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An accurate method
for tissue type detection and classification
on T2- and diffusion-weighted brain
MRI based on the image intensity
exponential decay is presented.
Relaxation weighted images and diffusion
coded images were both obtained
at equal intervals of image setting
parameters, allowing for an appropriate
data set for Prony method application.
Furthermore, diffusion coded images
were obtained for three orthogonal
gradient directions, allowing for
image data parameterized by the
Apparent Diffusion Coefficient (ADC)
or independently by each orthogonal
diffusion tensor component. Image
noise was reduced by the application
of morphological operators to select
representative decaying behaviors
and geometrical regularization of
the decay data points by adjusting
its geometrical properties to those
that are expected from noiseless
data, i.e., monotonous and convex
behavior. Data points were fitted
by an over determined Prony interpolation
procedure. Relaxation rate and diffusion
coefficient distributions were obtained
and tissue classification was performed
by means of the determination of
principal relaxation rate and diffusion
coefficients or diffusion modes
using a suitable mathematical morphology
operator, i.e., watershed or similar.
Image segmentation was performed
by linear regression analysis on
a pixel by pixel basis assuming
that the pixel intensity decay is
composed by a linear superposition
of the relaxation rate or diffusion
modes previously obtained from the
correspondent distribution functions.
The main advantage of the proposed
method rests in its accuracy and
speed of calculation with respect
to other methods such as Inverse
Laplace Transform algorithms, making
it suitable for on line application
on imaging data
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