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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access May 5, 2016

Efficient Computation of Greyscale Path Openings

  • Herman Schubert , Jasper J. van de Gronde and Jos B. T. M. Roerdink

Abstract

Path openings are morphological operators that are used to preserve long, thin, and curved structures in images. They have the ability to adapt to local image structures,which allows them to detect lines that are not perfectly straight. They are applicable in extracting cracks, roads, and similar structures. Although path openings are very efficient to implement for binary images, the greyscale case is more problematic. This study provides an analysis of the main existing greyscale algorithm, and shows that although its time complexity can be quadratic in the number of pixels, this is optimal in terms of the output (if the full opening transform is created). Also, it is shown that under many circumstances the worst-case running time is much less than quadratic. Finally, a new algorithm is provided,which has the same time complexity, but is simpler, faster in practice and more amenable to parallelization

References

[1] Appleton B., Talbot H. Efficient Path Openings and Closings. In C. Ronse, L. Najman, E. Decencière, editors, Mathematical Morphology: 40 Years On, volume 30 of Computational Imaging and Vision, pages 33–42. Springer Netherlands, 2005. 10.1007/1-4020-3443-1_4 10.1007/1-4020-3443-1_4Search in Google Scholar

[2] Asplund T. Improved Path Opening by Preselection of Paths. Master’s thesis, Uppsala Universitet, 2015 Search in Google Scholar

[3] Bismuth V., Vaillant R., Talbot H., Najman L. Curvilinear Structure Enhancement with the Polygonal Path Image - Application to Guide-Wire Segmentation in X-Ray Fluoroscopy. In N. Ayache, H. Delingette, P. Golland, K. Mori, editors, Med. Image Comput. Comput. Assist. Interv., volume 7511 of LNCS, pages 9–16. Springer Berlin Heidelberg, 2012. 10.1007/978-3-642- 33418-4_2 10.1007/978-3-642-33418-4_2Search in Google Scholar PubMed

[4] Cokelaer F., Talbot H., Chanussot J. Efficient Robust d-Dimensional Path Operators. IEEE J. Sel. Top. Signal. Process., 2012. 6(7), 830–839. 10.1109/jstsp.2012.2213578 10.1109/JSTSP.2012.2213578Search in Google Scholar

[5] van de Gronde J.J., Lysenko M., Roerdink J.B.T.M. Path-Based Mathematical Morphology on Tensor Fields. In I. Hotz, T. Schultz, editors, Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, Math. Vis., pages 109– 127. Springer International Publishing, 2015. 10.1007/978-3-319-15090-1_6 10.1007/978-3-319-15090-1_6Search in Google Scholar

[6] van de Gronde J.J.,Offringa A.R., Roerdink J.B.T.M. Efficient and robust path openings using the scale-invariant rank operator. Journal of Mathematical Imaging and Vision, 2016. Accepted 10.1007/s10851-016-0649-5Search in Google Scholar

[7] van de Gronde J.J., Schubert H.R., Roerdink J.B.T.M. Fast Computation of Greyscale Path Openings. In J.A. Benediktsson, J. Chanussot, L. Najman, H. Talbot, editors, Mathematical Morphology and Its Applications to Signal and Image Processing, volume 9082 of LNCS, pages 621–632. Springer International Publishing, 2015. 10.1007/978-3-319-18720-4_52 10.1007/978-3-319-18720-4_52Search in Google Scholar

[8] Heijmans H., Buckley M., Talbot H. Path Openings and Closings. J.Math. Imaging Vis., 2005. 22(2), 107–119. 10.1007/s10851- 005-4885-3 10.1007/s10851-005-4885-3Search in Google Scholar

[9] Kahn A.B. Topological Sorting of Large Networks. Commun. ACM, 1962. 5(11), 558–562. 10.1145/368996.369025 10.1145/368996.369025Search in Google Scholar

[10] Karas P., Morard V., Bartovsk`y J., Grandpierre T., Dokládalová E., Matula P., Dokládal P. Gpu implementation of linear morphological openings with arbitrary angle. Journal of Real-Time Image Processing, 2015. 10(1), 27–41 10.1007/s11554-012-0248-7Search in Google Scholar

[11] Luengo Hendriks C.L. Constrained and dimensionality-independent path openings. IEEE Trans. Image Process., 2010. 19(6), 1587–1595. 10.1109/tip.2010.2044959 10.1109/TIP.2010.2044959Search in Google Scholar PubMed

[12] Merveille O., Talbot H., Najman L., Passat N. Ranking Orientation Responses of Path Operators: Motivations, Choices and Algorithmics. In J.A. Benediktsson, J. Chanussot, L. Najman, H. Talbot, editors, Mathematical Morphology and Its Applications to Signal and Image Processing, volume 9082 of LNCS, pages 633–644. Springer International Publishing, 2015. 10.1007/978-3-319-18720-4_53 10.1007/978-3-319-18720-4_53Search in Google Scholar

[13] Morard V., Decencière E., Dokládal P. Geodesic Attributes Thinnings and Thickenings. In P. Soille, M. Pesaresi, G.K. Ouzounis, editors, Mathematical Morphology and Its Applications to Image and Signal Processing, volume 6671 of LNCS, pages 200– 211. Springer Berlin Heidelberg, 2011. 10.1007/978-3-642-21569-8_18 10.1007/978-3-642-21569-8_18Search in Google Scholar

[14] Morard V., Dokládal P., Decencière E. Linear openings in arbitrary orientation in O(1) per pixel. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011 pages 1457–1460. 10.1109/icassp.2011.5946767 10.1109/ICASSP.2011.5946767Search in Google Scholar

[15] Morard V., Dokládal P., Decencière E. One-Dimensional Openings, Granulometries and Component Trees in O(1) Per Pixel. IEEE J. Sel. Top. Signal. Process., 2012. 6(7), 840–848. 10.1109/jstsp.2012.2201694 10.1109/JSTSP.2012.2201694Search in Google Scholar

[16] Morard V., Dokládal P., Decencière E. Parsimonious Path Openings and Closings. IEEE Trans. Image Process., 2014. 23(4), 1543–1555. 10.1109/tip.2014.2303647 10.1109/TIP.2014.2303647Search in Google Scholar PubMed

[17] Talbot H., Appleton B. Efficient complete and incomplete path openings and closings. Image Vis. Comput., 2007. 25(4), 416–425. 10.1016/j.imavis.2006.07.021 10.1016/j.imavis.2006.07.021Search in Google Scholar

[18] Valero S., Chanussot J., Benediktsson J.A., Talbot H., Waske B. Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images. Pattern Recognit. Lett., 2010. 31(10), 1120– 1127. 10.1016/j.patrec.2009.12.018 10.1016/j.patrec.2009.12.018Search in Google Scholar

[19] Yuan J., Gleason S.S., Cheriyadat A.M. Systematic benchmarking of aerial image segmentation. Geoscience and Remote Sensing Letters, IEEE, 2013. 10(6), 1527–1531 10.1109/LGRS.2013.2261453Search in Google Scholar

Received: 2015-6-30
Accepted: 2016-2-9
Published Online: 2016-5-5

© 2016 Herman Schubert et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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