Abstract
In this paper we analyze neural stem/progenitor cells in an time-lapse image sequence. By using information about the previous positions of the cells, we are able to make a better selection of possible cells out of a collection of blob-like objects. As a blob detector we use Laplacian of Gaussian (LoG) filters at multiple scales, and the cell contours of the selected cells are segmented using dynamic programming. After the segmentation process the cells are tracked in the sequence using a combined nearest-neighbor and correlation matching technique. An evaluation of the system show that 95% of the cells were correctly segmented and tracked between consecutive frames.
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Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.C.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Transactions on Medical Imaging 21, 1212–1221 (2002)
Debeir, O., Camby, I., Kiss, R., Van Ham, P., Decaestecker, C.: A model-based approach for automated in vitro cell tracking and chemotaxis analyses. Cytometry 60A, 29–40 (2004)
Mukherjee, D., Ray, N., Acton, S.: Level set analysis for leukocyte detection and tracking. IEEE Transactions on Image Processing 13, 562–572 (2004)
Delingette, H., Montagnat, J.: Shape and topology constraints on parametric active contours. Computer Vision and Image Understanding 83, 140–171 (2001)
Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.: Multiassignment for tracking a large number of overlapping objects. IEEE Transactions on Aerospace and Electronic Systems 37, 2–21 (2001)
Gustavsson, T., Althoff, K., Degerman, J., Olsson, T., Thoreson, A.C., Thorlin, T., Eriksson, P.: Time-lapse microscopy and image processing for stem cell research modeling cell migration. In: Medical Imaging 2003: Image Processing, vol. 5032, pp. 1–15 (2003)
Wu, K., Gauthier, D., Levine, M.: Live cell image segmentation. IEEE Transactions on Biomedical Engineering 42, 1–12 (1995)
Kittler, J., Illingworth, J.: Minimun error thresholding. Pattern Recognition 19, 41–47 (1986)
ter Haar Romeny, B.: Front-End Vision & Multi-Scale Image Analysis. Kluwer Academic Publishers, Dordrecht (2003)
Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attentions. International Journal of Computer Vision 11, 283–318 (1993)
Bertsekas, D.: The auction algorithm for assignment and other network flow problems: A tutorial. Interfaces 20, 133–149 (1990)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics SMC-9, 62–66 (1979)
Althoff, K., Degerman, J., Gustavsson, T.: Tracking neural stem cells in time-lapse microscopy image sequences. In: Medical Imaging 2005: Image Processing (2005)
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Althoff, K., Degerman, J., Gustavsson, T. (2005). Combined Segmentation and Tracking of Neural Stem-Cells. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_30
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DOI: https://doi.org/10.1007/11499145_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26320-3
Online ISBN: 978-3-540-31566-7
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