metamorphs: deformable shape and texture models. In:
Proceedings IEEE CVPR, pp 496
-
503
13. Besag JE (1986) On the statistical analysis of dirty pictures. J R Stat Soc B 48:259
-
302
14. North OH (1943) An analysis of the factors which determine signal/noise discrimination in
pulsed-carrier systems. In: Proceedings of IEEE: RCA Labs, Princeton, NJ, Rep. PTR-6C,
pp 1016
1063
15. Kumar BVKV, Savvides M, Xie C (2006) Correlation pattern recognition for face recognition.
Proc IEEE 94(11):1963
-
1976
16. Aslan MS, Ali A, Rara H, Farag AA (2010) An automated vertebra identification and
segmentation in CT images. In: Proceedings of 5th international conference on image
processing (ICIP
-
236
17. Kim Y, Kim D (2009) A fully automatic vertebra segmentation method using 3D deformable
fences. Comput Med Imaging Graph 33:343
'
10), pp 233
-
352
18. Moon T (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 11:47
-
60
19. Borman S (2004) The expectation maximization algorithm: a short tutorial. Technical report
available at
http://www.seanborman.com/publications
20. Ali AM, Farag AA (2008) Automatic lung segmentation of volumetric low-dose CT scans
using graph cuts. In: Proceedings of the international symposium on visual computing
(ISVC
'
08), pp 258
-
267
21. Dubes RC, Jain AK (1989) Random
field models in image analysis. J Appl Stat 16:131
-
164
22. Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian
restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721
-
741
23. Derin H, Elliott H (1987) Modeling and segmentation of noisy and textured images using
gibbs random fields. IEEE Trans Pattern Anal Mach Intell 9(1):39
-
55
-
24. Ali A, Farag AA, Gimel
farb G (2008) A comparative study of energy minimization methods
for Markov random fields. In: Proceedings of ECCV
25. Cootes TF, Edwards GJ, Taylor CJ (1998) Active appearance models. In: Proceedings
European conference on computer vision
26. El Munim HEA, Farag AA (2007) Curve/surface representation and evolution using vector
level sets with application to the shape-based segmentation problem. IEEE Trans Pattern Anal
Mach Intell 29(6):945
'
958
27. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A
shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans
Med Imaging 22(2):137
-
154
28. Cremers D, Osher SJ, Soatto S (2006) Kernel density estimation and intrinsic alignment for
shape priors in level set segmentation. Int J Comput Vision 69(3):335
-
351
29. Aslan MS, Mostafa E, Abdelmunim H, Shalaby A, Farag AA (2011) A novel probabilistic
simultaneous segmentation and registration using level set. In: Proceedings of the international
conference on image processing, pp 2161
-
2164
30. Aslan MS, Shalaby A, Farag AA (2013) Clinically desired segmentation method for vertebral
bodies. In: IEEE 10th international symposium on biomedical imaging (ISBI), pp 840
-
843
31. Goldman LW (2007) Principles of CT: radiation dose and image quality. J Nucl Med Technol
35:213
-
225
32. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern
Anal Mach Intell 23(6):681
-
685
33. Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new
variational formulation. In: Proceedings of the 2005 IEEE Computer Society conference on
computer vision and pattern recognition
34. Ali AA, Farag AA, El-Baz AS (2007) Graph cuts framework for kidney segmentation with
prior shape constraints.
-
In: Proceedings of international conference on medical
image
computing and computer-assisted intervention (MICCAI
'
07), pp 384
-
392
35. Ali AM, Farag AA (2008) Density estimation using a new AIC-type criterion and the EM
algorithm for a linear combination of gaussians. In: Proceedings of IEEE international
conference on image processing (ICIP
'
08), pp 3024
-
3027
