Chlamydiae are obligate intracellular bacteria that propagate in the inclusion, a specific niche inside the host cell. The standard method for counting chlamydiae is the immunofluorescent staining and manual counting of chlamydial inclusions. High or medium throughput estimation of the reduction in chlamydia inclusions should be the basis of testing antichlamydial compounds and other drugs that positively or negatively influence chlamydial growth, yet low-throughput manual counting is the common approach. To overcome the time-consuming and subjective manual counting we developed an automatic inclusion counting system based on a commercially available DNA chip scanner. Fluorescently labeled inclusions are detected by the scanner, and the image is processed by ChlamyCount, a custom plugin of the ImageJ software environment. ChlamyCount was able to measure the inclusion counts over a one log dynamic range with high correlation to the theoretical counts. ChlamyCount was capable of accurately determining the minimum inhibitory concentration of the novel antimicrobial compound PCC00213 and the already known antichlamydial antibiotics moxifloxacin and tetracycline. ChlamyCount was also able to measure the chlamydial growth altering effect of drugs that influence host-bacterium interaction such as interferon-gamma, DEAE-dextran and cycloheximide. ChlamyCount is an easily adaptable system for testing antichlamydial antimicrobials and other compounds that influence Chlamydia-host interactions.

1 aBogdanov, Anita1 aEndrész, Valéria1 aUrbán, Szabolcs1 aLantos, Ildikó1 aDeák, Judit1 aBurián, Katalin1 aÖnder, K1 aAyaydin, Ferhan1 aBalázs, Péter1 aVirók, Dezső, P uhttps://www.inf.u-szeged.hu/en/publication/application-of-dna-chip-scanning-technology-for-the-automatic-detection-of-chlamydia-trachomatis-and-chlamydia-pneumoniae-inclusions01719nas a2200157 4500008004100000020001400041245007000055210006900125260000900194520115400203100002701357700002801384700001601412700002001428856011301448 2014 eng d a1077-314200aLocal and global uncertainty in binary tomographic reconstruction0 aLocal and global uncertainty in binary tomographic reconstructio c20143 aIn binary tomography the goal is to reconstruct the innerstructure of homogeneous objects from their projections. This is usually required from a low number of projections, which are also likely to be aﬀected by noise and measurement errors. In general, the distorted and incomplete projection data holds insuﬃcient information for the correct reconstruction of the original object. In this paper, we describe two methods for approximating the local uncertainty of the reconstructions, i.e., identifying how the information stored in the projections determine each part of the reconstructed image. These methods can measure the uncertainty of the reconstruction without any knowledge from the original object itself. Moreover, we provide a global uncertainty measure that can assess the information content of a projection set and predict the error to be expected in the reconstruction of a homogeneous object. We also give an experimental evaluation of our proposed methods, mention some of their possible applications, and describe how the uncertainty measure can be used to improve the performance of the DART reconstruction algorithm.

1 aVarga, László Gábor1 aNyúl, László, Gábor1 aNagy, Antal1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/local-and-global-uncertainty-in-binary-tomographic-reconstruction00460nas a2200121 4500008004100000020001400041245013900055210006900194260000900263300001400272490000700286856004500293 2013 eng d a1217-895000aAPPLICATION OF DNA CHIP SCANNING TECHNOLOGY FOR THE AUTOMATIC DETECTION OF CHLAMYDIA TRACHOMATIS AND CHLAMYDIA PNEUMONIAE INCLUSIONS0 aAPPLICATION OF DNA CHIP SCANNING TECHNOLOGY FOR THE AUTOMATIC DE c2013 a173 - 1740 v60 uhttps://www.inf.u-szeged.hu/en/node/116500394nas a2200097 4500008004100000245008100041210007800122260003800200300001400238856004400252 2013 eng d00aBináris képek rekonstrukciója két vetületből és morfológiai vázból0 aBináris képek rekonstrukciója két vetületből és morfológiai vázb aVeszprémbNJSZT-KÉPAFcJan 2013 a182 - 193 uhttps://www.inf.u-szeged.hu/en/node/94000635nas a2200133 4500008004100000245011700041210006900158260003800227300001400265100002100279700002000300700002100320856016000341 2013 eng d00aA comparison of heuristics for reconstructing hv-convex binary matrices from horizontal and vertical projections0 acomparison of heuristics for reconstructing hvconvex binary matr aVeszprémbNJSZT-KÉPAFcJan 2013 a168 - 1811 aOzsvár, Zoltán1 aBalázs, Péter1 aCzúni, László uhttps://www.inf.u-szeged.hu/en/publication/a-comparison-of-heuristics-for-reconstructing-hv-convex-binary-matrices-from-horizontal-and-vertical-projections00598nas a2200133 4500008004100000020001400041245012200055210006900177260000900246300001600255490000800271100002000279856016500299 2013 eng d a0166-218X00aComplexity results for reconstructing binary images with disjoint components from horizontal and vertical projections0 aComplexity results for reconstructing binary images with disjoin c2013 a2224 - 22350 v1611 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/complexity-results-for-reconstructing-binary-images-with-disjoint-components-from-horizontal-and-vertical-projections01468nas a2200169 4500008004100000245005600041210005600097260004600153300001100199520089100210100002501101700002801126700002001154700003101174700002601205856006701231 2013 eng d00aDirectional Convexity Measure for Binary Tomography0 aDirectional Convexity Measure for Binary Tomography aBerlin; HeidelbergbSpringer Verlagc2013 a9 - 163 aThere is an increasing demand for a new measure of convexity fordiscrete sets for various applications. For example, the well- known measures for h-, v-, and hv-convexity of discrete sets in binary tomography pose rigorous criteria to be satisfied. Currently, there is no commonly accepted, unified view on what type of discrete sets should be considered nearly hv-convex, or to what extent a given discrete set can be considered convex, in case it does not satisfy the strict conditions. We propose a novel directional convexity measure for discrete sets based on various properties of the configuration of 0s and 1s in the set. It can be supported by proper theory, is easy to compute, and according to our experiments, it behaves intuitively. We expect it to become a useful alternative to other convexity measures in situations where the classical definitions cannot be used.

