Abstract
Watershed segmentation of spectral images is typically achieved by first transforming the high-dimensional input data into a scalar boundary indicator map which is used to derive the watersheds. We propose to combine a Random Forest classifier with the watershed transform and introduce three novel methods to obtain scalar boundary indicator maps from class probability maps. We further introduce the multivariate watershed as a generalization of the classic watershed approach.
We thank Erika R. Amstalden and Kristine Glunde for acquiring and providing data.
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
Digabel, H., Lantuéjoul, C.: Iterative algorithms. In: Proc. 2nd Europ. Symp. Quant. Anal. of Microstr. in Material Science, Biology and Medicine, pp. 85–99 (1978)
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: Definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)
Lezoray, O.: Supervised automatic histogram clustering and watershed segmentation. Image Anal. Stereol. 22, 113–120 (2003)
Chang, C.Y., Shie, W.S., Wang, J.H.: Color image segmentation via fuzzy feature tuning and feature adjustment. In: Conf. on Syst., Man & Cybernetics, vol. 4, p. 6 (2002)
Huguet, A.B., de Andrade, M.C., Carceroni, R.L., Araujo, A.A.: Color-based watershed segmentation of low-altitude aerial images. In: 17. Brazilian Symp. on Comp. Graphics and Image Proc., pp. 138–145 (2004)
Noyel, G., Angulo, J., Jeulin, D., Balvay, D., Cuenod, C.-A.: Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging. In: Intern. Symp. on Biomedical Imaging: From Nano to Macro, pp. 1517–1520 (2008)
McDonnell, L.A., Heeren, R.M.A.: Imaging mass spectrometry. Mass Spectrometry Reviews 26, 606–643 (2006)
Moffat, J., et al.: A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124(6), 1283–1298 (2006)
Scheunders, P.: Multivalued image segmentation based on first fundamental form. In: Proc. of the Intern. Conf. on Image Anal. and Processing, pp. 185–190 (2001)
Li, P., Xiao, X.: Evaluation of multiscale morphological segmentation of multispectral imagery for land cover classification. In: Proc. of the IEEE Geoscience and Remote Sensing Symposium, vol. 4, pp. 2676–2679 (2004)
Noyel, G., Angulo, J., Jeulin, D.: Morphological segmentation of hyperspectral images. Image Anal. Stereol. 26, 101–109 (2007)
Malpica, N., Ortuno, J.E., Santos, A.: A multichannel watershed-based algorithm for supervised texture segmentation. Patt. Rec. Letters 24, 1545–1554 (2003)
Karvelis, P.S., et al.: Region based segmentation and classification of multispectral chromosome images. IEEE Trans. on Medical Imaging 27, 697–708 (2008)
Zhang, Y., Feng, X., Le., X.: Segmentation on multispectral remote sensing image using watershed transformation. Congr. on Image & Sign. Proc. 4, 773–777 (2008)
Angulo, J., Jeulin, D.: Stochastic watershed segmentation. In: Intern. Symp. on Mathematical Morphology, vol. 8, pp. 265–276 (2007)
Noyel, G., Angulo, J., Jeulin, D.: Random germs and stochastic watershed for unsupervised multispectral image segmentation. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 17–24. Springer, Heidelberg (2007)
Soille, P.: Morphological Image Analysis. Springer, Heidelberg (1999)
Aitchison, J.: The Statistical Analysis of Compositional Data. Monographs on Statistics and Applied Probability. Chapman and Hall, Boca Raton (1986)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. on PAMI 13, 583–598 (1991)
Meyer, F.: Topographic distance and watersheds lines. Sig. Proc. 38, 113–125 (1994)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Ulintz, P.J., Zhu, J., Qin, Z.S., Andrews, P.C.: Improved classification of mass spectrometry database search results using newer machine learning approaches. Molecular and Cellular Proteomics 5, 497–509 (2006)
Hanselmann, M., et al.: Concise representation of mass spectrometry images by probabilistic latent semantic analysis. Anal. Chem. 80(24), 9649–9658 (2008)
Hanselmann, M., et al.: Toward Digital Staining using Imaging Mass Spectrometry and Random Forests. J. of Prot. Res. (2009) DOI: 10.1021/pr900253y
Casella, G., Berger, R.L.: Statistical Inference. Duxbury Advanced Series (2002)
Godtliebsen, F., Marron, J.S., Chaudhuri, P.: Significance in scale space for bivariate density estimation. Journal of Comp. and Graph. Stat. 11, 1–21 (2002)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. Trans. on Patt. Anal. and Mach. Intelligence 24(5), 603–619 (2002)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. Proc. of the Conf. on Computer Vision, 836–846 (1998)
Grimaud, M.: New measure of contrast: the dynamics. Image Algebra and Morphological Image Processing III, 292–305 (1992)
Brun, L., Mokhtari, M., Meyer, F.: Hierarchical watersheds within the combinatorial pyramid framework. In: Andrès, É., Damiand, G., Lienhardt, P. (eds.) DGCI 2005. LNCS, vol. 3429, pp. 34–44. Springer, Heidelberg (2005)
Baddeley, A.J.: An error metric for binary images. Robust Comp. Vis., 59–78 (1992)
Rosenfeld, A., Pfaltz, J.: Sequential operations in digital picture processing. Robust Computer Vision 13(4), 471–494 (1966)
Gale, D., Shapley, L.S.: College admissions and the stability of marriage. American Mathematical Monthly 69, 9–14 (1962)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hanselmann, M., Köthe, U., Renard, B.Y., Kirchner, M., Heeren, R.M.A., Hamprecht, F.A. (2009). Multivariate Watershed Segmentation of Compositional Data. In: Brlek, S., Reutenauer, C., Provençal, X. (eds) Discrete Geometry for Computer Imagery. DGCI 2009. Lecture Notes in Computer Science, vol 5810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04397-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-04397-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04396-3
Online ISBN: 978-3-642-04397-0
eBook Packages: Computer ScienceComputer Science (R0)