Abstract
We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost function. We use manually annotated retinal scans to evaluate our method and demonstrate that it brings about a very substantial improvement.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Gonzalez, G., Turetken, E., Fleuret, F., Fua, P.: Delineating Trees in Noisy 2D Images and 3D Image-Stacks. In: CVPR, San Francisco, CA (June 2010)
Chimani, M., Kandyba, M., Ljubić, I., Mutzel, P.: Obtaining optimal k-cardinality trees fast. J. Exp. Algorithmics 14, 2.5–2.23 (2009)
Blum, C., Blesa, M.J.: New metaheuristic approaches for the edge-weighted k-cardinality tree problem. Computers & Operations Research 32(6), 1355–1377 (2005)
Leandro, J., Soares, J., Cesar, R., Jelinek, H.: Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers. In: Brazilian Symposium on Computer Graphics and Image Processing, p. 262 (2003)
Yedidya, T., Hartley, R.: Tracking of blood vessels in retinal images using kalman filter. In: DICTA, Washington, DC, USA, pp. 52–58. IEEE Computer Society, Los Alamitos (2008)
Can, A., Shen, H., Turner, J., Tanenbaum, H., Roysam, B.: Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. TITB 3(2), 125–138 (1999)
Fan, D.: Bayesian inference of vascular structure from retinal images. PhD thesis, Dept. of Computer Science, U. of Warwick, Coventry, UK (2006)
Sun, K., Sang, N., Zhang, T.: Marked point process for vascular tree extraction on angiogram. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 467–478. Springer, Heidelberg (2007)
Gonzalez, G., Fleuret, F., Fua, P.: Automated Delineation of Dendritic Networks in Noisy Image Stacks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 214–227. Springer, Heidelberg (2008)
Gonzalez, G., Fleuret, F., Fua, P.: Learning rotational features for filament detection. In: CVPR, Miami, FL, pp. 1582–1589 (June 2009)
Aibinu, A., Iqbal, M., Shafie, A., Salami, M., Nilsson, M.: Vascular intersection detection in retina fundus images using a new hybrid approach. Computers in Biology and Medicine 40(1), 81–89 (2010)
Huang, K., Yan, M.: Robust Optic Disk Detection in Retinal Images using Vessel Structure and Radon Transform. In: SPIE, vol. 6144 (2006)
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)
HHMI: Diadem challenge (2010), http://diademchallenge.org/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Türetken, E., Blum, C., González, G., Fua, P. (2010). Reconstructing Geometrically Consistent Tree Structures from Noisy Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-15705-9_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
Online ISBN: 978-3-642-15705-9
eBook Packages: Computer ScienceComputer Science (R0)