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A combined topological and statistical approach for interactive segmentation of 3D images

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Abstract

This paper presents a new framework for an interactive segmentation of 3D images. The framework is based on a bimodal data structure defined by a region adjacency graph (RAG) that is associated with a hierarchical classification tree (HCT). The RAG provides information about the spatial and topological organisation of the extracted regions of the image. The HCT provides information about the similarities between the extracted regions of the image based on a predefined set of features. The first contribution of our work is the combination of a RAG and a HCT. An incremental system was obtained by defining operators that work with and on the RAG and the HCT. If a static predefined processing chain has been defined, these operators can be used in batch mode. If a scheduler is available, they can be used in an adaptive manner. Finally, if a user chooses the operator to be used after each step, the operators can be used interactively. The second contribution of this paper is the formal description of these operators. To give the user the ability to incrementally improve the segmentation, powerful visualisation of the segmentation state and interfaces have been proposed, an important advantage of the proposed framework. To validate the proposed framework, a user study has been conducted in a concrete case of texture segmentation. Our system obtains very satisfactory results even for complex volumetric textures, and helps real users by providing high quality segmentations. The system has been tested by specialists in sonography to segment 3D ultrasound images of the skin. Some examples of segmentation are presented to illustrate the benefit of the interactivity provided by our approach.

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References

  1. Elisa Schaeffer, S.: Graph clustering. Comput. Sci. Rev. 1, 27–64 (2007)

    Google Scholar 

  2. Campadelli, P., Casiraghi, E., Exposito, A.: Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif. Intell. Med. 45, 185–196 (2009)

    Article  Google Scholar 

  3. Oliver, A., Freixenet, F., Martí, J., Pérez, E., Pont, J., Denton, E.R.E., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14, 87–110 (2010)

    Article  Google Scholar 

  4. Ilea, D.E., Whelan, P.F.: Image segmentation based on the integration of colour-texture descriptors: a review. Pattern Recognit. 44, 2479–2501 (2011)

    Article  MATH  Google Scholar 

  5. Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5, 127–142 (2001)

    Article  Google Scholar 

  6. McGuinness, K., O’Connor, N.E.: A comparative evaluation of interactive segmentation algorithms. Pattern Recognit. 43, 434–444 (2010)

    Google Scholar 

  7. Boykov, Y., Jolly, M.-P.: Interactive organ segmentation using graph cuts. In: MICCAI’00: International Conference on Medical Image Computing and Computer Assisted Intervention (2000)

  8. Bartz, D., Mayer, D., Fischer, J., Ley, S., del Rio, A., Thust, S., Heussel, C.P., Kauczor, H.-U., Strasser, W.: Hybrid segmentation and exploration of the human lungs. In: VIS’03: IEEE International Conference in Visualization, pp. 177–184 (2003)

  9. Gu, L., Peters, T.: Robust 3d organ segmentation using a fast hybrid algorithm. Comput. Assist. Radiol. Surg. 1268, 69–74 (2004)

    Google Scholar 

  10. Tzeng, F.-Y., Lum, E.B., Ma, K.-L.: An intelligent system approach to higher-dimensional classification of volume data. IEEE Trans. Visual. Comput. Graph. 11, 273–284 (2005)

    Google Scholar 

  11. Huang, R., Ma, K.-L.: A three-level graph based interactive volume segmentation system. In: ISVC ’05: Proceedings on the First International Symposium on Visual, Computing (2005)

  12. Ben-Zadok, N., Riklin-Raviv, T., Kiryati, N.: Interactive level set segmentation for image-guided therapy. In: ISBI ’09: IEEE International Symposium on Biomedical, Imaging, pp. 1079–1082 (2009)

  13. Prabni, J.-S., Ropinski, T., Hinrichs, K.: Uncertainty-aware guided volume segmentation. IEEE Trans. Visual. Comput. Graph. 16, 1358–1365 (2010)

    Article  Google Scholar 

  14. Paulhac, L., Ramel, J.-Y., Renard, T.: Interactive segmentation of 3d images using a region adjacency graph representation. In: ICIAR ’11: Proceedings of the 8th International Conference in Image Analysis and Recognition (2011)

  15. Noma, A., Graciano, A.B.V., Cesar Jr, R.M., Consularo, L.A., Bloch, I.: Interactive image segmentation by matching attributed relational graphs. Pattern Recognit. 45, 1159–1179 (2012)

    Article  Google Scholar 

  16. Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV’01: Proceedings of the International Conference on Computer Vision (2001)

  17. Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)

    Article  Google Scholar 

  18. Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1768–1783 (2006)

    Article  Google Scholar 

  19. Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. Image Process. 9, 561–576 (2000)

    Article  Google Scholar 

  20. Damiand, G.: Topological model for 3d image representation: definition and incremental extraction algorithm. Comput. Vis. Image Underst. 109, 260–289 (2008)

    Google Scholar 

  21. Baldacci, F., Braquelaire, A.J.-P., Domenger, J.-P.: Oriented boundary graph: a framework to design and implement 3d segmentation algorithms. In: ICPR’10: 20th International Conference on, Pattern Recognition, pp. 1116–1119, August 2010

  22. Chassery, J.-M., Montanvert, A.: Géométrie discréte en analyse d’images (1991)

  23. Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Trans. Syst. Man Cybernet. 8(6), 460–473 (1978)

    Article  Google Scholar 

  24. Amadasun, M., King, R.: Texture features corresponding to textural properties. IEEE Trans. Syst. Man Cybernet. 19(5), 1264–1274 (1989)

    Article  Google Scholar 

  25. Paulhac, L.: Solid database, 2009. http://www.rfai.li.univ-tours.fr/fr/ressources/3Dsynthetic_images_database.html

  26. Paulhac, L., Makris, P., Ramel, J.-Y.: A solid texture database for segmentation and classification experiments. In: VISSAPP ’09: Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (2009)

  27. Paulhac, L., Makris, P., Gregoire, J.-M., Ramel, J.-Y.: Human understandable features for segmentation of solid texture. In: ISVC ’09: Proceedings of the 5th International Symposium on Vision, Computing, pp. 379–390 (2009)

  28. Coleman, G.B., Andrews, H.C.: Image segmentation by clustering. In. Proceedings of the IEEE, pp. 773–785 (1979)

  29. Cardoso, J.S., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Trans. Image Process. 14(11), 1773–1782 (2005)

    Article  Google Scholar 

  30. Gusfield, D.: Partition-distance: a problem and class of perfect graphs arising in clustering. Inf. Process. Lett. 82(9), 159–164 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  31. Alison Noble, J., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)

    Google Scholar 

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Authors and Affiliations

  1. Laboratoire Informatique de, l’Université François Rabelais de Tours, 64 avenue Jean Portalis, 37200 , Tours, France

    Ludovic Paulhac, Jean-Yves Ramel & Pascal Makris

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  1. Ludovic Paulhac
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  2. Jean-Yves Ramel
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  3. Pascal Makris
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Correspondence to Ludovic Paulhac.

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Paulhac, L., Ramel, JY. & Makris, P. A combined topological and statistical approach for interactive segmentation of 3D images. Machine Vision and Applications 24, 1239–1253 (2013). https://doi.org/10.1007/s00138-012-0477-6

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  • DOI: https://doi.org/10.1007/s00138-012-0477-6

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