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:
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
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
Gaussian Mixture Model KEY: fitting a GMM to the surface of an object
directions distance ? ? MergeSplit Optimization Optimized by Split & Merge Expectation Maximization algorithm (SMEM) Parameters of the Gaussian mixture components Number of the components
 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  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
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
Estimation error Error distribution From the E-step of EM
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).
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.
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
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)
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).
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.