[Insight-users] Some questions about Geodesic Active Contours

[Ivan Macia]

Dear ITK users:

I have successfully implemented the Geodesic Active Contours pipeline and
the Watershed segmentation for segmenting liver structures in MRI images.
Both have been proven to work (thanks Joshua and Luis for the advice).

Now im trying to understand how the GAC filter works and try adjust the
parameters in the GAC pipeline. I´m using exactly the pipeline that appears
in the ITK Software Guide, but changing the Gradient Magnitude Gaussian for
a Gradient Magnitude (this has given better results for me) with a number of
about 20 iterations in the Curvature Anisotropic Diffusion filter.

As I understand, the GAC filter requires 2 inputs:

1) An initial level-set:

As the GAC solves an initial-value problem for the differential equation of
the level-set, is it necessary to provide a level-set for t = 0, which
evolves following the equation. This can be the output of any other level
set method or for example a Fast Marching Filter. Here I understand that the
role of the Fast Marching is creating an initial level-set. My question
regarding this are the following:
	a) The initial level-set as the output of the Fast Marching would have a
shape depending on the selected seed points and the time chosen as parameter
but this is only my guess.
	b) What is the difference between the standard Fast Marching and the way it
is used here?
	c) To which extent the chosen seed points for the Fast Marching affect the
final result of the GAC? If by error I select a seed point outside the
region would the final result be wrong? And if the boundaries of the zero
level-set resulting from the Fast Marching where outside the region to
segment would the result be wrong? I imagine this is controlled by the
distance parameter of the Fast Marching but I don´t know if it´s critical.
	d) In my case i have to segment the liver which has a quite complicated
shape. Would be better to use another initial level-set that would give
better results?

2) A Feature Image.
As I understand this Feature Image affects both, the Propagation and the
Advection terms in the differential equation. In one of the answers of Luis
I read that the vector map A(x) of the advection term is computed over the
Feature Image as the inverted Gradient Recursive Gaussian of this image. My
questions here:
	a) I guess that here the Feature Image (here output of the Sigmoid) is very
important, as it is the one that really gives the information about the
shape of the object but to which extent? Of course I guess that that also
depends on the weights of alpha, beta and gamma.
	b) What is the diference between the Propagation and Advection terms in the
way they act? I think that both make the region grow. Is that right?
	c) Finally there is the Curvature Term. I guess this is only for smoothing
the result depending on the curvature of the boundaries. If I´m not wrong
this can make the region to grow and shrink. Am I?

Seems like a lot of questions but I would really appreciate some information
/ advice on all or some of these questions.

Thank you very much in advance for your help

Ivan

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Ivan Macia
Ing. Industrial
Ing. Automatica y Electronica Ind.
VICOMTech - Visual Interaction and
Communication Technologies Center
Paseo Mikeletegi 57
20009 San Sebastian
Spain
Tel: +34 943 30 92 30
Fax: +34 943 30 93 93
e-mail: imacia at vicomtech . es
http://www . vicomtech . es

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__________________________________