1 aTasi, Tamás Sámuel1 aNyúl, László, Gábor1 aBalázs, Péter1 aSanniti di Baja, Gabriella1 aRuiz-Shulcloper, Jose uhttp://link.springer.com/chapter/10.1007%2F978-3-642-41827-3_201422nas a2200181 4500008004100000020001400041245004900055210004900104260000900153300001400162490000800176520087500184100002401059700002801083700002001111700001701131856009201148 2013 eng d a1077-314200aDynamic angle selection in binary tomography0 aDynamic angle selection in binary tomography c2013 a306 - 3180 v1173 aIn this paper, we present an algorithm for the dynamic selection of projection angles in binary tomography. Based on the information present in projections that have already been measured, a new projection angle is computed, which aims to maximize the information gained by adding this projection to the set of measurements. The optimization model used for angle selection is based on a characterization of solutions of the binary reconstruction problem, and a related definition of information gain. From this formal model, an algorithm is obtained by several approximation steps. Results from a series of simulation experiments demonstrate that the proposed angle selection scheme is indeed capable of finding angles for which the reconstructed image is much more accurate than for the standard angle selection scheme. © 2012 Elsevier Inc. All rights reserved.

1 aBatenburg, Joost, K1 aPalenstijn, Willem, Jan1 aBalázs, Péter1 aSijbers, Jan uhttps://www.inf.u-szeged.hu/en/publication/dynamic-angle-selection-in-binary-tomography01385nas a2200157 4500008004100000020001400041245010800055210006900163260000900232300001400241490000700255520077300262100002101035700002001056856015101076 2013 eng d a0324-721X00aAn empirical study of reconstructing hv-convex binary matrices from horizontal and vertical projections0 aempirical study of reconstructing hvconvex binary matrices from c2013 a149 - 1630 v213 aThe reconstruction of hv-convex binary matrices (or equivalently, binary images) from their horizontal and vertical projections is proved to be NP-hard. In this paper we take a closer look at the difficulty of the problem. We investigate different heuristic reconstruction algorithms of the class, and compare them from the viewpoint of running-time and reconstruction quality. Using a large set of test images of different sizes and with varying number of components, we show that the reconstruction quality can depend not only on the size of the image, but on the number and location of its components, too. We also reveal that the reconstruction time can also be affected by the number of the so-called switching components present in the image.

1 aOzsvár, Zoltán1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/an-empirical-study-of-reconstructing-hv-convex-binary-matrices-from-horizontal-and-vertical-projections00616nas a2200145 4500008004100000245009200041210007500133260003800208300001400246100002700260700002000287700001600307700002100323856012600344 2013 eng d00aGradiens módszerek automatikus súlyozásán alapuló diszkrét tomográfiai eljárás0 aGradiens módszerek automatikus súlyozásán alapuló diszkrét tomog aVeszprémbNJSZT-KÉPAFcJan 2013 a210 - 2231 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aCzúni, László uhttps://www.inf.u-szeged.hu/en/publication/gradiens-modszerek-automatikus-sulyozasan-alapulo-diszkret-tomografiai-eljaras01269nas a2200169 4500008004100000245005900041210005900100260004300159300001400202520067100216100002700887700002800914700001600942700002000958700001900978856010200997 2013 eng d00aLocal uncertainty in binary tomographic reconstruction0 aLocal uncertainty in binary tomographic reconstruction aCalgarybIASTED - Acta PresscFeb 2013 a490 - 4963 a

We describe a new approach for the uncertainty problem arisingin the field of discrete tomography, when the low number of projections does not hold enough information for an accurate, and reliable reconstruction. In this case the lack of information results in uncertain parts on the reconstructed image which are not determined by the projections and cannot be reliably reconstructed without additional information. We provide a method that can approximate this local uncertainty of reconstructions, and show how each pixel of the reconstructed image is determined by a set of given projections. We also give experimental results for validating our approach.

1 aVarga, László Gábor1 aNyúl, László, Gábor1 aNagy, Antal1 aBalázs, Péter1 aKampel, Martin uhttps://www.inf.u-szeged.hu/en/publication/local-uncertainty-in-binary-tomographic-reconstruction00491nas a2200097 4500008004100000245008700041210006900128260004800197100002000245856012800265 2013 eng d00aPrior Information, Machine Learning, and Direction Dependency in Binary Tomography0 aPrior Information Machine Learning and Direction Dependency in B aSzeged, HungarybUniversity of Szegedc20131 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/prior-information-machine-learning-and-direction-dependency-in-binary-tomography00683nas a2200157 4500008004100000020002200041245009300063210006900156260005300225300001400278100002000292700002000312700002600332700003100358856013600389 2013 eng d a978-3-642-41821-100aReconstruction and Enumeration of hv-Convex Polyominoes with Given Horizontal Projection0 aReconstruction and Enumeration of hvConvex Polyominoes with Give aHeidelberg; London; New YorkbSpringercNov 2013 a100 - 1071 aHantos, Norbert1 aBalázs, Péter1 aRuiz-Shulcloper, Jose1 aSanniti di Baja, Gabriella uhttps://www.inf.u-szeged.hu/en/publication/reconstruction-and-enumeration-of-hv-convex-polyominoes-with-given-horizontal-projection00626nas a2200145 4500008004100000020001400041245012100055210006900176260000900245300001400254490000800268100002000276700002000296856016400316 2013 eng d a0169-296800aThe reconstruction of polyominoes from horizontal and vertical projections and morphological skeleton is NP-complete0 areconstruction of polyominoes from horizontal and vertical proje c2013 a343 - 3590 v1251 aHantos, Norbert1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/the-reconstruction-of-polyominoes-from-horizontal-and-vertical-projections-and-morphological-skeleton-is-np-complete01780nas a2200205 4500008004100000020002200041245006700063210006700130260007800197300001200275520103100287100001901318700002001337700002501357700002201382700002201404700001701426700002101443856011001464 2013 eng d a978-3-319-02894-100aRestoration of blurred binary images using discrete tomography0 aRestoration of blurred binary images using discrete tomography aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer Verlagc2013 a80 - 903 aEnhancement of degraded images of binary shapes is an important task in many image processing applications, *e.g.* to provide appropriate image quality for optical character recognition. Although many image restoration methods can be found in the literature, most of them are developed for grayscale images. In this paper we propose a novel binary image restoration algorithm. As a first step, it restores the projections of the shape using 1-dimensional deconvolution, then reconstructs the image from these projections using a discrete tomography technique. The method does not require any parameter setting or prior knowledge like an estimation of the signal-to-noise ratio. Numerical experiments on a synthetic dataset show that the proposed algorithm is robust to the level of the noise. The efficiency of the method has also been demonstrated on real out-of-focus alphanumeric images.

