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Powerful Parallel and Symmetric 3D Thinning Schemes Based on Critical Kernels

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Abstract

The main contribution of the present article consists of new 3D parallel and symmetric thinning schemes which have the following qualities:

  • They are effective and sound, in the sense that they are guaranteed to preserve topology. This guarantee is obtained thanks to a theorem on critical kernels;

  • They are powerful, in the sense that they remove more points, in one iteration, than any other symmetric parallel thinning scheme;

  • They are versatile, as conditions for the preservation of geometrical features (e.g., curve extremities or surface borders) are independent of those accounting for topology preservation;

  • They are efficient: we provide in this article a small set of masks, acting in the grid ℤ3, that is sufficient, in addition to the classical simple point test, to straightforwardly implement them.

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References

  1. Aktouf, Z., Bertrand, G., Perroton, L.: A three-dimensional holes closing algorithm. Pattern Recognit. Lett. 23(5), 523–531 (2002)

    Article  MATH  Google Scholar 

  2. Alexandroff, P.: Diskrete Räume. Mat. Sb. 2, 501–518 (1937)

    Google Scholar 

  3. Alexandroff, P., Hopf, H.: Topologie, I. Springer, Berlin (1935)

    Book  Google Scholar 

  4. Bernard, T., Manzanera, A.: Improved low complexity fully parallel thinning algorithm. In: Proceedings 10th International Conference on Image Analysis and Processing (ICIAP’99) (1999)

    Google Scholar 

  5. Bertrand, G.: Simple points, topological numbers and geodesic neighborhoods in cubic grids. Pattern Recognit. Lett. 15, 1003–1011 (1994)

    Article  Google Scholar 

  6. Bertrand, G.: On P-simple points. C. R. Acad. Sci., Ser. 1 Math. 321, 1077–1084 (1995)

    MATH  MathSciNet  Google Scholar 

  7. Bertrand, G.: New notions for discrete topology. In: Discrete Geometry for Computer Imagery. LNCS, vol. 1568, pp. 218–228. Springer, Berlin (1999)

    Chapter  Google Scholar 

  8. Bertrand, G.: On critical kernels. C. R. Acad. Sci., Ser. 1 Math. I(345), 363–367 (2007)

    Article  MathSciNet  Google Scholar 

  9. Bertrand, G., Aktouf, Z.: A three-dimensional thinning algorithm using subfields. In: Vision Geometry III, vol. 2356, pp. 113–124. SPIE, Bellingham (1996)

    Chapter  Google Scholar 

  10. Bertrand, G., Couprie, M.: A new 3D parallel thinning scheme based on critical kernels. In: Discrete Geometry for Computer Imagery. LNCS, vol. 4245, pp. 580–591. Springer, Berlin (2006)

    Chapter  Google Scholar 

  11. Bertrand, G., Couprie, M.: Two-dimensional parallel thinning algorithms based on critical kernels. J. Math. Imaging Vis. 31(1), 35–56 (2008)

    Article  MathSciNet  Google Scholar 

  12. Bertrand, G., Couprie, M.: On parallel thinning algorithms: minimal non-simple sets, P-simple points and critical kernels. J. Math. Imaging Vis. 35(1), 23–35 (2009)

    Article  MathSciNet  Google Scholar 

  13. Bertrand, G., Malandain, G.: A new characterization of three-dimensional simple points. Pattern Recognit. Lett. 15(2), 169–175 (1994)

    Article  MATH  Google Scholar 

  14. Bing, R.: Some aspects of the topology of 3-manifolds related to the Poincaré conjecture. In: Lectures on Modern Mathematics, vol. II, pp. 93–128 (1964)

    Google Scholar 

  15. Blum, H.: A Transformation for Extracting New Descriptors of Shape (1967)

  16. Couprie, M.: Note on fifteen 2D parallel thinning algorithms. Internal report, Université de Marne-la-Vallée, IGM2006-01 (2005)

  17. Couprie, M., Bertrand, G.: New characterizations of simple points in 2D, 3D and 4D discrete spaces. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 637–648 (2009)

