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Shape Analysis for Microscopy Kangyu Pan in collaboration with: Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin & Michael.

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Presentation on theme: "Shape Analysis for Microscopy Kangyu Pan in collaboration with: Jens Hillebrand, Mani Ramaswami Institute for Neuroscience Trinity College Dublin & Michael."— Presentation transcript:

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,  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), 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), (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), (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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