The role of deformable models [1, 2, 3], in medical image analysis [4] has been increasing over the past two decades. The location of a pathology, the study of anatomical structures, computer-assisted surgery, or quantification of tissue volumes are a few of the applications in which deformable models have proved to be very effective. Due to their importance, the study and improvement of these models is still a challenge [5, 6, 7, 8]. These techniques are used to give a highlevel interpretation of low-level information such as contours or isolated regions.
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9 References
Kass M, Witkin A, Terzopoulos D. 1988. Snakes: active contour models. Int J Comput Vision 1:321-331.
Caselles V, Catte F, Coll T, Dibos F. 1993. A geometric model for actives contours. Num Math 66:1-31.
Cohen LD. On active contour models and ballons. Comput Vision Graphics Image Process: Image Understand 53(2):211-218, 1991.
McInerney T, Terzopoulos D. 1996. Deformable models in medical images analysis: a survey. Med Image Anal 1(2):91-108.
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.
Pujol O, Radeva P. 2004. Texture segmentation by statistic deformable models. Int J Image Graphics 4(3):433-452.
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.
Pujol O, Gil D, Radeva P. 2005. Fundamentals of stop and go active models. J Image Vision Computing 23(8):681-691.
McInerney T, Terzopoulos D. 2000. T-Snakes: topology adaptative snakes. Med Image Anal 4:73-91.
Osher S, Sethian JA. 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations, J Comput Phys 79:12-49.
Ronfard R. 1994. Region-based strategies for active contour models. Int J Comput Vision 13(2):229-251.
Zhu SC. 1994. Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. Technical Report 94-10, Harvard Robotics Laboratory.
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.
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.
Samson C, Blanc-F éraud L, Aubert G, Zerubia J. 1999. A level set model for image classification. In Proceedings of the second international conference on scale-space theories in computer vision. Lecture notes in computer science, Vol. 1682, pp. 306-317.
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.
Evans LC. 1993. Partial differential equations. Berkeley Mathematics Lecture Notes, vol. 3B. Berkeley: UCB Press.
Tveito A, Winther R. 1998. Introduction to partial differential equations. Texts in Applied Mathematics, no 29. New York: Springer.
Xu C, Prince JL. 1998. Generalized gradient vector flow: external forces for active contours. Signal Process Int J 71(2):132-139.
Gil D, Radeva P. 2005. Curvature vector flow to assure convergent deformable models. Comput Vision Graphics Image Process 99(1):118-125. EMMCVP’03.
Haralick R, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Trans Syst Mach Cybern 3:610-621.
Tuceryan M. 1994. Moment based texture segmentation. Pattern Recognit Lett 15:659-668.
Lindeberg T. 1994. Scale-space theory in computer vision. New York: Kluwer Academic.
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.
Mallat S. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11(7):674-694.
Mandelbrot B. 1983. The fractal geometry of nature. New York: W.H. Freeman.
Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Machine Intell 24(7):971-987.
Julesz B. 1962. Visual pattern discrimination. IRE Trans Inf Theory, 8:84-92.
P. Ohanian, Dubes R. 1992. Performance evaluation for four classes of textural features. Pattern Recognit 25(8):819-833.
Salembier P, Garrido L. 2000. Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans Image Process 9(4):561-576.
Duda R, Hart P. 2001. Pattern classification, 2d ed. New York: Wiley-Interscience.
Paragios N, Deriche R. 1999. Unifying boundary and region-based information for geodesic active tracking. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, Vol. 2, pp. 300-305. Washington, DC: IEEE Computer Society.
Pujo O. A semi-supervised statistical framework and generative snakes for IVUS analysis. PhD dissertation, Universidad Aut ónoma de Barcelona.
Hofmann T, Puzicha J, Buhmann JM. 1998. Unsupervised texture segmentation in a determin- istic annealing framework. IEEE Trans Pattern Anal Machine Intell 20(8):803-818.
Sapiro G. 1997. Color snakes. Comput Vision Image Understand 68(2):247-253.
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Pujol, O., Radeva, P. (2007). Alternate Spaces For Model Deformation: Application Of Stop And Go Active Models To Medical Images. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_9
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