1 aNemeth, Jozsef1 aBalázs, Péter1 aBlanc-Talon, Jacques1 aKasinski, Andrzej1 aPhilips, Wilfried1 aPopescu, Dan1 aScheunders, Paul uhttps://www.inf.u-szeged.hu/en/publication/restoration-of-blurred-binary-images-using-discrete-tomography01724nas a2200181 4500008004100000245013300041210006900174260002800243300001400271520095600285100002001241700002001261700002201281700002101303700002001324700002201344856017601366 2013 eng d00aA uniqueness result for reconstructing hv-convex polyominoes from horizontal and vertical projections and morphological skeleton0 auniqueness result for reconstructing hvconvex polyominoes from h aTriestebIEEEcSep 2013 a788 - 7933 a

In this article we study the uniqueness of the reconstruction in a special class of 4-connected hv-convex images, using two projections and the so-called morphological skeleton. Generally, if just the two projections are given, there can be exponentially many hv-convex 4-connected images satisfying them. Knowing the morphological skeleton in addition, we can reduce the number of solutions. In the studied class, the images are defined by two parameters. We show that the uniqueness of their reconstruction depends only on the values of those parameters.

1 aHantos, Norbert1 aBalázs, Péter1 aRamponi, Giovanni1 aLončarić, Sven1 aCarini, Alberto1 aEgiazarian, Karen uhttps://www.inf.u-szeged.hu/en/publication/a-uniqueness-result-for-reconstructing-hv-convex-polyominoes-from-horizontal-and-vertical-projections-and-morphological-skeleton02174nas a2200133 4500008004100000245005900041210005900100260006700159300000700226520166700233100001801900700002001918856010201938 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 binary tomography, the goal is to reconstruct binary images from a small set of their projections. However, especially when only two projections are used, the task can be extremely underdetermined. In this paper, we show how to reduce ambiguity by using the morphological skeleton of the image as a priori. Three different variants of our method based on Simulated Annealing are tested using artificial binary images, and compared by reconstruction time and error. © 2012 Springer-Verlag.

1 aHantos, Norbert1 aBalázs, Péter1 aPalágyi, Kálmán1 aBarneva, Reneta, P1 aBrimkov, Valentin, E1 aAggarwal, Jake, K uhttps://www.inf.u-szeged.hu/en/publication/binary-image-reconstruction-from-two-projections-and-skeletal-information00436nas a2200109 4500008004100000245007100041210006900112260006000181300000700241490003300248856004500281 2012 eng d00aBinary tomography using two projections and morphological skeleton0 aBinary tomography using two projections and morphological skelet aSzegedbUniv Szeged Institute of InformaticscJune 2012 a200 vVolume of Extended Abstracts uhttps://www.inf.u-szeged.hu/en/node/114501191nas a2200157 4500008004100000245009700041210006900138260007600207300001400283520051000297100002000807700002400827700002300851700001900874856014000893 2012 eng d00aA central reconstruction based strategy for selecting projection angles in binary tomography0 acentral reconstruction based strategy for selecting projection a aBerlin; Heidelberg; New York; London; Paris; TokyobSpringercJune 2012 a382 - 3913 aIn this paper we propose a novel strategy for selecting projection angles in binary tomography which yields significantly more accurate reconstructions than others. In contrast with previous works which are of experimental nature, the method we present is based on theoretical observations. We report on experiments for different phantom images to show the effectiveness and roboustness of our procedure. The practically important case of noisy projections is also studied. © 2012 Springer-Verlag.

1 aBalázs, Péter1 aBatenburg, Joost, K1 aCampilho, Aurélio1 aKamel, Mohamed uhttps://www.inf.u-szeged.hu/en/publication/a-central-reconstruction-based-strategy-for-selecting-projection-angles-in-binary-tomography00458nas a2200097 4500008004100000245009900041210007400140260009400214300000700308856004500315 2012 eng d00aChlamydia inklúziók automatizált számolása fluoreszcens DNS-chip szkenner segítségével0 aChlamydia inklúziók automatizált számolása fluoreszcens DNSchip aSzegedbSZTE ÁOK II. sz. Belgyógyászati Klinika és Kardiológiai Központc2012.11.23 a37 uhttps://www.inf.u-szeged.hu/en/node/115200607nas a2200121 4500008004100000245010700041210006900148260007000217300000700287100002100294700002000315856015000335 2012 eng d00aEmpirical studies of reconstructing hv-convex binary matrices from horizontal and vertical projections0 aEmpirical studies of reconstructing hvconvex binary matrices fro aSzegedbUniversity of Szeged, Institute of InformaticscJune 2012 a441 aOzsvár, Zoltán1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/empirical-studies-of-reconstructing-hv-convex-binary-matrices-from-horizontal-and-vertical-projections01136nas a2200193 4500008004100000245008800041210006900129260005500198300001400253520037300267100002700640700002000667700001600687700002800703700002400731700002600755700003000781856013100811 2012 eng d00aAn energy minimization reconstruction algorithm for multivalued discrete tomography0 aenergy minimization reconstruction algorithm for multivalued dis aLondonbCRC Press - Taylor and Frances Groupc2012 a179 - 1853 aWe propose a new algorithm for multivalued discrete tomography, that reconstructs images from few projections by approximating the minimum of a suitably constructed energy function with a deterministic optimization method. We also compare the proposed algorithm to other reconstruction techniques on software phantom images, in order to prove its applicability.

