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

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

7. [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.  Firstly, similar to Zhang’s split technique [1] relied on multiple random splits at each iteration 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. Split operation Section(4.2.2) EM operation Split Algorithm

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

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

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

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 BiOS Symposium.

15. 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) Michael J. Higgins Intelligent Polymer Research Institute University of Wollongong, Australia Skeletal muscle cells & fibres

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

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.