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Shape Analysis for Microscopy

Shape Analysis for Microscopy. Kangyu Pan in collaboration with: Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin & Michael J. Higgins Intelligent Polymer Research Institute University of Wollongong, Australia. Jens Hillebrand, Mani Ramaswami

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Shape Analysis for Microscopy

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  1. Shape Analysis for Microscopy Kangyu Pan in collaboration with: Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin & Michael J. Higgins Intelligent Polymer Research Institute University of Wollongong, Australia

  2. Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin Memory Formation Neuron cells • Stimulated synapses • Protein synthesis • Roles of the specific proteins • Shape of the synapses

  3. Roles of the specific proteins ?

  4. Co-localization of the different proteins

  5. Gaussian Mixture Model KEY: fitting a GMM to the surface of an object

  6. Optimization • Parameters of the Gaussian mixture components • Number of the components • Optimized by Split& Merge Expectation Maximization algorithm (SMEM) Merge Split • directions • distance ? ?

  7. Split Algorithm • Firstly, similar to Zhang’s split technique [1] relied on multiple random splits at each iteration [1] Z. Zhang, C. Chen, J. Sun, and K. L. Chan, “EM algorithms for Gaussian mixtures with split-and-merge operation”, Pattern Recognition, vol. 36, no. 9, pp. 1973–1983, 2003. Split operation Section(4.2.2) EM operation Publication: K. Pan, A. Kokaram, J. Hillebrand, and M. Ramaswami, “Gaussian mixtures for intensity modelling of spots in microscopy”, IEEE International Symposium on Biomedical Imaging (ISBI), 2010.

  8. Lately, we developed an error-based SMEM (eSMEM) which is deterministic, repeatable, more efficient. • Error distribution • A collection of the error that belongs to each mixture component at each pixel site

  9. Estimation error From the E-step of EM • Error distribution

  10. New Error-based Split algorithm Contour view Split • directions • distance ? ?

  11. Results Publication: K. Pan, J. Hillebrand, M. Ramaswami, and A. Kokaram, “Gaussian mixture models for spots in microscopy using a new split/merge EM algorithm”, IEEE International Conference on Image Processing (ICIP'10) , 3645-3648 (2010).

  12. GUI for the biologists

  13. Co-localization Analysis

  14. Shape of synapses ? Publication: K. Pan, D. Corrigan, J. Hillebrand, M. Ramaswami, and A. Kokaram, “A Wavelet-Based Bayesian Framework for 3D Object Segmentation in Microscopy”, SPIE BiOSSymposium.

  15. Michael J. Higgins Intelligent Polymer Research Institute University of Wollongong, Australia Regeneration of muscle tissue • Research on a novel technique that uses electrical stimulation to control the growth of muscle cells through conductive polymer materials. • To assess the performance of various processes, we must measure ‘muscle cell density’quantitatively. • Which requires the classification of: • Cell (with only one nucleus) • & • Fibres (with multiple nuclei inside cell body) Skeletal muscle cells & fibres

  16. The number of nuclei in each cell/fibre • Segmentation of the cell/fibre (especially the overlapped cells and fibres) Cell body (segmentation of the overlapped cell bodies) Skeletal cells & fibres Nuclei (Using GMM and optimized with eSMEM)

  17. A NEW ACTIVE CONTOUR TECHNIQUE FOR CELL/FIBRE SEGMENTATION Cellsnake : Publication: K. Pan, A. Kokaram , K. Gilmore , M. J. Higgins , R. Kapsa and G. G. Wallace, “Cellsnake: A new active contour technique for cell/fibre segmentation”, IEEE International Conference on Image Processing (ICIP'11) , 3645-3648 (2011).

  18. Future work • Organize the algorithms as plug-in tools for the software that the biologists used (like ‘IGOR Pro’). • Run more experiments to further examine the performance of the techniques and submit the dissertation in April.

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