Segmentation_2_component_image_514_V2
Aim:
Manual or automatic segmentation of 2-component image, via the feature
space (scatterplot).
History:
June 2005: First developments (in Java) by Laetitia Pasquet and
No�l Bonnet.
June 2006: Version 2 by Cedric Gillet and No�l Bonnet
Method:
Load the 2 images:
1) Building the scatterplot:
Scatterplot: linear
scale
logarithmic scale
Only pixels of both images with a grey level value between the
minimum and the maximum threshold are considered.
In addition, a coefficient of reduction can be applied. If the
coefficient equals 2, the size of the scatterplot is 128 x 128, for
instance.
Two scatterplots are produced, one with a linear scale and the other
one with a logarithmic scale. Any one can be used for the others steps
of the procedures.
2) Choice of the automatic or
interactive mode of segmentation
- Manual (interactive) mode:
.......
After the number of classes (N) has beeen selected, the user
has to draw N regions of interest on the scatterplot. Each time a ROI
is drawn, pressing "Enter" allows to store the ROI and select a grey
level value corresponding to that ROI (204 in the exemple above).
Whan all the ROIs have been drawn, a composite figure (with the
different classes of pixels) is built in the feature space
(scatterplot) and in the image space (see below). Black pixels are
pixels that were not included in any ROI.
Partitionned
scatterplot
Partitionned (segmented) image space
- Automatic mode:
Note that the number of classes is irrelevant
First phase: look at the variation of the number of
modes
(= classes)
in the automatic mode
as a function of the
smoothing
parameter (sigma)
The curve plots the number of modes as a function of the smoothing
parameter. A plateau is generally expected for the "true" number of
classes. Here, we can see a plateau for 4 classes for sigma between 7
and 15. Note that several plateaus can sometimes be obtained,
reflecting the
intrinsic hierarchical nature of clustering.
This first phase can be repeated several times, when necessary.
Second phase: fix a value of sigma
Estimated pdf
for the final pdf estimation (here, sigma=8)
Automatically partitionned pdf
Automatically
partitionned (segmented) image space
Version 2:
In the manual or the automatic mode,
several coefficients of colocalisation are computed (for the whole
image, for the computed scatterplot, and for the selected or
automatically computed regions.