1 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aDi Giamberardino, Paolo1 aIacoviello, Daniela1 aJorge, Renato M Natal1 aTaveres, Joao, Manuel R S uhttps://www.inf.u-szeged.hu/en/publication/an-energy-minimization-reconstruction-algorithm-for-multivalued-discrete-tomography00553nas a2200121 4500008004100000245007800041210006900119260007000188300000700258100002500265700002000290856012100310 2012 eng d00aExtracting geometrical features of discrete images from their projections0 aExtracting geometrical features of discrete images from their pr aSzegedbUniversity of Szeged, Institute of InformaticscJune 2012 a521 aTasi, Tamás Sámuel1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/extracting-geometrical-features-of-discrete-images-from-their-projections01369nas a2200181 4500008004100000245006900041210006900110260008200179300001400261520067500275100001800950700002500968700002000993700002001013700002301033700001901056856011201075 2012 eng d00aMachine learning as a preprocessing phase in discrete tomography0 aMachine learning as a preprocessing phase in discrete tomography aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer VerlagcAug 2012 a109 - 1243 aIn 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/en/publication/machine-learning-as-a-preprocessing-phase-in-discrete-tomography00611nas a2200133 4500008004100000245009200041210006900133260007000202300000700272100002700279700002000306700001600326856013500342 2012 eng d00aA novel optimization-based reconstruction algorithm for multivalued discrete tomography0 anovel optimizationbased reconstruction algorithm for multivalued aSzegedbUniversity of Szeged, Institute of InformaticscJune 2012 a571 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal uhttps://www.inf.u-szeged.hu/en/publication/a-novel-optimization-based-reconstruction-algorithm-for-multivalued-discrete-tomography00584nas a2200133 4500008004100000245008700041210006900128260004800197300001200245100002700257700002000284700001600304856013000320 2012 eng d00aAn optimization-based reconstruction algorithm for multivalued discrete tomography0 aoptimizationbased reconstruction algorithm for multivalued discr aVeszprémbUniversity of PannoniacDec 2012 a39 - 401 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal uhttps://www.inf.u-szeged.hu/en/publication/an-optimization-based-reconstruction-algorithm-for-multivalued-discrete-tomography01183nas a2200181 4500008004100000245008800041210006900129260004700198300001400245520049500259100002500754700001600779700002000795700001400815700001600829700002500845856013100870 2012 eng d00aPerimeter estimation of some discrete sets from horizontal and vertical projections0 aPerimeter estimation of some discrete sets from horizontal and v aCrete, GreekbIASTED ACTA PresscJune 2012 a174 - 1813 aIn this paper, we design neural networks to estimate the perimeter of simple and more complex discrete sets from their horizontal and vertical projections. The information extracted this way can be useful to simplify the problem of reconstructing the discrete set from its projections, which task is in focus of discrete tomography. Beside presenting experimental results with neural networks, we also reveal some statistical properties of the perimeter of the studied discrete sets.

1 aTasi, Tamás Sámuel1 aHegedűs, M1 aBalázs, Péter1 aPetrou, M1 aSappa, A, D1 aTriantafyllidis, A G uhttps://www.inf.u-szeged.hu/en/publication/perimeter-estimation-of-some-discrete-sets-from-horizontal-and-vertical-projections00555nas a2200133 4500008004100000245007500041210006900116260004800185300000700233100002000240700002000260700002300280856011800303 2012 eng d00aSolving binary tomography from morphological skeleton via optimization0 aSolving binary tomography from morphological skeleton via optimi aVeszprémbUniversity of PannoniacDec 2012 a421 aHantos, Norbert1 aBalázs, Péter1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/en/publication/solving-binary-tomography-from-morphological-skeleton-via-optimization00384nas a2200097 4500008004100000245008300041210007500124260002800199300001400227856004500241 2011 eng d00aBináris tomográfiai rekonstrukció objektum alapú evolúciós algoritmussal0 aBináris tomográfiai rekonstrukció objektum alapú evolúciós algor aSzegedbNJSZTcJan 2011 a117 - 127 uhttps://www.inf.u-szeged.hu/en/node/112801426nas a2200169 4500008004100000020001400041245007300055210006900128260001300197300001400210490000700224520084600231100002701077700002001104700001601124856011601140 2011 eng d a1524-070300aDirection-dependency of binary tomographic reconstruction algorithms0 aDirectiondependency of binary tomographic reconstruction algorit cNov 2011 a365 - 3750 v733 aIn this work we study the relation between the quality of a binary tomographic reconstruction and the choice of angles of the projections. We conduct experiments on a set of software phantoms by reconstructing them from different projection sets using three different discrete tomography reconstruction algorithms, and compare the accuracy of the corresponding reconstructions with suitable approaches. To validate our results for possible real-world applications, we conduct the experiments by adding random noise of different characteristics to the simulated projection data, and by applying small topological changes on the phantom images as well. In addition, we also discuss some consequences of the angle-selection dependency and possible practical applications arising from the field of non-destructive testing, too.

1 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal uhttps://www.inf.u-szeged.hu/en/publication/direction-dependency-of-binary-tomographic-reconstruction-algorithms00253nam a2200085 4500008004100000245002300041210002300064260003500087856004500122 2011 eng d00aKéprekonstrukció0 aKéprekonstrukció aBudapestbTypotex Kiadóc2011 uhttps://www.inf.u-szeged.hu/en/node/115000562nas a2200145 4500008004100000245007200041210007200113260002800185300001400213100002000227700002000247700001700267700002300284856010900307 2011 eng d00aMediánszűrés alkalmazása algebrai rekonstrukciós módszerekben0 aMediánszűrés alkalmazása algebrai rekonstrukciós módszerekben aSzegedbNJSZTcJan 2011 a106 - 1161 aHantos, Norbert1 aBalázs, Péter1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/en/publication/medianszures-alkalmazasa-algebrai-rekonstrukcios-modszerekben01793nas a2200169 4500008004100000020001400041245005700055210005700112260006500169300001400234490000700248520120500255100002701460700002001487700001601507856010001523 2011 eng d a0324-721X00aProjection selection dependency in binary tomography0 aProjection selection dependency in binary tomography aSzegedbUniversity of Szeged, Institute of Informaticsc2011 a167 - 1870 v203 aIt has already been shown that the choice of projection angles can significantly influence the quality of reconstructions in discrete tomography. In this contribution we summarize and extend the previous results by explaining and demonstrating tile effects of projection selection dependency, in a set of experimental software tests. We perform reconstructions of software phantoms, by using different binary tomography reconstruction algorithms, from different equiangular and non-equiangular projections sets, under various conditions (i.e., when the objects to be reconstructed undergo slight topological changes, or the projection data is affected by noise) and compare the results with suitable approaches. Based on our observations, we reveal regularities in the resulting data and discuss possible consequences of such projection selection dependency in binary tomography.

