Abstract
Minimum cost paths have been extensively studied theoretical tools for interactive image segmentation. The existing geodesically linked active contour (GLAC) model, which basically consists of a set of vertices connected by paths of minimal cost, blends the benefits of minimal paths and region-based active contours. This results in a closed piecewise-smooth curve, over which an edge or region energy functional can be formulated. As an important shortcoming, the GLAC in its initial formulation does not guarantee the curve to be simple, consistent with respect to the purpose of segmentation. In this paper, we draw our inspiration from the GLAC and other boundary-based interactive segmentation algorithms, in the sense that we aim to extract a contour given a set of user-provided points, by connecting these points using paths. The key idea is to select a combination among a set of possible paths, such that the resulting structure represents a relevant closed curve. Instead of considering minimal paths only, we switch to a more general formulation, which we refer to as admissible paths. These basically correspond to the roads travelling along the bottom of distinct valleys between given endpoints. We introduce a novel term to favor the simplicity of the generated contour, as well as a local search method to choose the best combination among possible paths.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig2_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig4_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig5_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig6_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig7_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig8_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig9_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig10_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig11_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig12_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig13_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11263-014-0751-3/MediaObjects/11263_2014_751_Fig14_HTML.jpg)
Similar content being viewed by others
Notes
Note that, in the entire paper, curves will be assumed to be defined over the normalized range \([0,1]\).
In our framework, curves with points of multiplicity\(>2\) are detected and excluded from the search.
References
Adams, C. C. (2004). The Knot book: An elementary introduction to the mathematical theory of knots. American Mathematical Society.
Appleton, B., & Talbot, H. (2006). Globally minimal surfaces by continuous maximal flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1), 106–118.
Arnold, V. I. (1994). Plane curves, their invariants, perestroikas and classification. Advances in Soviet Mathematics: Singularities and Bifurcations, 21, 33–91.
Ben-Zadok, N., Riklin-Raviv, T., & Kiryati, N. (2009). Interactive level-set segmentation for image guided therapy. In IEEE International symposium on biomedical imaging: From Nano to Macro (ISBI) (pp. 1079–1082). Boston, USA.
Benmansour, F., & Cohen, L. (2009). Fast object segmentation by growing minimal paths from a single point on 2D or 3D images. Journal of Mathematical Imaging and Vision, 33(2), 209–221.
Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient N-D segmentation. International Journal of Computer Vision, 70(2), 109–131.
Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.-P., & Osher, S. (2007). Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and vision, 28(2), 151–167.
Brox, T., & Cremers, D. (2009). On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional. International Journal of Computer Vision, 84(2), 184–193.
Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61–79.
Chan, T., Esedoglu, S., & Nikolova, M. (2006). Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics, 66(5), 1632–1648.
Chan, T., & Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.
Cohen, L., & Kimmel, R. (1997). Global minimum for active contour models: A minimal path approach. International Journal of Computer Vision, 24(1), 57–78.
Crandall, M. G., Ishii, H., & Lions, P.-L. (1992). User’s guide to viscosity solutions of second order partial differential equations. Bulletin of the American Mathematical Society, 27, 1–67.
Cremers, D., Fluck, O., Rousson, M., & Aharon, S. (2007). A probabilistic level set formulation for interactive organ segmentation. In SPIE medical imaging (Vol. 6512). San Diego, USA.
Eppstein, D. (1998). Finding the \(k\) shortest paths. SIAM Journal of Computing, 28(2), 652–673.
Falcão, A., Stolfi, J., & Lotufo, R. (2004). The image foresting transform: Theory, algorithms, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1), 19–29.
Falcão, A., Udupa, J., & Miyazawa, F. (2000). An ultra-fast user-steered image segmentation paradigm: Live Wire on the Fly. IEEE Transactions on Medical Imaging, 19(1), 55–62.
Gao, Y., Kikinis, R., Bouix, S., Shenton, M., & Tannenbaum, A. (2012). A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. Medical Image Analysis, 16(6), 1216–1227.
Gérard, O., Deschamps, T., Greff, M., & Cohen, L. D. (2002). Real-time interactive path extraction with on-the-fly adaptation of the external forces. In European conference on computer vision (ECCV) (Vol. 3, pp. 807–821). Copenhagen: Denmark.
Grady, L. (2006). Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), 1768–1783.
Ivins, J., & Porrill, J. (1995). Active region models for segmenting textures and colours. Image and Vision Computing, 13(5), 431–438.
Karasev, P., Kolesov, I., Fritscher, K., Vela, P., Mitchell, P., & Tannenbaum, A. (2013). Interactive medical image segmentation using PDE control of active contours. IEEE Transactions on Medical Imaging, 32(11), 2127–2139.
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331.
Kaul, V., Yezzi, A., & Tsai, Y. (2012). Detection of curves with unknown endpoints and arbitrary topology using minimal paths. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10), 1952–1965.
Kim, J., Fisher, J. W., Yezzi, A., Çetin, M., & Willsky, A. S. (2005). A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Transactions on Image Processing, 14(10), 1486–1502.
Lankton, S., & Tannenbaum, A. (2008). Localizing region-based active contours. IEEE Transactions on Image Processing, 17(11), 2029–2039.