    Article  Google Scholar 

  18. Duda, O., Hart, P., Munson, J.: Graphical data processing research study and experimental investigation. Tech. Rep. AD650926 (1967)

  19. Giblin, P.: Graphs, Surfaces and Homology. Chapman & Hall, London (1981)

    MATH  Google Scholar 

  20. Golay, J.: Hexagonal parallel pattern transformations. IEEE Trans. Comput. C-18, 733–740 (1969)

    Article  Google Scholar 

  21. Kong, T.: On the problem of determining whether a parallel reduction operator for n-dimensional binary images always preserves topology. In: Procs. SPIE Vision Geometry II, vol. 2060, pp. 69–77 (1993)

    Chapter  Google Scholar 

  22. Kong, T.Y.: On topology preservation in 2-D and 3-D thinning. Int. J. Pattern Recognit. Artif. Intell. 9, 813–844 (1995)

    Article  Google Scholar 

  23. Kong, T.Y.: Topology-preserving deletion of 1’s from 2-, 3- and 4-dimensional binary images. In: Discrete Geometry for Computer Imagery. LNCS, vol. 1347, pp. 3–18. Springer, Berlin (1997)

    Google Scholar 

  24. Kong, T.Y.: Minimal non-simple and minimal non-cosimple sets in binary images on cell complexes. In: Discrete Geometry for Computer Imagery. LNCS, vol. 4245, pp. 169–188. Springer, Berlin (2006)

    Chapter  Google Scholar 

  25. Kong, T.Y., Rosenfeld, A.: Digital topology: introduction and survey. Comput. Vis. Graph. Image Process. 48, 357–393 (1989)

    Article  Google Scholar 

  26. Kovalevsky, V.: Finite topology as applied to image analysis. Comput. Vis. Graph. Image Process. 46, 141–161 (1989)

    Article  Google Scholar 

  27. Lohou, C.: Contribution à l’analyse topologique des images: étude d’algorithmes de squelettisation pour images 2D et 3D, selon une approche topologie digitale ou topologie discrète. Ph.D. thesis, Université de Marne-la-Vallée, France (2001)

  28. Lohou, C.: Detection of the non-topology preservation of ma’s 3D surface-thinning algorithm, by the use of P-simple points. Pattern Recognit. Lett. 29, 822–827 (2008)

    Article  Google Scholar 

  29. Lohou, C., Bertrand, G.: Two symmetrical thinning algorithms for 3D binary images. Pattern Recognit. 40, 2301–2314 (2007)

    Article  MATH  Google Scholar 

  30. Ma, C.: On topology preservation in 3D thinning. Comput. Vis. Graph. Image Process. 59(3), 328–339 (1994)

    Google Scholar 

  31. Ma, C.M.: A 3D fully parallel thinning algorithm for generating medial faces. Pattern Recognit. Lett. 16(1), 83–87 (1995)

    Article  Google Scholar 

  32. Ma, C.M., Sonka, M.: A 3D fully parallel thinning algorithm and its applications. Comput. Vis. Image Underst. 64(3), 420–433 (1996)

    Article  Google Scholar 

  33. Ma, C.M., Wan, S.Y., Lee, J.D.: Three-dimensional topology preserving reduction on the 4-subfields. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1594–1605 (2002)

    Article  Google Scholar 

  34. Malgouyres, R., Francés, A.: Deciding whether a simplicial 3-complex collapses to a 1-complex is NP-complete. In: Discrete Geometry for Computer Imagery. LNCS, vol. 4992, pp. 177–188. Springer, Berlin (2008)

    Chapter  Google Scholar 

  35. Manzanera, A., Bernard, T., Prêteux, F., Longuet, B.: Medial faces from a concise 3D thinning algorithm. In: Proceedings IEEE International Conference on Computer Vision (ICCV’99), pp. 337–343 (1999)