1 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal uhttps://www.inf.u-szeged.hu/en/publication/projection-selection-dependency-in-binary-tomography00652nas a2200145 4500008004100000245009900041210007200140260005900212300001400271100002000285700002400305700001900329700002000348856013800368 2011 hun d00aTehetséggondozó program a Szegedi Tudományegyetem Informatikai Tanszékcsoport BSc szakjain0 aTehetséggondozó program a Szegedi Tudományegyetem Informatikai T aDebrecenbDebreceni Egyetem Informatikai KarcAug 2011 a905 - 9121 aBalázs, Péter1 aNémeth, Zoltán, L1 aCser, László1 aHerdon, Miklós uhttps://www.inf.u-szeged.hu/en/publication/tehetseggondozo-program-a-szegedi-tudomanyegyetem-informatikai-tanszekcsoport-bsc-szakjain00555nas a2200157 4500008004100000245005900041210005900100260002800159300001300187100002700200700002000227700001600247700001700263700002300280856009400303 2011 hun d00aVetületi irányfüggőség a bináris tomográfiában0 aVetületi irányfüggőség a bináris tomográfiában aSzegedbNJSZTcJan 2011 a92 - 1051 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aKato, Zoltan1 aPalágyi, Kálmán uhttps://www.inf.u-szeged.hu/en/publication/vetuleti-iranyfuggoseg-a-binaris-tomografiaban00528nas a2200121 4500008004100000245008200041210006900123260004400192300000700236100001800243700002000261856012500281 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/en/publication/binary-tomographic-reconstruction-with-an-object-based-evolutionary-algorithm01538nas a2200217 4500008004100000020002200041245007400063210006900137260004800206300001400254520074200268100002701010700002001037700001601057700002301073700002501096700002501121700002601146700003101172856011701203 2010 eng d a978-3-642-12711-300aDirection-dependency of a binary tomographic reconstruction algorithm0 aDirectiondependency of a binary tomographic reconstruction algor aBuffalo, NY, USAbSpringer VerlagcMay 2010 a242 - 2533 a

We study how the quality of an image reconstructed by a binary tomographic algorithm depends on the direction of the observed object in the scanner, if only a few projections are available. To do so we conduct experiments on a set of software phantoms by reconstructing them form different projection sets using an algorithm based on D.C. programming (a method for minimizing the difference of convex functions), and compare the accuracy of the corresponding reconstructions by two suitable approaches. Based on the experiments, we discuss consequences on applications arising from the field of non-destructive testing, as well.

1 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aBarneva, Reneta, P1 aBrimkov, Valentin, E1 aHauptman, Herbert, A1 aJorge, Renato M Natal1 aTavares, João, Manuel R S uhttps://www.inf.u-szeged.hu/en/publication/direction-dependency-of-a-binary-tomographic-reconstruction-algorithm01696nas a2200289 4500008004100000020002200041245009900063210006900162260005400231300001400285520070400299100002001003700002001023700001801043700001901061700001901080700001901099700001801118700001901136700002201155700001701177700001901194700002101213700001501234700001601249856014101265 2010 eng d a978-3-642-17276-200aImage enhancement by median filters in algebraic reconstruction methods: an experimental study0 aImage enhancement by median filters in algebraic reconstruction aLas Vegas, NV, USAbSpringer VerlagcNov-Dec 2010 a339 - 3483 a

Algebraic methods for image reconstruction provide good solutions even if only few projections are available. However, they can create noisy images if the number of iterations or the computational time is limited. In this paper, we show how to decrease the effect of noise by using median filters during the iterations. We present an extensive study by applying filters of different sizes and in various times of the reconstruction process. Also, our test images are of different structural complexity. Our study concentrates on the ART and its discrete variant DART reconstruction methods.

1 aHantos, Norbert1 aBalázs, Péter1 aBebis, George1 aBoyle, Richard1 aParvin, Bahram1 aKoracin, Darko1 aChung, Ronald1 aHammound, Riad1 aHussain, Muhammad1 aKar-Han, Tan1 aCrawfis, Roger1 aThalmann, Daniel1 aKao, David1 aAvila, Lisa uhttps://www.inf.u-szeged.hu/en/publication/image-enhancement-by-median-filters-in-algebraic-reconstruction-methods-an-experimental-study00643nas a2200169 4500008004100000245007000041210006900111260004100180300001400221100002000235700001800255700002500273700002000298700002300318700001900341856011300360 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/en/publication/machine-learning-for-supporting-binary-tomographic-reconstruction00358nas a2200097 4500008004100000245005700041210005700098260005300155300000700208856004500215 2010 eng d00aMedian filtering in algebraic reconstruction methods0 aMedian filtering in algebraic reconstruction methods aSzeged, HungarybUniversity of SzegedcJune 2010 a36 uhttps://www.inf.u-szeged.hu/en/node/112200536nas a2200133 4500008004100000245006500041210006500106260005300171300000700224100002700231700002000258700001600278856010800294 2010 eng d00aObject rotation effects on binary tomographic reconstruction0 aObject rotation effects on binary tomographic reconstruction aSzeged, HungarybUniversity of SzegedcJune 2010 a761 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal uhttps://www.inf.u-szeged.hu/en/publication/object-rotation-effects-on-binary-tomographic-reconstruction00574nas a2200121 4500008004100000245009700041210006900138260005300207300000700260100002500267700002000292856014000312 2010 eng d00aObtaining geometrical properties of binary images from two projections using neural networks0 aObtaining geometrical properties of binary images from two proje aSzeged, HungarybUniversity of SzegedcJune 2010 a691 aTasi, Tamás Sámuel1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/obtaining-geometrical-properties-of-binary-images-from-two-projections-using-neural-networks00686nas a2200193 4500008004100000245006000041210006000101260005000161300001400211100002700225700002000252700001600272700002500288700001400313700002200327700001700349700002100366856010500387 2010 eng d00aProjection selection algorithms for discrete tomography0 aProjection selection algorithms for discrete tomography aSydney, Australia bSpringer VerlagcDec 2010 a390 - 4011 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aBlanc-Talon, Jacques1 aBone, Don1 aPhilips, Wilfried1 aPopescu, Dan1 aScheunders, Paul uhttps://www.inf.u-szeged.hu/en/publication/projection-selection-algorithms-for-discrete-tomography-000506nas a2200133 4500008004100000020001400041245007000055210006700125260002300192300001600215490000800231100002000239856011300259 2009 eng d a0166-218X00aA benchmark set for the reconstruction of hv-convex discrete sets0 abenchmark set for the reconstruction of hvconvex discrete sets bElseviercAug 2009 a3447 - 34560 v1571 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/a-benchmark-set-for-the-reconstruction-of-hv-convex-discrete-sets00386nas a2200097 4500008004100000245007400041210007400115260003300189300002100222856004500243 2009 hun d00aDöntési fákon alapuló előfeldolgozás a bináris tomográfiában0 aDöntési fákon alapuló előfeldolgozás a bináris tomográfiában aBudapestbAkaprintcJan 2009 anincs számozás uhttps://www.inf.u-szeged.hu/en/node/111701357nas a2200181 4500008004100000020002200041245008700063210006900150260004500219300001400264520065900278100002000937700001800957700002400975700002600999700002001025856013001045 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 a

