Zusammenfassung
Some cell detection approaches which deal with bright-field microscope images utilize defocussing to increase the image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a modified monogenic signal framework. We show empirically that using the local phase of the previous monogenic signal in place of the defocused image improves the cell-background classification rate. The evaluation was performed on L929 adherent cell line with more than 1000 manually labeled cells. The improvement was 6.8 % using a random forest classifier and 10 % using a support vector machine classifier with a radial basis function kernel.
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Literatur
Jesper Sjöström P, Frydel BR,Wahlberg LU. Artificial neural network-aided image analysis system for cell counting. Cytometry. 1999;36(1):18–26.
Nattkemper TW, Ritter H, Schubert W. Extracting patterns of lymphocyte fluorescence from digital microscope images. Intell Data Anal Med Pharmacol. 1999;99:79–88.
Long X, Cleveland WL, Yao YL. A new preprocessing approach for cell recognition. IEEE Trans Inf Technol Biomed. 2005;9(3):407–12.
Long X, Cleveland WL, Yao YL. Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure. Comput Biol Med. 2006;36(4):339–62.
Agero U, Monken CH, Ropert C, et al. Cell surface fluctuations studied with defocussing microscopy. Phys Rev E. 2003;67(5):051904.
Ali R, Gooding M, Szilágyi T, et al. Automatic segmentation of adherent biological cell boundaries and nuclei from bright-field microscopy images. Mach Vis Appl. 2012;23(4):607–21.
Ali R, Szilagyi T, Gooding M, et al. On the use of low-pass filters for image processing with inverse Laplacian models. J Math Imaging Vis. 2010; p. 1–10.
Teague MR. Deterministic phase retrieval: a Green’s function solution. J Opt Soc Am. 1983;73(11):1434–41.
Felsberg M, Sommer G. The monogenic signal. IEEE Trans Signal Process. 2001;49(12):3136–44.
Khoshgoftaar TM, Golawala M, Van Hulse J. An empirical study of learning from imbalanced data using random forest. Proc IEEE Int Conf Tool Artif Intell. 2007;p. 310–7.
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Mualla, F., Schöll, S., Sommerfeldt, B., Hornegger, J. (2013). Using the Monogenic Signal for Cell-Background Classification in Bright-Field Microscope Images. In: Meinzer, HP., Deserno, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2013. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36480-8_31
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DOI: https://doi.org/10.1007/978-3-642-36480-8_31
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