Biomedical Engineering Reference
In-Depth Information
4. McInerney T, Terzopoulos D. 1996. Deformable models in medical images analysis: a survey.
Med Image Anal
1
(2):91-108.
5. Paragios N, Deriche R. 1999. Geodesic active contours for supervised texture segmentation. In
Proceedings of the IEEE conference on computer vision and pattern recognition
, Vol. 2, pp.
422-427. Washington, DC: IEEE Computer Society.
6. Pujol O, Radeva P. 2004. Texture segmentation by statistic deformable models.
Int J Image
Graphics
4
(3):433-452.
7. Jehan-Besson S, Barlaud M, Aubert G. 2003. DREAMS: deformable regions driven by an
Eulerian accurate minimization method for image and video segmentation. Submitted to
Int
J Comput Vision
.
8. Pujol O, Gil D, Radeva P. 2005. Fundamentals of stop and go active models.
J Image Vision
Computing
23
(8):681-691.
9. McInerney T, Terzopoulos D. 2000. T-Snakes: topology adaptative snakes.
Med Image Anal
4
:73-91.
10. Osher S, Sethian JA. 1988. Fronts propagating with curvature-dependent speed: algorithms
based on Hamilton-Jacobi formulations,
J Comput Phys
79
:12-49.
11. Ronfard R. 1994. Region-based strategies for active contour models.
Int J Comput Vision
13
(2):229-251.
12. Zhu SC. 1994.
Region competition: unifying snakes, region growing, and Bayes/MDL for
multi-band image segmentation
. Technical Report 94-10, Harvard Robotics Laboratory.
13. Yezzi A, Tsai A, Willsky A. 1999. A statistical approach to snakes for bimodal and trimodal
imagery. In
Proceedings of the seventh IEEE international conference on computer vision
,
pp. 898-903. Washington, DC: IEEE Computer Society.
14. Chakraborty A, Staib L, Duncan J. 1996. Deformable boundary finding in medical images by
integrating gradient and region information.
IEEE Trans Med Imaging
15
:859-870.
15. Samson C, Blanc-Feraud L, Aubert G, Zerubia J. 1999. A level set model for image classifica-
tion. In
Proceedings of the second international conference on scale-space theories in computer
vision
.
Lecture notes in computer science
, Vol. 1682, pp. 306-317.
16. Xu C, Yezzi A, Prince J. 2000. On the relationship between parametric and geometric active
contours. In
Proceedings of the 34th Asilomar conference on signals, systems, and computers
,
pp. 483-489. Washington, DC: IEEE Computer Society.
17. Evans LC. 1993.
Partial differential equations
. Berkeley Mathematics Lecture Notes, vol. 3B.
Berkeley: UCB Press.
18. Tveito A, Winther R. 1998.
Introduction to partial differential equations
. Texts in Applied
Mathematics, no 29. New York: Springer.
19. Xu C, Prince JL. 1998. Generalized gradient vector flow: external forces for active contours.
Signal Process Int J
71
(2):132-139.
20. Gil D, Radeva P. 2005. Curvature vector flow to assure convergent deformable models.
Comput
Vision Graphics Image Process
99
(1):118-125. EMMCVP'03.
21. Haralick R, Shanmugam K, Dinstein I. 1973. Textural features for image classification.
IEEE
Trans Syst Mach Cybern
3
:610-621.
22. Tuceryan M. 1994. Moment based texture segmentation.
Pattern Recognit Lett
15
:659-668.
23. Lindeberg T. 1994.
Scale-space theory in computer vision
. New York: Kluwer Academic.
24. Jain A, Farrokhnia F. 1990. Unsupervised texture segmentation using gabor filters.
Proceedings
of the international conference on systems, man and cybernetics
, pp. 14-19. Washington, DC:
IEEE Computer Society.
25. Mallat S. 1989. A theory for multiresolution signal decomposition: the wavelet representation.
IEEE Trans Pattern Anal Machine Intell
11
(7):674-694.
26. Mandelbrot B. 1983.
The fractal geometry of nature
. New York: W.H. Freeman.
27. Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution grayscale and rotation invariant tex-
ture classificationwith local binary patterns.
IEEE Trans Pattern Anal Machine Intell
24
(7):971-
987.