In 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/en/publication/an-evolutionary-approach-for-object-based-image-reconstruction-using-learnt-priors00578nas a2200157 4500008004100000020001400041245008100055210006900136260000900205300001200214490000700226100001800233700002500251700002000276856012400296 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/en/publication/learning-connectedness-and-convexity-of-binary-images-from-their-projections00323nas a2200109 4500008004100000245004600041210004600087260001400133300000700147490001400154856004500168 2009 eng d00aNeutron tomography with prior information0 aNeutron tomography with prior information cJuly 2009 a380 vAbstracts uhttps://www.inf.u-szeged.hu/en/node/111800376nas a2200085 4500008004100000245010200041210006900143260003300212856004500245 2009 eng d00aReconstruction of binary images with disjoint components from horizontal and vertical projections0 aReconstruction of binary images with disjoint components from ho aBudapestbAkaprintcJan 2009 uhttps://www.inf.u-szeged.hu/en/node/111601310nas a2200157 4500008004100000020002200041245009700063210006900160260008200229300001400311520062200325100002000947700002200967700002300989856014001012 2009 eng d a978-3-642-10208-000aReconstruction of canonical hv-convex discrete sets from horizontal and vertical projections0 aReconstruction of canonical hvconvex discrete sets from horizont aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer VerlagcNov 2009 a280 - 2883 aThe problem of reconstructing some special hv-convex discretesets from their two orthogonal projections is considered. In general, the problem is known to be NP-hard, but it is solvable in polynomial time if the discrete set to be reconstructed is also 8-connected. In this paper, we define an intermediate class - the class of hv-convex canonical discrete sets - and give a constructive proof that the above problem remains computationally tractable for this class, too. We also discuss some further theoretical consequences and present experimental results as well. © Springer-Verlag Berlin Heidelberg 2009.

1 aBalázs, Péter1 aWiederhold, Petra1 aBarneva, Reneta, P uhttps://www.inf.u-szeged.hu/en/publication/reconstruction-of-canonical-hv-convex-discrete-sets-from-horizontal-and-vertical-projections01557nas a2200145 4500008004100000020001400041245009800055210006900153260004800222300001400270490000700284520095900291100002001250856014101270 2008 eng d a0324-721X00aOn the ambiguity of reconstructing hv-convex binary matrices with decomposable configurations0 aambiguity of reconstructing hvconvex binary matrices with decomp aSzeged, HungarybUniversity of Szegedc2008 a367 - 3770 v183 a`Reconstructing binary matrices from their row, column, diagonal, and antidiagonal sums (also called projections) plays a central role in discrete tomography. One of the main difficulties in this task is that in certain cases the projections do not uniquely determine the binary matrix. This can yield an extremely large number of (sometimes very different) solutions. This ambiguity can be reduced by having some prior knowledge about the matrix to be reconstructed. The main challenge here is to find classes of binary matrices where ambiguity is drastically reduced or even completely eliminated. The goal of this paper is to study the class of $hv$-convex matrices which have decomposable configurations from the viewpoint of ambiguity. First, we give a negative result in the case of three projections. Then, we present a heuristic for the reconstruction using four projections and analyze its performance in quality and running time.`

In 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. ` `

We present a general framework for reconstructing binary images with disjoint components from the horizontal and vertical projections. We develop a backtracking algorithm that works for binary images having components from an arbitrary class. Thus, a priori knowledge about the components of the image to be reconstructed can be incorporated into the reconstruction process. In addition, we show how to extend the algorithm to obtain a branch-and-bound scheme useful to reconstruct images satisfying some further properties (for example similarity to a model image) as much as possible. Experimental results are also presented.

1 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/discrete-tomographic-reconstruction-of-binary-images-with-disjoint-components-using-shape-information00576nas a2200133 4500008004100000020001400041245010600055210006900161260002300230300001200253490000800265100002000273856014900293 2008 eng d a0304-397500aA framework for generating some discrete sets with disjoint components by using uniform distributions0 aframework for generating some discrete sets with disjoint compon bElseviercOct 2008 a15 - 230 v4061 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/a-framework-for-generating-some-discrete-sets-with-disjoint-components-by-using-uniform-distributions01248nas a2200097 4500008004100000245006200041210006000103260005800163520086900221856006001090 2008 eng d00aA képfeldolgozás kutatása a Szegedi Tudományegyetemen0 aképfeldolgozás kutatása a Szegedi Tudományegyetemen aDebrecenbDebreceni Egyetem Informatikai Karc2008///3 aA digitális képfeldolgozás kutatásának a Szegedi TudományegyetemTermészettudományi és Informatikai Karán, az Informatikai Tanszékcsoport Képfeldolgozás és Számítógépes Grafika Tanszékén közel négy évtizedes hagyománya van. A Tanszék valamennyi munkatársa nemzetközileg elismert kutatómunkát folytat, melyet már több száz rangos publikáció fémjelez. Számos, a képfeldolgozás kutatásában vezető egyetemmel és kutatóintézettel építettünk ki szoros kapcsolatot és folytattunk eredményes kutatómunkát, aktív résztvevői vagyunk a hazai és a nemzetközi tudományos közéletnek. A legfontosabb, jelenleg is folyó kutatásaink: orvosi képek feldolgozása, diszkrét tomográfia, képszegmentálás, térinformatika, távérzékelés, képregisztráció, vázkijelölés, műtéti tervezés. uhttp://www.agr.unideb.hu/if2008/kiadvany/papers/E62.pdf01433nas a2200169 4500008004100000020002200041245004500063210003700108260004800145300001400193520087500207100002001082700002501102700002301127700002501150856008801175 2008 eng d a978-3-540-78274-200aOn the number of hv-convex discrete sets0 anumber of hvconvex discrete sets aBuffalo, NY, USAbSpringer VerlagcApr 2008 a112 - 1233 a

