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
Roles
of the specific proteins ?
Co
-
localization
of the different proteins
Gaussian Mixture Model
KEY:
fitting a GMM to the surface of an object
•
directions
•
distance
?
?
Merge
Split
Optimization
Optimized by
Split
&
Merge
Expectation Maximization
algorithm (SMEM)
Parameters
of the Gaussian mixture components
Number
of the components
[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
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
New
Error
-
based
Split algorithm
•
directions
•
distance
?
?
Split
Contour view
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).
GUI for the biologists
Co
-
localization Analysis
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.
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