Abstract
Cancer cell morphology can be used as an indicator of metastasizing behaviors. To analyze cancer cell morphology, we used 3D phase-contrast microscopy. This is one of the most common imaging modalities for the observation of long-term multi-cellular processes of living cells without phototoxicity and photobleaching, which is common in other fluorescent labeling techniques. However, it also has certain drawbacks at the image level, such as non-uniform illumination and phase-contrast interference rings. Our first step compensates for row-contrast artifacts via single cell detection using intensity-based global segmentation. We extracted cross-sections using principle component analysis; this was due to the interference’s non-symmetric diffusion pattern, which appeared around each individual cell. Then, we analyzed cell morphology by an intensity gradient, considering local peaks as bright ring regions. Finally, we applied a self-organizing map method that has potential viability for cancer cell classification into active and inactive categories.
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Ivan, A., Olivier, D., Veronique, M., Robert, K., Nadine, W., Christine, D.: Automated tracking of unmarked cells migrating in three-dimensional matrices applied to anti-cancer drug screening. Expl. Cell Res. 316, 181–193 (2010)
Friedl, P., Zanker, K.S., Brocker, E.B.: Cell migration strategies in 3D extracellular matrix: differences in morphology, cell matrix interactions, and integrin function. Microsc. Res. Tech. 43, 369–378 (1998)
Li, K., Chen, M., Kanade, T., Miller, E.D., Weiss, L.E., Campbell, P.G.: Cell population tracking and lineage construction with spatiotemporal context. Med. Image Anal. 12(5), 546–566 (2008)
Hang, S., Zhaozheng, Y., Seungil, H., Takeo, K.: Cell segmentation in phase-contrast microscopy images via semi-supervised classification over optics-related features. Med. Image Anal. 17, 766–778 (2013)
Decaestecker, C., Debeir, O., Van Ham, P., Kiss, R.: Can anti-migratory drugs be screened in vitro? A review of 2D and 3D assays for the quantitative analysis of cell migration. Med. Res. Rev. 27, 149–176 (2007)
Pan, J., Kanade, T., Chen, M.: Learning to detect different types of cells under phase-contrast microscopy. In: Proc. MIAAB (2009)
Magg, M.: Tracking cells in phase-contrast light microscopic images, Diploma Theses, ETH Zurich (2010)
Orikawa, J., Tanaka, T.: Cell segmentation from phase-contrast images using hybrid watershed and region growing algorithm for genomic drug discovery. In: Proc. SICE (2011)
Kang, M.S., Song, S.M., Lee, H.A., Kim, M.H.: Cell morphology classification in phase-contrast microscopy image reducing halo artifact. In: Proc. SPIE (2012)
Kang, M.S., Lee, J.E., Kim, H.R., Kim, M.H.: Classification of tumor cells in phase-contrast microscopy image using fourier descriptor. J. of Biomedical Engineering Research 33, 169–176 (2012)
Lee, H., Kim, J.: Retrospective correction of nonuniform illumination on bi-level images. Opt. Express 17, 23880–23893 (2009)
Arici, T., Dikbas, S., Altunbasck, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 15, 1921–1935 (2009)
Prewitt, J.M.S., Mendelsohn, M.L.: The analysis of cell images. Ann. New York Academy Science 128, 1035–1053 (1966)
Kang, M.S., Lee, J.E., Jeon, W.K., Choi, H.K., Kim, M.H.: Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU. In: Proc. SPIE (2013)
Arthur, A.: Some techniques for shading machine renderings of solids. In: Proc. AFIPS, pp. 37–45 (1968)
Antonille, S., Gualtieri, J.A.: Visualizing Clusters in High-Dimensional Data with a Kohonen Self Organizing Map, Technical Report (2000)
Smith, L.: A Tutorial on Principal Components Analysis (2002)
Watanabe, Y.: A method for volume estimation by using vector areas and centroids of serial cross sections. IEEE Trans. Biomed. Eng. 29(3), 202–205 (1982)
Shlens, J.: A tutorial on principal component analysis (2005)
Martínez, P., Gualtieri, J.A., Aguilar, P.L., Pérez, R., Linaje, M., Preciado, J.C., Plaza, A.: Hyperspectral image classification using a self-organizing map. In: Summaries of the X JPL Airborne Earth Science Workshop, JPL/NASA (2001)
Kohonen, T.: Self-organizing maps. Proceedings of the IEEE 78, 1464–1480 (1990)
Dian, P.: The use of self-organizing map method and feature selection in image database classification system. International Journal of Computer Science 9, 1694–1814 (2012)
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Kang, MS., Kim, HR., Kim, MH. (2014). Cell Classification in 3D Phase-Contrast Microscopy Images via Self-Organizing Maps. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_63
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DOI: https://doi.org/10.1007/978-3-319-14364-4_63
Publisher Name: Springer, Cham
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