Log in

Combination of Piecewise-Geodesic Paths for Interactive Segmentation

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. Note that, in the entire paper, curves will be assumed to be defined over the normalized range \([0,1]\).

  2. 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.

    Article  Google Scholar 

  • Arnold, V. I. (1994). Plane curves, their invariants, perestroikas and classification. Advances in Soviet Mathematics: Singularities and Bifurcations, 21, 33–91.

    Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient N-D segmentation. International Journal of Computer Vision, 70(2), 109–131.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61–79.

    Article  MATH  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • Chan, T., & Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.

    Article  MATH  Google Scholar 

  • Cohen, L., & Kimmel, R. (1997). Global minimum for active contour models: A minimal path approach. International Journal of Computer Vision, 24(1), 57–78.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ivins, J., & Porrill, J. (1995). Active region models for segmenting textures and colours. Image and Vision Computing, 13(5), 431–438.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Lankton, S., & Tannenbaum, A. (2008). Localizing region-based active contours. IEEE Transactions on Image Processing, 17(11), 2029–2039.

    Article  MathSciNet  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Mortensen, E., & Barrett, W. (1998). Interactive segmentation with intelligent scissors. Graphical Models and Image Processing, 60(5), 349–384.

    Article  MATH  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sagiv, C., Sochen, N., & Zeevi, Y. (2006). Integrated active contours for texture segmentation. IEEE Transactions on Image Processing, 15(6), 1633–1646.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Tsitsiklis, J. N. (1995). Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control, 40(9), 1528–1538.

    Article  MATH  MathSciNet  Google Scholar 

  • Unal, G., Yezzi, A., & Krim, H. (2005). Information-theoretic active polygons for unsupervised texture segmentation. International Journal of Computer Vision, 62(3), 199–220.

    Article  Google Scholar 

  • 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.

    MathSciNet  Google Scholar 

  • Yen, J. Y. (1971). Finding the K shortest loopless paths in a network. Management Science, 17(11), 712–716.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julien Mille.

Additional information

Communicated by M. Hebert.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1 (PDF 965 kb)

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:

$$\begin{aligned} \phi (u,v) = \left\| \mathcal {C}(u)-\mathcal {C}(v) \right\| ^p \end{aligned}$$

where \(p>1\) is a real exponent. The length of the zero level set of \(\phi _\mathcal {C}\),

$$\begin{aligned} \left| \mathcal {Z}_\mathcal {C}\right| = \int _0^L \int _0^L \delta (\phi (u,v))\left\| \nabla \phi (u,v) \right\| {\text {d}}u {\text {d}}v, \end{aligned}$$
(19)

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):

$$\begin{aligned} \nabla \phi (u,v)&= \left[ \phi _u(u,v)~~\phi _v(u,v) \right] ^T \nonumber \\&= p \left\| \mathcal {C}(u)-\mathcal {C}(v) \right\| ^{p-2} \left[ \begin{aligned} \mathcal {C}'(u) \cdot (\mathcal {C}(u)-\mathcal {C}(v)) \\ -\mathcal {C}'(v) \cdot (\mathcal {C}(u)-\mathcal {C}(v)) \end{aligned} \right] \end{aligned}$$

\(\square \)

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:

$$\begin{aligned} \delta (\phi (u,v)) = \frac{\delta (u-v)}{\left| \phi _u(v,v) \right| } \end{aligned}$$
(20)

Integrating (20) into (19) and applying the definition of measure \(\delta \):

$$\begin{aligned} \left| \mathcal {Z}_\mathcal {C}\right|&= \int _0^L \int _0^L \delta (\phi (u,v))\left\| \nabla \phi (u,v) \right\| {\text {d}}u {\text {d}}v \\&= \int _0^L \int _0^L \frac{\delta (u-v)}{\left| \phi _u(v,v) \right| } \left\| \nabla \phi (u,v) \right\| {\text {d}}u {\text {d}}v \\&= \int _0^L \frac{\left\| \nabla \phi (v,v) \right\| }{\left| \phi _u(v,v) \right| } {\text {d}}v \end{aligned}$$

Expanding the gradient gives:

$$\begin{aligned} \left| \mathcal {Z}_\mathcal {C}\right|&= \int _0^L \left\{ \right. \frac{p \left\| \mathcal {C}(v)-\mathcal {C}(v) \right\| ^{p-2}}{p \left\| \mathcal {C}(v)-\mathcal {C}(v) \right\| ^{p-2}} \\&\frac{ \sqrt{2(\mathcal {C}'(v)\cdot (\mathcal {C}(v)-\mathcal {C}(v)))^2}}{ \left| \mathcal {C}'(v)\cdot (\mathcal {C}(v)-\mathcal {C}(v))\right| } \left. \right\} {\text {d}}v \\&= \int _0^L \sqrt{2}~{\text {d}}v \\&= L\sqrt{2} \end{aligned}$$

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:

$$\begin{aligned} \left| {\Omega _{\text {in}}}(\mathcal {C}) \right|&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp (u) \cdot {\mathcal {C}}'(u)~{\text {d}}u \\&= \frac{1}{2} \int _0^1 x(u){y}'(u) - {x}'(u)y(u)~{\text {d}}u \end{aligned}$$

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:

$$\begin{aligned} \mathcal {X}[\mathcal {C}] = \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}'{\text {d}}u + \frac{1}{2}\mathcal {C}^\perp (1) \cdot \mathcal {C}(0) \end{aligned}$$
Fig. 15
figure 15

The exteriority of an open curve is measured as the signed area of the multiple connected region that it forms with the line segment joining its two endpoints

Proof

Let \(S\) be the parametrization of the line segment joining \(\mathcal {C}(1)\) and \(\mathcal {C}(0)\), over \([0,1]\):

$$\begin{aligned} S(u) = (1-u)\mathcal {C}(1)+u\mathcal {C}(0) \end{aligned}$$

The signed area is then obtained by applying Green’s theorem on a piecewise basis:

$$\begin{aligned} \mathcal {X}[\mathcal {C}]&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}' {\text {d}}u + \frac{1}{2} \int _0^1 S^\perp (u)\cdot {S}'(u) {\text {d}}u \\&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}' {\text {d}}u \\&\quad + \frac{1}{2} \int _0^1 ((1-u)\mathcal {C}^\perp (1)+u\mathcal {C}^\perp (0))\cdot (\mathcal {C}(0)-\mathcal {C}(1)) {\text {d}}u \\&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}' {\text {d}}u \\&\quad + \frac{1}{2} \int _0^1 (1-u)\mathcal {C}^\perp (1) \cdot \mathcal {C}(0) +u\mathcal {C}^\perp (1)\cdot \mathcal {C}(0) {\text {d}}u \\&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}' {\text {d}}u + \frac{1}{2} \int _0^1 \mathcal {C}^\perp (1) \cdot \mathcal {C}(0) {\text {d}}u \\&= \frac{1}{2} \int _0^1 \mathcal {C}^\perp \cdot {\mathcal {C}}' {\text {d}}u + \frac{1}{2} \mathcal {C}^\perp (1) \cdot \mathcal {C}(0) \end{aligned}$$

\(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-014-0751-3

Keywords

Navigation