    Chapter  Google Scholar 

  36. Manzanera, A., Bernard, T., Prêteux, F., Longuet, B.: Ultra-fast skeleton based on an isotropic fully parallel algorithm. In: Proceedings Discrete Geometry for Computer Imagery (DGCI’99). Lecture Notes in Computer Science, vol. 1568, pp. 313–324. Springer, Berlin (1999)

    Chapter  Google Scholar 

  37. Manzanera, A., Bernard, T., Prêteux, F., Longuet, B.: n-dimensional skeletonization: a unified mathematical framework. J. Electron. Imaging 11(1), 25–37 (2002)

    Article  Google Scholar 

  38. Németh, G., Kardos, P., Palágyi, K.: Topology preserving 2-subfield 3D thinning algorithms. In: Signal Processing, Pattern Recognition and Applications (SPPRA 2010), vol. 678, pp. 311–316 (2010)

    Google Scholar 

  39. Németh, G., Kardos, P., Palágyi, K.: Topology preserving 3D thinning algorithms using four and eight subfields. In: Campilho, A., Kamel, M. (eds.) Image Analysis and Recognition. Lecture Notes in Computer Science, vol. 6111, pp. 316–325. Springer, Berlin (2010)

    Chapter  Google Scholar 

  40. Palágyi, K.: A 3D fully parallel surface-thinning algorithm. Theor. Comput. Sci. 406(1–2), 119–135 (2008)

    Article  MATH  Google Scholar 

  41. Pfaltz, J., Rosenfeld, A.: Computer representation or planar regions by their skeletons. Commun. ACM 10, 119–125 (1967)

    Article  Google Scholar 

  42. Ronse, C.: Minimal test patterns for connectivity preservation in parallel thinning algorithms for binary digital images. Discrete Appl. Math. 21(1), 67–79 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  43. Rosenfeld, A.: Connectivity in digital pictures. J. ACM 17, 146–160 (1970)

    Article  MATH  MathSciNet  Google Scholar 

  44. Rosenfeld, A.: A characterization of parallel thinning algorithms. Inf. Control 29(3), 286–291 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  45. Rosenfeld, A., Pfaltz, J.: Sequential operations in digital picture processing. J. ACM 13, 471–494 (1966)

    Article  MATH  Google Scholar 

  46. Saha, P., Chaudhuri, B., Chanda, B., Dutta Majumder, D.: Topology preservation in 3D digital space. Pattern Recognit. 27, 295–300 (1994)

    Article  MathSciNet  Google Scholar 

  47. Whitehead, J.: Simplicial spaces, nuclei and m-groups. Proc. Lond. Math. Soc. 45(2), 243–327 (1939)

    Article  Google Scholar 

  48. Zeeman, E.: On the dunce hat. Topology 2, 341–358 (1964)

    Article  MathSciNet  Google Scholar 

Download references

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Correspondence to Gilles Bertrand.

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This work has been partially supported by the “ANR-2010-BLAN-0205 KIDICO” project.

Appendix

Appendix

Proof of Theorem 5

The following result is a consequence of Theorem 4.3(iii) of [8], note that this theorem holds for complexes of arbitrary dimensions.

Let \(S \in \mathbb{X}^{3}\) , let RS, and let T such that RTS. If R contains the critical kernel of S, then T collapses onto R .

Now let \(X \in \mathbb{X}^{3}\) and let YX such that Y contains the critical kernel of X.

Let XY={x 1,…,x k }. Thus the faces of XY are ordered according to their indices in an arbitrary way.

We set X 0=X, X i =X∖{x 1,…,x i }, i∈[1,k].

Let i∈[1,k]. The complex X i contains Y, thus \(X_{i}^{-}\) contains all the faces which are critical for X. By the above result \(X_{i-1}^{-}\) collapses onto \(X_{i}^{-} = [X_{i-1} \setminus \{ x_{i}\}]^{-}\), which means that x i is simple for X i−1 and that X i is an elementary thinning of X i−1 (Definition 1). Thus, the xel complex Y=X k is a thinning of X=X 0. □

Proof of Theorem 7

Let \(X \in \mathbb{X}^{3}\) and let YX.

