Shape Analysis for Microscopy

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.