Abstract
This paper proposes a new image restoration method for phase contrast microscopy as a mean to enhance the quality of images prior to image analysis. Compared to state-of-the-art image restoration algorithms, our method has a more solid theoretical foundation and is orders of magnitude more efficient in computation. We validated the proposed method by applying it to automated muscle myotube detection, a challenging problem that has not been tackled without staining images. Results on 300 phase contrast microscopy images from three different culture conditions demonstrate that the proposed restoration scheme improves myotube detection, and that our approach is far more computationally efficient than previous methods.
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
Yin, Z., Kanade, T., Chen, M.: Understanding the phase contrast optics to restore artifact-free microscopy images for segmentation. Med. Image Anal. 16(5), 1047–1062 (2012)
Huh, S., Ker, D.F.E., Su, H., Kanade, T.: Apoptosis Detection for Adherent Cell Populations in Time-Lapse Phase-Contrast Microscopy Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 331–339. Springer, Heidelberg (2012)
Su, H., Yin, Z., Kanade, T., Huh, S.: Phase contrast image restoration via dictionary representation of diffraction patterns. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 615–622. Springer, Heidelberg (2012)
Gonzalez, R.C., Woods, R.E.: Digital Image Prcessing. Addison-Wesley Publishing Company, Inc. (1992)
Tedesco, F.S., Dellavalle, A., Diaz-Manera, J., Messina, G., Cossu, G.: Repairing skeletal muscle: regenerative potential of skeletal muscle stem cells. J. Clin. Invest. 120(1), 11–19 (2010)
Born, M., Wolf, E.: Principles of Optics, 6th edn. Pergamon Press (1980)
Ojala, T., Pietikäinen, M., Mäenpää, T.T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Trans. Pattern. Anal. Mach. Intell. 24(7), 971–987 (2002)
Liu, M.-Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy Rate Superpixel Segmentation. In: Proc. CVPR, pp. 2097–2104 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huh, S., Su, H., Chen, M., Kanade, T. (2013). Efficient Phase Contrast Microscopy Restoration Applied for Muscle Myotube Detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-40811-3_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40810-6
Online ISBN: 978-3-642-40811-3
eBook Packages: Computer ScienceComputer Science (R0)