One of the basic problems in discrete tomography is thereconstruction of discrete sets from few projections. Assuming that the set to be reconstructed fulfills some geometrical properties is a commonly used technique to reduce the number of possibly many different solutions of the same reconstruction problem. The class of hv-convex discrete sets and its subclasses have a well-developed theory. Several reconstruction algorithms as well as some complexity results are known for those classes. The key to achieve polynomial-time reconstruction of an hv- convex discrete set is to have the additional assumption that the set is connected as well. This paper collects several statistics on hv-convex discrete sets, which are of great importance in the analysis of algorithms for reconstructing such kind of discrete sets. © 2008 Springer-Verlag Berlin Heidelberg.

1 aBalázs, Péter1 aBrimkov, Valentin, E1 aBarneva, Reneta, P1 aHauptman, Herbert, A uhttps://www.inf.u-szeged.hu/en/publication/on-the-number-of-hv-convex-discrete-sets01563nas a2200289 4500008004100000020002200041022001400063245008600077210006900163260005000232300001600282520059600298100002000894700001800914700001900932700001900951700001900970700002100989700001901010700001801029700002101047700001601068700001701084700002501101700001801126856012901144 2008 eng d a978-3-540-89645-6 a0302-974300aReconstruction of binary images with few disjoint components from two projections0 aReconstruction of binary images with few disjoint components fro aLas Vegas, NV, USAbSpringer VerlagcDec 2008 a1147 - 11563 aWe present a general framework for reconstructing binary imageswith few disjoint components from the horizontal and vertical projections. We develop a backtracking algorithm that works for binary images having components from an arbitrary class. Thus, a priori information about the components of the image to be reconstructed can be incorporated into the reconstruction process. In addition, we can keep control over the number of components which can increase the speed and accuracy of the reconstruction. Experimental results are also presented. © 2008 Springer Berlin Heidelberg.

1 aBalázs, Péter1 aBebis, George1 aBoyle, Richard1 aParvin, Bahram1 aKoracin, Darko1 aRemagnino, Paolo1 aPorikli, Fatih1 aPeters, Jörg1 aKlosowski, James1 aArns, Laura1 aChun, Yu, Ka1 aRhyne, Theresa-Marie1 aMonroe, Laura uhttps://www.inf.u-szeged.hu/en/publication/reconstruction-of-binary-images-with-few-disjoint-components-from-two-projections00490nas a2200097 4500008004100000245008600041210006900127260004800196100002000244856012800264 2007 eng d00aBinary Tomography Using Geometrical Priors: Uniqueness and Reconstruction Results0 aBinary Tomography Using Geometrical Priors Uniqueness and Recons aSzeged, HungarybUniversity of Szegedc20071 aBalázs, Péter uhttps://www.inf.u-szeged.hu/en/publication/binary-tomography-using-geometrical-priors-uniqueness-and-reconstruction-results01512nas a2200157 4500008004100000020002200041245008700063210006900150260003900219300001400258520089400272100002001166700002101186700001701207856013001224 2007 eng d a978-0-8176-3614-200aDecomposition Algorithms for Reconstructing Discrete Sets with Disjoint Components0 aDecomposition Algorithms for Reconstructing Discrete Sets with D aCambridgebBirkhauser Bostonc2007 a153 - 1733 aThe reconstruction of discrete sets from their projections is a frequently studied field in discrete tomography with applications in electron microscopy, image processing, radiology, and so on. Several efficient reconstruction algorithms have been developed for certain classes of discrete sets having some good geometrical properties. On the other hand, it has been shown that the reconstruction under certain circumstances can be very time-consuming, even NP-hard. In this chapter we show how prior information that the set to be reconstructed consists of several components can be exploited in order to facilitate the reconstruction. We present some general techniques to decompose a discrete set into components knowing only its projections and thus reduce the reconstruction of a general discrete set to the reconstruction of single components, which is usually a simpler task.

1 aBalázs, Péter1 aHerman, Gábor T1 aKuba, Attila uhttps://www.inf.u-szeged.hu/en/publication/decomposition-algorithms-for-reconstructing-discrete-sets-with-disjoint-components01196nas a2200145 4500008004100000020001400041245008500055210006900140260002300209300001600232490000700248520064700255100002000902856012800922 2007 eng d a0262-885600aA decomposition technique for reconstructing discrete sets from four projections0 adecomposition technique for reconstructing discrete sets from fo bElseviercOct 2007 a1609 - 16190 v253 aThe reconstruction of discrete sets from four projections is in general an NP-hard problem. In this paper we study the class of decomposable discrete sets and give an efficient reconstruction algorithm for this class using four projections. It is also shown that an arbitrary discrete set which is Q-convex along the horizontal and vertical directions and consists of several components is decomposable. As a consequence of decomposability we get that in a subclass of *hv*-convex discrete sets the reconstruction from four projections can also be solved in polynomial time. Possible extensions of our method are also discussed.

One of the basic problems in discrete tomography is thereconstruction of discrete sets from few projections. Assuming that the set to be reconstructed fulfils some geometrical properties is a commonly used technique to reduce the number of possibly many different solutions of the same reconstruction problem. Since the reconstruction from two projections in the class of so-called hv-convex sets is NP-hard this class is suitable to test the efficiency of newly developed reconstruction algorithms. However, until now no method was known to generate sets of this class from uniform random distribution and thus only ad hoc comparison of several reconstruction techniques was possible. In this paper we first describe a method to generate some special hv-convex discrete sets from uniform random distribution. Moreover, we show that the developed generation technique can easily be adapted to other classes of discrete sets, even for the whole class of hv- convexes. Several statistics are also presented which are of great importance in the analysis of algorithms for reconstructing hv-convex sets. © Springer-Verlag Berlin Heidelberg 2007.

