Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-14T05:17:32.871Z Has data issue: false hasContentIssue false
  • English
  • Français

Identifying Weak Linear Features with the “Coalescing Shortest Path Image Transform”

Published online by Cambridge University Press:  09 November 2011

Pascal Vallotton ,
Changming Sun ,
David Lovell ,
Martin Savelsbergh ,
Matthew Payne  and
Gerald Muench
Pascal Vallotton*
Affiliation:
Division of Mathematics, Informatics, and Statistics, CSIRO, Sydney, Australia
Changming Sun
Affiliation:
Division of Mathematics, Informatics, and Statistics, CSIRO, Sydney, Australia
David Lovell
Affiliation:
Division of Mathematics, Informatics, and Statistics, CSIRO, Sydney, Australia
Martin Savelsbergh
Affiliation:
Division of Mathematics, Informatics, and Statistics, CSIRO, Sydney, Australia
Matthew Payne
Affiliation:
Division of Mathematics, Informatics, and Statistics, CSIRO, Sydney, Australia School of Medicine, University of Western Sydney, Australia
Gerald Muench
Affiliation:
School of Medicine, University of Western Sydney, Australia
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the “Coalescing Shortest Path Image Transform” with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features.

Keywords

shortest pathsimage transformlinear featureneurite tracingmembrane tracingimage analysisimage processing
Type
Software and Techniques Development
Information
Microscopy and Microanalysis , Volume 17 , Issue 6 , December 2011 , pp. 911 - 914
Copyright
Copyright © Microscopy Society of America 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Dijkstra, E. (1959). A note on two problems in connexion with graphs. Numer Math 1, 269–271.CrossRefGoogle Scholar
Falcao, A.X., Stolfi, J. & Lotufo, R.D.A. (2004). The image foresting transform: Theory, algorithms, and applications. IEEE T Pattern Anal 26(1), 19–29.CrossRefGoogle ScholarPubMed
Falcao, A.X., Udupa, J.K. & Miyazawa, F.K. (2000). An ultra-fast user-steered image segmentation paradigm: Live wire on the fly. IEEE T Med Imaging 19(1), 55–62.CrossRefGoogle ScholarPubMed
Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag.CrossRefGoogle Scholar
Lindeberg, T. (1998). Edge detection and ridge detection with automatic scale selection. Int J Comput Vision 30(2), 117–154.CrossRefGoogle Scholar
Mehlhorn, K. & Näher, S. (1999). LEDA: A Platform for Combinatorial and Geometric Computing. UK: Cambridge University Press.Google Scholar
Meijering, E., Jacob, M., Sarria, J.C.F., Steiner, P., Hirling, H. & Unser, M. (2004). Neurite tracing in fluorescence microscopy images using ridge filtering and graph searching: Principles and validation. In Proceedings 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, vols 1 and 2, pp. 1219–1222. New York: IEEE.Google Scholar
Poon, M., Hamarneh, G. & Abugharbieh, R. (2008). Efficient interactive 3D Livewire segmentation of complex objects with arbitrary topology. Comput Med Imag Graph 32(8), 639–650.CrossRefGoogle ScholarPubMed
Sun, C. & Vallotton, P. (2009). Fast linear feature detection using multiple directional non-maximum suppression. J Microsc-Oxford 234(2), 147–157.CrossRefGoogle ScholarPubMed
Sun, C.M., Vallotton, P., Wang, D.D., Lopez, J., Ng, Y. & James, D. (2009). Membrane boundary extraction using circular multiple paths. Pattern Recogn 42(4), 523–530.CrossRefGoogle Scholar
Vallotton, P., Sun, C.M., Lovell, D., Fazio, V.J. & Newman, J. (2010). DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials. J Appl Crystallogr 43, 1548–1552.CrossRefGoogle Scholar
Yan, J.Y., Zhao, B.S., Wang, L., Zelenetz, A. & Schwartz, L.H. (2006). Marker-controlled watershed for lymphoma segmentation in sequential CT images. Med Phys 33(7), 2452–2460.CrossRefGoogle ScholarPubMed