  1. (i)

    Suppose Y contains the critical kernel of X. Let Z such that YZX. Since Z contains the critical kernel of X, by Theorem 5, Z is a thinning of X.

  2. (ii)

    Suppose Y does not contain the critical kernel of X. Then, there exists a face which is critical for X in X Y . There exists also a face x in X Y which is \(\mathcal{M}\)-critical for X. Then, the \(\mathcal{M}\)-crucial clique \(C = x^{+}_{X}\) is non-simple for X (see [12], Theorem 28, and Remark 19), i.e., the set Z=XC is not a thinning of X. We observe that YZ. Thus, there exists Z such that YZX, and such that Z is not a thinning of X.

 □

Proof of Proposition 8

We proved the proposition with the help of a computer program. All 226 possible configurations of the neighborhood of a point x in X were examined, and for each of them the equivalence between Definition 1 and conditions (1) and (2) was successfully tested. □

Proof of Proposition 10

We proved the proposition with the help of a computer program. All 216 possible configurations of the \(\mathcal{K}\)-neighborhood of a 2-clique C in X were examined, and for each of them the equivalence between Definition 3 and conditions (1) and (2) was successfully tested. □

Proof of Proposition 11

We proved the proposition with the help of a computer program. All 28 possible configurations of the \(\mathcal{K}\)-neighborhood of a 1-clique C in X were examined, and for each of them the equivalence between Definition 3 and the condition was successfully tested. □

Proof of Theorem 16 and Theorem 15

We proved the proposition with the help of a computer program. The condition “\(\mathcal{K}^{*}(C) \cap X\) is reducible” could not be checked directly because of combinatorial explosion, so we proved the property recursively with respect to the cardinality of \(S = \mathcal{K}^{*}(C) \cap X\). More precisely, knowing that the proposition is trivially true for |S|=0, we checked it for all possible configurations of n elements of S, for n=1,…,N (with N=26,16,8 for d=3,2,1 respectively, C being a d-clique), based on the fact that the proposition was already proved for n−1. For simplicity, the configurations of n elements out of N were generated by scanning all possible 2N configurations and selecting those with precisely n elements. □

Proof of Theorem 17

We proved the theorem with the help of a computer program. It is trivially true when |S|=0 (case of a 0-clique). We checked it for all possible configurations of N elements of S, (with N=26,16,8 for d=3,2,1 respectively, C being a d-clique). For each of these configurations, we tested for each simple voxel x of S the reducibility of S∖{x}, thanks to Theorem 16 and to characterizations of regular cliques (Proposition 13) and simple points (Proposition 8). □

Proof of Theorem 21

Let \(X \in \mathbb{V}^{3}\), let C be a d-clique which is critical for X, and let x=⋂{xC}.

Suppose C is not \(\mathcal{M}\)-crucial for X. Then there exists a d′-clique D which is critical for X, and such that x is a proper face of the face y=⋂{xD}. Thus, we have d<d′ and D is a proper subset of C.

  1. (i)

    Suppose D is \(\mathcal{D}\)-crucial for X. It means that C contains a voxel belonging to a d′-clique which is \(\mathcal{D}\)-crucial for X, with d′>d. Thus, C is not \(\mathcal{D}\)-crucial for X.

  2. (ii)

    Suppose D is not \(\mathcal{D}\)-crucial for X. It means that D (hence also C) contains a voxel belonging to a d″-clique which is \(\mathcal{D}\)-crucial for X, with d″>d′>d. Again, C cannot be \(\mathcal{D}\)-crucial for X.

Thus, a clique which is \(\mathcal{D}\)-crucial for X is necessarily \(\mathcal{M}\)-crucial for X. It follows that the \(\mathcal{D}\)-crucial kernel of X is a subset of its \(\mathcal{M}\)-crucial kernel. □

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Bertrand, G., Couprie, M. Powerful Parallel and Symmetric 3D Thinning Schemes Based on Critical Kernels. J Math Imaging Vis 48, 134–148 (2014). https://doi.org/10.1007/s10851-012-0402-7

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