02171nas a2200133 4500008004100000245005900041210005900100260006700159300000700226520166700233100001801900700002001918856009901938 2012 eng d00aArtificial intelligence methods in discrete tomography0 aArtificial intelligence methods in discrete tomography aSzegedbUniversity Szeged, Institute of InformaticscJune 2012 a163 a
Tomography is an imaging procedure to examine the internal structure of objects. The crosssection
images are constructed with the aid of the object’s projections. It is often necessary to
minimize the number of those projections to avoid the damage or destruction of the examined
object, since in most cases the projections are made by destructive rays.
Sometimes the number of available projections are so small that conventional methods cannot
provide satisfactory results. In these cases Discrete Tomograpy can provide acceptable solutions,
but it can only be used with the assumption the object is made of only a few materials,
thus only a small number of intensity values appear in the reconstructed cross-section image.
Although there are a lot of discrete tomographic reconstruction algorithms, only a few papers
deal with the determination of intensity values of the image, in advance. In our work we
try to fill this gap by using different learning methods. During the learning and classification
we used the projection values as input arguments.
In the second part of our talk we concentrate on Binary Tomography (a special kind of Discrete
Tomography)where it is supposed that the object is composed of onematerial. Thus, there
can be only two intensities on the cross-section image - one for the object points and one for
the background. Here, we compared our earlier presented binary tomographic evolutionary
reconstruction algorithm to two others. We present the details of the above-mentioned reconstruction
method and our experimental results. This paper is based on our previous works.