On-line Image Mosaicing of Live Stem Cells
Image mosaicing is a technique that permits to enlarge the field of view of a camera. For instance, it is employed to achieve panoramas with common cameras or even in scientific applications, to achieve the image of a whole culture in microscopical imaging. Usually, a mosaic of cell cultures is achieved through using automated microscopes. However, this is often performed in batch, through CPU intensive minimization algorithms. In addition, live stem cells are studied in phase contrast, showing a low contrast that cannot be improved further. We present a method to study the flat field from live stem cells images even in case of 100% confluence, this permitting to build accurate mosaics on-line using high performance algorithms.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332676Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1227
 Emilie Flaberg, Per Sabelstrnaöm, Christer Strandh, and LaszloSzekely, "Extended field laser confocal microscopy (eflcm): Combining automated gigapixel image capture with in silico virtual microscopy," BMC Med Imaging, vol. 8, pp. 1-13, 2008.
 Richard L. Mort, Thaya Ramaesh1, Dirk A. Kleinjan, Steven D. Morle, and John D. West, "Mosaic analysis of stem cell function and wound healing in the mouse corneal epithelium," BMC Developmental Biology, vol. 9, no. 4, pp. 1-14, 2009.
 Y. G. Patel, K. S. Nehal, I. Aranda, Y. Li, A. C. Halpern, and M. Rajadhyaksha, "Confocal reflectance mosaicing of basal cell carcinomas in mohs surgical skin excisions," Journal of Biomedical Optics, vol. 12, no. 3, pp. 034027-1-034027-10, May/June 2007.
 F. Sadeghian, Z. Seman, A. R. Ramli, B. H. Abdul Kahar, and M. Saripan, "A framework for white blood cell segmentation in microscopic blood images using digital image processing," Biological Procedures Online, vol. 11, pp. 196-206, December 2009.
 Kang Li and Takeo Kanade, "Nonnegative mixed-norm preconditioning for microscopy image segmentation," Lecture Notes in Computer Science, vol. 5636, pp. 362-373, 2009.
 E. D. Cheng, S. Challa, R. Chakravorty, and J. Markham, "Microscopic cell segmentation by parallel detection and fusion algorithm," in Proc. of the 10th IEEE International Workshop on Multimedia Signal Processing, 2008, pp. 94-100.
 N. N. Kachouie and P. W. Fieguth, "Background estimation for microscopic cellular images," in IEEE International Conference on Image Processing, San Diego, CA, USA, October 12-15, 2008, pp. 3040- 3043.
 Alessandro Bevilacqua, Alessandro Gherardi, Ludovico Carozza, and Filippo Piccinini, "Semi-automatic background detection in microscopic images," in International Conference on Biological Science and Engineering (ICBSE), Venice, Italy, November 24-26, 2010.
 Bruce D. Lucas and Takeo Kanade, "An iterative image registration technique with an application to stereo vision," in International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
 J. Y. Bouguet, "Pyramidal implementation of the Lukas Kanade feature tracker: Description of the algorithm," In Intel Research Laboratory, Technical Report, 1999.
 H. Foroosh, J. B. Zerubia, and M. Berthod, "Extension of phase correlation to subpixel registration," IEEE Transactions on Image Processing, vol. 14, no. 1, pp. 12-22, 2002.
 M. A. Fischler and R. C. Bolles, "Random sample and consensus: A paradigm for model fitting with application to image analysis and automated cartography," Comm. of the ACM, vol. 24, no. 6, pp. 381- 395, 1981.