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Selected Publications of the Department of Image Processing and Computer Graphics of the year 2008

Articles in journal or book chapters

1. Erich Sorantin, Emese Balogh, Anna Vilanova i Bartroli, Kalman Palagyi, Laszlo G. Nyul, Franz Lindbichler, and Andrea Ruppert. Techniques of Virtual Dissection of the Colon Based on Spiral CT Data. In Emanuele Neri, Davide Caramella, and Carlo Bartolozzi, editors, Image Processing in Radiology: Current Applications, Medical Radiology, chapter 18, pages 257-268. Springer Verlag, Berlin, Heidelberg, 2008. [doi:10.1007/978-3-540-49830-8_18]
   Abstract: Colorectal cancer represents the third most commonly diagnosed cancer and is the second leading cause of cancer deaths in the United States (Gazelle et al. 2000). In addition, colorectal cancer is responsible for about 11% of all new cancer cases per year (Gazelle et al. 2000). Five-year prognosis is about 90% for patients with localized disease compared to 60% if there is a regional spread and a drop to 10% in patients with distant metastasis (Gazelle et al. 2000). In the field of medicine there is a widely accepted opinion that most colorectal cancers arise from pre-existent adenomatous polyps (Johnson 2000). Therefore, different societies, such as the American Cancer Society, have proposed screening for colorectal cancer (Byers et al. 1997; Winawer et al. 1997). Today, different options exist for detection of colorectal cancer, including digital rectal examination, fecal occult blood testing, flexible and rigid sigmoidoscopy, barium enema and its variants, colonoscopy and recently computed tomography or magnetic resonance-based virtual colonography (Gazelle et al. 2000).
   
   @INCOLLECTION{SorantinIPR2008,
   AUTHOR = {Erich Sorantin and Emese Balogh and Vilanova i Bartroli, Anna and Kalman Palagyi and Laszlo G. Nyul and Franz Lindbichler and Andrea Ruppert},
   BOOKTITLE = {Image Processing in Radiology: Current Applications},
   PUBLISHER = {Springer Verlag},
   TITLE = {Techniques of Virtual Dissection of the Colon Based on Spiral CT Data},
   YEAR = {2008},
   ADDRESS = {Berlin, Heidelberg},
   CHAPTER = {18},
   EDITOR = {Emanuele Neri and Davide Caramella and Carlo Bartolozzi},
   PAGES = {257-268},
   SERIES = {Medical Radiology},
   DOI = {10.1007/978-3-540-49830-8_18},
   }


2. Peter Balazs. A framework for generating some discrete sets with disjoint components by using uniform distributions. Theoretical Computer Science, 406:15-23, 2008. [PDF]
   Abstract: Discrete tomography deals with the reconstruction of discrete sets from few projections. Assuming that the set to be reconstructed belongs to a certain class of discrete sets with some geometrical properties is a commonly used technique to reduce the number of possibly many different solutions of the same reconstruction problem. The average performance of reconstruction algorithms are often tested on such classes by choosing elements of a given class from uniform random distributions. This paper presents a general framework for generating discrete sets with disjoint connected components using uniform distributions. Especially, the uniform random generation of $hv$-convex discrete sets and Q-convex discrete sets according to the size of the minimal bounding rectangle are discussed.
   
   @ARTICLE{Balazs2008d,
   AUTHOR = {Peter Balazs},
   JOURNAL = {Theoretical Computer Science},
   TITLE = {A framework for generating some discrete sets with disjoint components by using uniform distributions},
   YEAR = {2008},
   PAGES = {15-23},
   VOLUME = {406},
   }


3. Peter Balazs. Discrete tomographic reconstruction of binary images with disjoint components using shape information. International Journal of Shape Modeling, 14:2:189-207, 2008. [PDF]
   Abstract: 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.
   
   @ARTICLE{Balazs2008ws,
   AUTHOR = {Peter Balazs},
   JOURNAL = {International Journal of Shape Modeling},
   TITLE = {Discrete tomographic reconstruction of binary images with disjoint components using shape information},
   YEAR = {2008},
   PAGES = {189-207},
   VOLUME = {14:2},
   PUBLISHER = {World Scientific},
   }


4. Peter Balazs. On the Ambiguity of Reconstructing hv-Convex Binary Matrices with Decomposable Configurations. Acta Cybernetica, 18(3):367-377, 2008. [PDF] Keyword(s): discrete tomography, hv-convex binary matrix, decomposable configuration, reconstruction algorithm.
   Abstract: 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.
   
