Shape Analysis for Microscopy

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29 Οκτ 2013 (πριν από 4 χρόνια και 14 μέρες)

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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.