Li, Y., Sun, J., Tang, C.-K., & Shum, H.-Y. (2006). Lazy snap**. ACM Transactions on Graphics (TOG)—Proceedings of ACM SIGGRAPH 2004, 23(3), 303–308.
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2), 158–175.
Michailovich, O., Rathi, Y., & Tannenbaum, A. (2007). Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Transactions on Image Processing, 16(11), 2787– 2801.
Mille, J., Bougleux, S., & Cohen, L. (2012). Minimally overlap** paths sets for closed contour extraction. In International conference on computer vision theory and applications (VISAPP), Rome, Italy.
Mille, J., Bougleux, S., & Cohen, L. (2013). Combination of paths for interactive segmentation. In British machine vision conference (BMVC), Bristol, UK.
Mille, J., & Cohen, L. (2009). Geodesically linked active contours: evolution strategy based on minimal paths. In 2nd international conference on scale space and variational methods in computer vision (SSVM), LNCS (Vol. 5567, pp 163–174), Voss, Norway, 2009. Springer.
Miranda, P., Falcão, A., & Spina, T. (2012). Riverbed: A novel user-steered image segmentation method based on optimum boundary tracking. IEEE Transactions on Image Processing, 21(6), 3042–3052.
Mortensen, E., & Barrett, W. (1998). Interactive segmentation with intelligent scissors. Graphical Models and Image Processing, 60(5), 349–384.
Mumford, D., & Shah, J. (1989). Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42(5), 577–685.
Paragios, N., & Deriche, R. (2002). Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision, 46(3), 223–247.
Rother, C., Kolmogorov, V., & Blake, A. (2004). Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3), 309–314.
Sagiv, C., Sochen, N., & Zeevi, Y. (2006). Integrated active contours for texture segmentation. IEEE Transactions on Image Processing, 15(6), 1633–1646.
Sethian, J. A. (1996). A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Science, 93(4), 1591–1595.
Sethian, J. A. (1999). Level sets methods and fast marching methods (2nd ed.). Cambridge: Cambridge University Press.
Tsitsiklis, J. N. (1995). Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control, 40(9), 1528–1538.
Unal, G., Yezzi, A., & Krim, H. (2005). Information-theoretic active polygons for unsupervised texture segmentation. International Journal of Computer Vision, 62(3), 199–220.
Vicente, S., Kolmogorov, V., & Rother, C. (2008). Graph cut based image segmentation with connectivity priors. In IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA.
Whitney, H. (1937). On regular closed curves in the plane. Compositio Mathematica, 4, 276–284.
Yen, J. Y. (1971). Finding the K shortest loopless paths in a network. Management Science, 17(11), 712–716.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by M. Hebert.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix: Mathematical Derivations of Overlap and Exteriority Terms
Appendix: Mathematical Derivations of Overlap and Exteriority Terms
1.1 Overlap Term
Let \(\mathcal {C}\) be a regular curve parameterized over \([0,L]\). Let \(\phi \) be a \(C^1\) function defined over \([0,L]^2\) representing the distance between two positions on the curve:
where \(p>1\) is a real exponent. The length of the zero level set of \(\phi _\mathcal {C}\),
quantifies the self-overlap of \(\mathcal {C}\).
Proposition
If \(\mathcal {C}\) is simple, i.e. without self-intersection and self-tangency, then \(\left| \mathcal {Z}_\mathcal {C}\right| = L\sqrt{2}\).
Proof
As a preliminary calculation, let us express the gradient of \(\phi \) (partial derivatives are written using the indexed notation):
\(\square \)
If \(\mathcal {C}\) is regular and simple, varying with respect to \(u\) in range \([0,L], \phi (u,v)\) is nowhere zero except when \(u=v\). Hence, for a fixed \(v\), we have:
Integrating (20) into (19) and applying the definition of measure \(\delta \):
Expanding the gradient gives:
1.2 Exteriority Term
Let \(\mathcal {C}\) be a piecewise-smooth regular curve parameterized over \([0,1]\). If it is simple and positively oriented such that normal vector \({\mathcal {C}}'^\perp \) points inward, its inner area may be expressed using Green’s theorem:
When one calculates the previous expression on a non-simple closed curve, one gets the signed area, in which positively and negatively oriented connected components have positive and negative contributions, respectively.
Proposition
The signed area formed by an open curve \(\mathcal {C}\) over \([0,1]\) and the line segment from \(\mathcal {C}(1)\) returning to \(\mathcal {C}(0)\) (see Fig. 15), which we use to as the exteriority measure in Sect. 5.2, may be expressed as:
Proof
Let \(S\) be the parametrization of the line segment joining \(\mathcal {C}(1)\) and \(\mathcal {C}(0)\), over \([0,1]\):
The signed area is then obtained by applying Green’s theorem on a piecewise basis:
\(\square \)
Rights and permissions
About this article
Cite this article
Mille, J., Bougleux, S. & Cohen, L.D. Combination of Piecewise-Geodesic Paths for Interactive Segmentation. Int J Comput Vis 112, 1–22 (2015). https://doi.org/10.1007/s11263-014-0751-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-014-0751-3