   @ARTICLE{Balazs:2008:ActaCybernetica,
   AUTHOR = {Peter Balazs},
   JOURNAL = {Acta Cybernetica},
   TITLE = {On the Ambiguity of Reconstructing hv-Convex Binary Matrices with Decomposable Configurations},
   YEAR = {2008},
   NUMBER = {3},
   PAGES = {367--377},
   VOLUME = {18},
   KEYWORDS = {discrete tomography, hv-convex binary matrix, decomposable configuration, reconstruction algorithm},
   }


5. Gyorgy Bekes, Eors Mate, Laszlo G. Nyul, Attila Kuba, and Marta Fidrich. Geometrical model-based segmentation of the organs of sight on CT images. Medical Physics, 35(2):735-743, February 2008. [WWW] [PDF] [doi:10.1118/1.2826557] Keyword(s): biological tissues, computerised tomography, eye, image segmentation, medical image processing, neurophysiology, radiation therapy.
   Abstract: Segmentation of organs of sight such as the eyeballs, lenses, and optic nerves is a time consuming task for clinicians. The small size of the organs and the similar density of the surrounding tissues make the segmentation difficult. We developed a new algorithm to segment these organs with minimal user interaction. The algorithm needs only three seed points to fit an initial geometrical model to start an effective segmentation. The clinical evaluation shows that the output of our method is useful in clinical practice.
   
   @ARTICLE{bekes:735,
   AUTHOR = {Gyorgy Bekes and Eors Mate and Laszlo G. Nyul and Attila Kuba and Marta Fidrich},
   JOURNAL = {Medical Physics},
   TITLE = {Geometrical model-based segmentation of the organs of sight on CT images},
   YEAR = {2008},
   MONTH = {February},
   NUMBER = {2},
   PAGES = {735-743},
   VOLUME = {35},
   KEYWORDS = {biological tissues, computerised tomography, eye, image segmentation, medical image processing, neurophysiology, radiation therapy},
   PUBLISHER = {American Association of Physicists in Medicine},
   URL = {http://link.aip.org/link/?MPH/35/735/1},
   DOI = {10.1118/1.2826557},
   }


6. Zoltan Kato. Segmentation of Color Images via Reversible Jump MCMC Sampling. Image and Vision Computing, 26(3):361-371, March 2008. [PDF] [doi:10.1016/j.imavis.2006.12.004]
   Abstract: Reversible jump Markov chain Monte Carlo (RJMCMC) is a recent method which makes it possible to construct reversible Markov chain samplers that jump between parameter subspaces of different dimensionality. In this paper, we propose a new RJMCMC sampler for multivariate Gaussian mixture identification and we apply it to color image segmentation. For this purpose, we consider a first order Markov random field (MRF) model where the singleton energies derive from a multivariate Gaussian distribution and second order potentials favor similar classes in neighboring pixels. The proposed algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The estimation is done according to the Maximum A Posteriori (MAP) criterion. The algorithm has been validated on a database of real images with human segmented ground truth.
   
   @ARTICLE{Kato2007,
   AUTHOR = {Zoltan Kato},
   JOURNAL = {Image and Vision Computing},
   TITLE = {Segmentation of Color Images via Reversible Jump MCMC Sampling},
   YEAR = {2008},
   MONTH = {March},
   NUMBER = {3},
   PAGES = {361--371},
   VOLUME = {26},
   PUBLISHER = {Elsevier},
   DOI = {10.1016/j.imavis.2006.12.004},
   }


7. Kalman Palagyi. A 3D fully parallel surface-thinning algorithm. Theoretical Computer Science, 406:119-135, 2008. [PDF] [doi:10.1016/j.tcs.2008.06.041]
   Abstract: The thinning is an iterative layer by layer erosion until only the ``skeletons'' of the objects are left. This paper presents a thinning algorithm for extracting medial surfaces from 3D binary pictures. The strategy which is used is called fully parallel, which means that the same parallel operator is applied at each iteration. An efficient implementation of the proposed algorithm on conventional sequential computers is given and the topological correctness for (26, 6) binary pictures is proved.
   
