Abstract
In this paper, we tackle the problem of finding microorganisms in bright field microscopy images, which is an important and challenging step in various tasks, like classifying soil textures. Apart from bacteria or fungi, these images can contain impurities such as sand particles, which increase the difficulty of microbe detection. Following a semantic segmentation approach, where a label is inferred for each pixel, we achieve encouraging classification results on a database containing five different types of microbes. We review and evaluate multiple techniques including segment classification, conditional random field models, and level set approaches.
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References
X. Wu and S. K. Shah, “A bottom-up and top-down model for cell segmentation using multispectral data,” in Proc. IEEE Int. Conf. on Biomedical Imaging (Rotterdam, 2010), pp. 592–595.
A. Gelas, K. Mosaliganti, A. Gouaillard, L. Souhait, R. Noche, N. Obholzer, and S. G. Megason, “Variational level-set with gaussian shape model for cell segmentation,” in Proc. IEEE Int. Conf. on Image Processing (Cairo, 2009), pp. 1089–1092.
A. Pinidiyaarachchi and C. Wöhlby, “Seeded water-sheds for combined segmentation and tracking of cells,” in Proc. Int. Conf. on Image Analysis and Processing (Genoa, 2005), pp. 336–343.
M. Krause, P. Rösch, B. Radt, and J. Popp, “Localizing and identifying living bacteria in an abiotic environment by a combination of raman and fluorescence microscopy,” Anal. Chem. 80, 8568–8575 (2008).
G. Csurka and F. Perronnin, “A simple high performance approach to semantic segmentation,” in Proc. British Machine Vision Conf. (Leeds, 2008), pp. 213–222.
B. Fröhlich, E. Rodner, and J. Denzler, “A fast approach for pixelwise labeling of facade images,” in Proc. Int. Conf. on Pattern Recognition (San Francisco, 2010), Vols. 7, 8, pp. 3029–3032.
D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002).
Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
M. Kass, A. Witkin, and D. Terzopoulus, “Snakes: active contour models,” Int. J. Comput. Vision 1(4), 321–331 (1987).
S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations,” J. Comput. Phys. 79, 12–49 (1988).
D. Mumford and J. Shah, “Optimal approximation by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math. 42, pp. 557–685 (1989).
T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Processing 10, 266–277 (2001).
D. Cremers, M. Rousson, and R. Deriche, “A review of statistical approaches to level set segmentation: Integrating color, texture, motion, and shape,” Int. J. Comput. Vision 72, 195–215 (2007).
V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vision 22, 61–79 (1997). http://dl.acm.org/citation.cfm?id=250488.250495
C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Processing 19, 3243–3254 (2010).
B. Fröhlich, E. Rodner, M. Kemmler, and J. Denzler, “Efficient Gaussian process classification using random decision forests,” Pattern Recogn. Image Anal. 21, 184–187 (2011). doi: 10.1134/S1054661811020337.
M. Schmidt, UGM: A matlab toolbox for probabilistic undirected graphical models (2007). http://www.di.ens.fr/mschmidt/Software/UGM.html
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Michael Kemmler, born January 31, 1983, received the Diploma degree in Computer Science with honors in 2009 from the Friedlich Schiller University of Jena, Germany. As a PhD student of the Jena Graduate School for Microbial Communication, he is currently pursuing his studies under supervision of Joachim Denzler at the Chair for Computer Vision, University of Jena. His research interests are in the area of machine learning, object recognition and bioinformatics, including kernel methods, visual image and scene classification as well as bacterial classification.
Björn Fröhlich, born September 10, 1984, earned the degree “Diplom-Informatiker” from the Friedrich Schiller University of Jena in the year 2009. He is currently a holder of a scholarship in the Graduate School on Image Processing and Image Interpretation from the Free State of Thuringia (Germany) and a PhD student at the chair of Computer Vision, Institute of Computer Science, Friedrich Schiller University in Jena. Research interests are focused on object recognition and image segmentation.
Erik Rodner, born May 22, 1983 earned the Diploma degree in Computer Science with honours in 2007 from the University of Jena, Germany. He pursued and received his Ph. D. in 2011 with summa cum laude for his work on learning with few examples, which was done under supervision of Joachim Denzler at the computer vision research group of the University of Jena. Erik is currently continuing his research as a postdoctoral researcher. His research interests include kernel methods, visual object discovery, rare animals, and scene understanding.
Joachim Denzler, born April 16, 1967, earned the degrees “DiplomInformatiker”, “Dr.-Ing.,” and “Habilitation” from the University of Erlangen in the years 1992, 1997, and 2003, respectively. Currently, he holds a position of full professor for computer science and is head of the Chair for Computer Vision, Faculty of Mathematics and Informatics, Friedrich-Schiller-University of Jena. His research interests comprise active computer vision, object recognition and tracking, 3D reconstruction, and plenoptic modeling, as well as computer vision for autonomous systems. He is author and coauthor of over 90 journal papers and technical articles. He is a member of the IEEE, IEEE computer society, DAGM, and GI. For his work on object tracking, plenoptic modeling, and active object recognition and state estimation, he was awarded the DAGM best aper awards in 1996, 1999, and 2001, respectively.
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Kemmler, M., Fröhlich, B., Rodner, E. et al. Segmentation of microorganism in complex environments. Pattern Recognit. Image Anal. 23, 512–517 (2013). https://doi.org/10.1134/S1054661813040056
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DOI: https://doi.org/10.1134/S1054661813040056