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.
In this paper we investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or - with the aid of the knowledge acquired - to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers. © 2012 Springer-Verlag.
1 aGara, Mihály1 aTasi, Tamás Sámuel1 aBalázs, Péter1 aKöthe, Ullrich1 aMontanvert, Annick1 aSoille, Pierre uhttps://www.inf.u-szeged.hu/publication/machine-learning-as-a-preprocessing-phase-in-discrete-tomography00582nas a2200145 4500008004100000245008300041210007500124260002800199300001400227100001800241700002000259700001700279700002300296856011700319 2011 eng d00aBináris tomográfiai rekonstrukció objektum alapú evolúciós algoritmussal0 aBináris tomográfiai rekonstrukció objektum alapú evolúciós algor aSzegedbNJSZTcJan 2011 a117 - 1271 aGara, Mihály1 aBalázs, Péter1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/publication/binaris-tomografiai-rekonstrukcio-objektum-alapu-evolucios-algoritmussal00525nas a2200121 4500008004100000245008200041210006900123260004400192300000700236100001800243700002000261856012200281 2010 eng d00aBinary tomographic reconstruction with an object-based evolutionary algorithm0 aBinary tomographic reconstruction with an objectbased evolutiona aSzegedbUniversity of SzegedcJune 2010 a311 aGara, Mihály1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/publication/binary-tomographic-reconstruction-with-an-object-based-evolutionary-algorithm00640nas a2200169 4500008004100000245007000041210006900111260004100180300001400221100002000235700001800255700002500273700002000298700002300318700001900341856011000360 2010 eng d00aMachine learning for supporting binary tomographic reconstruction0 aMachine learning for supporting binary tomographic reconstructio aIstambul, TurkeybSpringercAug 2010 a101 - 1051 aBalázs, Péter1 aGara, Mihály1 aTasi, Tamás Sámuel1 aKöthe, Ullrich1 aMontanvert, Annick1 aSoille, Pierre uhttps://www.inf.u-szeged.hu/publication/machine-learning-for-supporting-binary-tomographic-reconstruction00577nas a2200145 4500008004100000245007400041210007400115260003300189300002100222100001800243700002000261700002500281700002000306856010500326 2009 hun d00aDöntési fákon alapuló előfeldolgozás a bináris tomográfiában0 aDöntési fákon alapuló előfeldolgozás a bináris tomográfiában aBudapestbAkaprintcJan 2009 anincs számozás1 aGara, Mihály1 aBalázs, Péter1 aChetverikov, Dmitrij1 aSziranyi, Tamas uhttps://www.inf.u-szeged.hu/publication/dontesi-fakon-alapulo-elofeldolgozas-a-binaris-tomografiaban01354nas a2200181 4500008004100000020002200041245008700063210006900150260004500219300001400264520065900278100002000937700001800957700002400975700002600999700002001025856012701045 2009 eng d a978-3-642-02229-600aAn evolutionary approach for object-based image reconstruction using learnt priors0 aevolutionary approach for objectbased image reconstruction using aOslo, NorwaybSpringer-VerlagcJune 2009 a520 - 5293 aIn this paper we present a novel algorithm for reconstructingbinary images containing objects which can be described by some parameters. In particular, we investigate the problem of reconstructing binary images representing disks from four projections. We develop a genetic algorithm for this and similar problems. We also discuss how prior information on the number of disks can be incorporated into the reconstruction in order to obtain more accurate images. In addition, we present a method to exploit such kind of knowledge from the projections themselves. Experiments on artificial data are also conducted. © 2009 Springer Berlin Heidelberg.
1 aBalázs, Péter1 aGara, Mihály1 aSalberg, Arnt-Borre1 aHardeberg, Jon, Yngve1 aJenssen, Robert uhttps://www.inf.u-szeged.hu/publication/an-evolutionary-approach-for-object-based-image-reconstruction-using-learnt-priors00575nas a2200157 4500008004100000020001400041245008100055210006900136260000900205300001200214490000700226100001800233700002500251700002000276856012100296 2009 eng d a1218-458600aLearning connectedness and convexity of binary images from their projections0 aLearning connectedness and convexity of binary images from their c2009 a27 - 480 v201 aGara, Mihály1 aTasi, Tamás Sámuel1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/publication/learning-connectedness-and-convexity-of-binary-images-from-their-projections01168nas a2200121 4500008004100000245006500041210006500106260001200171520076400183100001800947700001700965856006400982 2009 eng d00aSupervised Color Image Segmentation in a Markovian Framework0 aSupervised Color Image Segmentation in a Markovian Framework c2009///3 aThis is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper: Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001. Note that the current demo program implements only a supervised version of the segmentation method described in the above paper (i.e. parameter values are learned interactively from representative regions selected by the user). Otherwise, the program implements exactly the color MRF model proposed in the paper. Images are automatically converted from RGB to the perceptually uniform CIE-L*u*v* color space before segmentation.
1 aGara, Mihály1 aKato, Zoltan uhttp://www.inf.u-szeged.hu/~kato/software/colormrfdemo.html01654nas a2200145 4500008004100000245010400041210006900145260004600214300001200260490000900272520104500281100002001326700001801346856014401364 2008 eng d00aDecision trees in binary tomography for supporting the reconstruction of hv-convex connected images0 aDecision trees in binary tomography for supporting the reconstru aJuan-les-Pins, FrancebSpringercOct 2008 a433-4430 v52593 aIn binary tomography, several algorithms are known for reconstructing binary images having some geometrical properties from their projections. In order to choose the appropriate reconstruction algorithm it is necessary to have a priori information of the image to be reconstructed. In this way we can improve the speed and reduce the ambiguity of the reconstruction. Our work is concerned with the problem of retrieving geometrical information from the projections themselves. We investigate whether it is possible to determine geometric features of binary images if only their projections are known. Most of the reconstruction algorithms based on geometrical information suppose $hv$-convexity or connectedness about the image to be reconstructed. We investigate those properties in detail, and also the task of separating 4- and 8-connected images. We suggest decision trees for the classification, and show some preliminary experimental results of applying them for the class of $hv$-convex and connected discrete sets.
1 aBalázs, Péter1 aGara, Mihály uhttps://www.inf.u-szeged.hu/publication/decision-trees-in-binary-tomography-for-supporting-the-reconstruction-of-hv-convex-connected-images00715nas a2200169 4500008004100000245010400041210006900145260005300214300000700267100001800274700002000292700002300312700002300335700002000358700002300378856014400401 2008 eng d00aDetermination of geometric features of binary images from their projections by using decision trees0 aDetermination of geometric features of binary images from their aSzeged, HungarybUniversity of SzegedcJuly 2008 a261 aGara, Mihály1 aBalázs, Péter1 aPalágyi, Kálmán1 aBánhelyi, Balázs1 aGergely, Tamás1 aMatievics, István uhttps://www.inf.u-szeged.hu/publication/determination-of-geometric-features-of-binary-images-from-their-projections-by-using-decision-trees