Download ITK. Ch 9 Segmentation

ITK. Ch 9 Segmentation
9.1.4 Confidence Connected
9.1.5 Isolated Connected
9.1.6 Confidence Connected in Vector Images
2010.01.30 Jin-ju Yang
9.1.4 Confidence Connected
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Examples/Segmentation/ConfidenceConnected.cxx.
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First, the algorithm computes the mean and standard deviation of intensity values
for all the pixels currently included in the region.
A user-provided factor(ƒ) is used to multiply the standard deviation and define a
range around the mean.
Neighbor pixels whose intensity values fall inside the range are accepted and
included in the region.
When no more neighbor pixels are found that satisfy the criterion, the algorithm
is considered to have finished its first iteration.
At that point, the mean and standard deviation of the intensity levels are
recomputed using all the pixels currently included in the region.
This mean and standard deviation defines a new intensity range that is used to
visit current region neighbors and evaluate whether their intensity falls inside the
range.
This iterative process is repeated until no more pixels are added or the maximum
number of iterations is reached.
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-> When faced with noisy images, it is usually convenient to
pre-process the image by using an edge preserving
smoothing filter.
Argument => Input output seed(X, Y)
-> In this case the float type is used for the
pixels due to the requirements of the
smoothing filter.
-> When faced with noisy images,
it is usually convenient to preprocess the image by using an
edge preserving smoothing filter.
->Declare the type of the region growing filter
-> The cast filter is required here to convert float
pixel types to integer types since only a few image
file formats support float types.
homogeneous regions may only require a couple of
iterations. Inhomogeneous fields may require more
iterations.
f defines how large the range of intensities will be.
In practice, it seems to be more important to
carefully select the multiplier factor than the number
of iterations.
Results
Input
ƒ = 2.5 (default value)
WM( 60,116)
Ventricle( 81,112)
GM( 107,69)
This illustrates the vulnerability of the region growing methods when the anatomical
structures to be segmented do not have a homogeneous statistical distribution over the
image space.
ƒ = 1.5
WM( 60,116)
Ventricle( 81,112)
GM( 107,69)
ƒ = 3.5
WM( 60,116)
Ventricle( 81,112)
GM( 107,69)
• Small values of the multiplier will restrict the inclusion of pixels to those
having very similar intensities to those in the current region.
• Larger values of the multiplier will relax the accepting condition and will
result in more generous growth of the region.
iteration=5, ƒ = 2.5 (default value)
iteration = 1
iteration = 2
iteration = 3
iteration = 4
This iterative process is repeated until no more pixels are added or
the maximum number of iterations is reached.
9.1.5 Isolated Connected
• Examples/Segmentation/IsolatedConnectedImageFilter.cxx.
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This filter is a close variant of the ConnectedThresholdImageFilter.
In this filter two seeds and a lower threshold are provided by the user.
The filter will grow a region connected to the first seed and not connected to the
second one.
In order to do this, the filter finds an intensity value that could be used as upper
threshold for the first seed.
A binary search is used to find the value that separates both seeds.
Argument => Input output seed1(X, Y) lower seed2(X, Y)
->by user GM seed
->by user
-> by user WM seed
Results
input
Smoothed image
output
• The selection of threshold values should therefore be performed in the
smoothed image since the distribution of intensities could be quite different
from that of the input image.
• This filter is intended to be used in cases where adjacent anatomical
structures are difficult to separate.
• Selecting one seed in one structure and the other seed in the adjacent
structure creates the appropriate setup for computing the threshold that will
separate both structures.
9.1.6 Confidence Connected in Vector Images
• Examples/Segmentation/VectorConfidenceConnected.cxx.
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This example illustrates the use of the confidence connected concept applied to
images with vector pixel types.
The basic difference between the scalar and vector version is that the vector
version uses the covariance matrix instead of a variance, and a vector mean
instead of a scalar mean.
The membership of a vector pixel value to the region is measured using the
Mahalanobis distance.