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Shape Analysis for MicroscopyKangyu Pan
in collaboration with:
Jens Hillebrand, Mani RamaswamiInstitute for Neuroscience
Trinity College Dublin&
Michael J. HigginsIntelligent Polymer Research InstituteUniversity of Wollongong, Australia
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Memory Formation
Neuron cells Stimulated synapses
Protein synthesis
Roles of the specific proteins Shape of the synapses
Jens Hillebrand, Mani RamaswamiInstitute for Neuroscience
Trinity College Dublin
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Roles of the specific proteins ?
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Co-localization of the different proteins
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Gaussian Mixture Model
KEY: fitting a GMM to the surface of an object
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• directions• distance ?
?
Merge Split
Optimization
Optimized by Split & Merge Expectation Maximization algorithm (SMEM)
Parameters of the Gaussian mixture components
Number of the components
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[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 forintensity modelling of spots in microscopy”, IEEE International Symposium on Biomedical Imaging (ISBI), 2010.
Split operationSection(4.2.2)
EM operation
Split Algorithm
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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
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Estimation error
Error distribution
|)()(|)( nnormnEMn xIxIxE
)()()( nnmnm xExwxE
From the E-step of EM
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New Error-basedSplit algorithm
• directions• distance ?
?
Split2minx
1minxi
j
maxxminX
j
i
Contour view
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)(xInorm )(xIEM
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).
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GUI for the biologists
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Co-localization Analysis
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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.
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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. HigginsIntelligent Polymer Research InstituteUniversity of Wollongong, Australia
Skeletal muscle cells & fibres
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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)
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A NEW ACTIVE CONTOUR TECHNIQUE FOR CELL/FIBRE SEGMENTATIONCellsnake :
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).
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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.