1 aBalázs, Péter1 aErsbøll, Bjarne, Kjær1 aPedersen, Kim, Steenstrup uhttps://www.inf.u-szeged.hu/en/publication/generation-and-empirical-investigation-of-hv-convex-discrete-sets01748nas a2200181 4500008004100000020002300041245007900064210006900143260003700212300001400249520109300263100002001356700001401376700001601390700001701406700002101423856012201444 2007 eng d a978-953-184-116-0 00aReconstructing some hv-convex binary images from three or four projections0 aReconstructing some hvconvex binary images from three or four pr aIstanbul, TurkeybIEEEcSep 2007 a136 - 1403 aThe reconstruction of binary images from their projections is animportant problem in discrete tomography. The main challenge in this task is that in certain cases the projections do not uniquely determine the binary image. This can yield an extremely large number of (sometimes very different) solutions. Moreover, under certain circumstances the reconstruction becomes NP-hard. A commonly used technique to reduce ambiguity and to avoid intractability is to suppose that the image to be reconstructed arises from a certain class of images having some geometrical properties. This paper studies the reconstruction problem in the class of hv-convex images having their components in so-called decomposable configurations. First, we give a negative result showing that there can be exponentially many images of the above class having the same three projections. Then, we present a heuristic that uses four projections to reconstruct an hv-convex image with decomposable configuration. We also analyze the performance of our heuristic from the viewpoints of accuracy and running time.

1 aBalázs, Péter1 aPetrou, M1 aSaramaki, T1 aErcil, Aytul1 aLončarić, Sven uhttps://www.inf.u-szeged.hu/en/publication/reconstructing-some-hv-convex-binary-images-from-three-or-four-projections00368nas a2200097 4500008004100000245005000041210004900091260007300140300001200213856004500225 2007 eng d00aUniform generation of hv-convex discrete sets0 aUniform generation of hvconvex discrete sets aDebrecenbKépfeldolgozók és Alakfelismerők TársaságacJan 2007 a63 - 70 uhttps://www.inf.u-szeged.hu/en/node/111400397nas a2200109 4500008004100000245007800041210006900119260001400188300000700202490003300209856004500242 2006 eng d00aOn the ambiguity of reconstructing decomposable hv-convex binary matrices0 aambiguity of reconstructing decomposable hvconvex binary matrice cJune 2006 a170 vVolume of Extenden Abstracts uhttps://www.inf.u-szeged.hu/en/node/111300948nas a2200109 4500008004100000245009000041210006900131260004900200300001200249520053200261856004500793 2006 eng d00aThe number of line-convex directed polyominoes having the same orthogonal projections0 anumber of lineconvex directed polyominoes having the same orthog aBerlin, HeidelbergbSpringer-Verlagc2006/// a77 - 853 aThe number of line-convex directed polyominoes with givenhorizontal and vertical projections is studied. It is proven that diagonally convex directed polyominoes are uniquely determined by their orthogonal projections. The proof of this result is algorithmical. As a counterpart, we show that ambiguity can be exponential if antidiagonal convexity is assumed about the polyomino. Then, the results are generalised to polyominoes having convexity property along arbitrary lines. © Springer-Verlag Berlin Heidelberg 2006.

uhttps://www.inf.u-szeged.hu/en/node/114000403nas a2200121 4500008004100000020001400041245007800055210006900133260001200202300001400214490000800228856004500236 2005 eng d a0166-218X00aReconstruction of 8-connected but not 4-connected hv-convex discrete sets0 aReconstruction of 8connected but not 4connected hvconvex discret c2005/// a149 - 1680 v147 uhttps://www.inf.u-szeged.hu/en/node/110000939nas a2200109 4500008004100000245007100041210006900112260008100181300001400262520050800276856004500784 2005 eng d00aReconstruction of decomposable discrete sets from four projections0 aReconstruction of decomposable discrete sets from four projectio aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer Verlagc2005/// a104 - 1143 aIn this paper we introduce the class of decomposable discretesets and give a polynomial algorithm for reconstructing discrete sets of this class from four projections. It is also shown that the class of decomposable discrete sets is more general than the class S′8 of hv-convex 8-but not 4-connected discrete sets which was studied in [3]. As a consequence we also get that the reconstruction from four projections in S′8can be solved in O(mn) time. © Springer-Verlag Berlin Heidelberg 2005.

uhttps://www.inf.u-szeged.hu/en/node/114100406nas a2200121 4500008004100000020001400041245008200055210006900137260001200206300001400218490000700232856004500239 2005 eng d a1571-065300aReconstruction of discrete sets from four projections: strong decomposability0 aReconstruction of discrete sets from four projections strong dec c2005/// a329 - 3450 v20 uhttps://www.inf.u-szeged.hu/en/node/110900397nas a2200109 4500008004100000245007800041210006900119260001400188300000700202490003300209856004500242 2004 eng d00aReconstruction of discrete sets from four projections: Decomposable cases0 aReconstruction of discrete sets from four projections Decomposab cJuly 2004 a220 vVolume of Extended Abstracts uhttps://www.inf.u-szeged.hu/en/node/111200444nas a2200097 4500008004100000245009600041210006900137260008100206300001400287856004500301 2003 eng d00aA fast algorithm for reconstructing hv-convex 8-connected but not 4-connected discrete sets0 afast algorithm for reconstructing hvconvex 8connected but not 4c aBerlin; Heidelberg; New York; London; Paris; TokyobSpringer Verlagc2003/// a388 - 397 uhttps://www.inf.u-szeged.hu/en/node/110800415nas a2200109 4500008004100000245009600041210006900137260001400206300000700220490003300227856004500260 2002 eng d00aA fast algorithm for reconstructing hv-convex 8-connected but not 4-connected discrete sets0 afast algorithm for reconstructing hvconvex 8connected but not 4c cJuly 2002 a190 vVolume of Extended Abstracts uhttps://www.inf.u-szeged.hu/en/node/1111