   @ARTICLE{PalagyiTCS2008,
   AUTHOR = {Kalman Palagyi},
   JOURNAL = {Theoretical Computer Science},
   TITLE = {A 3D fully parallel surface-thinning algorithm},
   YEAR = {2008},
   PAGES = {119-135},
   VOLUME = {406},
   DOI = {10.1016/j.tcs.2008.06.041},
   }

Conference articles

1. Marton Balasko, Attila Kuba, Attila Tanacs, Zoltan Kiss, Antal Nagy, and Burkhard Schillinger. Comparison Radiography and Tomography Possibilities of FRM-II (20 MW) and Budapest (10 MW) Research Reactor. In Muhammed Arif, editor, Proceedings of the Eight World Conference WCNR-8, pages 18-27, October 2008. [PDF]
   Abstract: In November of 2005 a measurement series was conducted on different kinds of reference objects to compare the results of the FRM-II (ANTARES) and the Budapest (Radiography Station) research reactor in the field of neutron radiography, gamma radiography, classical tomography and discrete tomography by an ANDOR CCD camera and Imaging plate with BAS 2500 Scanner (contributed by AIDA software).
   
   @INPROCEEDINGS{BalaskoKuba:2008:Comparison,
   AUTHOR = {Marton Balasko and Attila Kuba and Attila Tanacs and Zoltan Kiss and Antal Nagy and Burkhard Schillinger},
   BOOKTITLE = {Proceedings of the Eight World Conference WCNR-8},
   TITLE = {Comparison Radiography and Tomography Possibilities of FRM-II (20 MW) and Budapest (10 MW) Research Reactor},
   YEAR = {2008},
   EDITOR = {Muhammed Arif},
   MONTH = {October},
   PAGES = {18--27},
   }


2. Marton Balasko, Erzsebet Svab, Zoltan Kiss, Attila Tanacs, Antal Nagy, and Attila Kuba. Study of the Inner Structure of a Damaged Control Rod by Neutron and X-ray Radiography and Discrete Tomography. In Muhammed Arif, editor, Proceedings of the Eight World Conference WCNR-8, pages 294-303, October 2008. [PDF]
   Abstract: A control rod K5 of the 10 MW Budapest Research Reactor was damaged in 2004. We have applied both neutron and X-ray radiography to study the distribution of the residual material in the damaged K5. X-ray radiography visualized the main distribution of the residual materials, like different kinds of B4C and water, and the frozen gas bubbles, while neutron radiography displayed the fine structure of the congealed B4C. The 3D reconstruction of the most interesting parts of K5 has been done by discrete tomography.
   
   @INPROCEEDINGS{BalaskoKiss:2008:Study,
   AUTHOR = {Marton Balasko and Erzsebet Svab and Zoltan Kiss and Attila Tanacs and Antal Nagy and Attila Kuba},
   BOOKTITLE = {Proceedings of the Eight World Conference WCNR-8},
   TITLE = {Study of the Inner Structure of a Damaged Control Rod by Neutron and X-ray Radiography and Discrete Tomography},
   YEAR = {2008},
   EDITOR = {Muhammed Arif},
   MONTH = {October},
   PAGES = {294--303},
   }


3. Peter Balazs. On the number hv-convex discrete sets. In Valentin E. Brimkov, Reneta P. Barneva, and Herbert A. Hauptman, editors, Proceedings of the International Workshop on Combinatorial Image Analysis, volume 4958 of Lecture Notes in Computer Science, Buffalo, USA, pages 112-123, April 2008. Springer Verlag. [PDF]
   Abstract: One of the basic problems in discrete tomography is the reconstruction 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.
   
   @INPROCEEDINGS{Balazs2008a,
   AUTHOR = {Peter Balazs},
   BOOKTITLE = {Proceedings of the International Workshop on Combinatorial Image Analysis},
   TITLE = {On the number hv-convex discrete sets},
   YEAR = {2008},
   ADDRESS = {Buffalo, USA},
   EDITOR = {Valentin E. Brimkov and Reneta P. Barneva and Herbert A. Hauptman},
   MONTH = {April},
   PAGES = {112--123},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {4958},
   }


4. Peter Balazs. Reconstruction of binary images with few disjoint components from two projections. In George Bebis et al., editor, Proceedings of the International Symposium on Visual Computing, volume 5359 of Lecture Notes in Computer Science, Las Vegas, NV, USA, pages 1147-1156, December 2008. Springer Verlag. [PDF]
   Abstract: We present a general framework for reconstructing binary images with 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.
   
   @INPROCEEDINGS{Balazs2008c,
   AUTHOR = {Peter Balazs},
   BOOKTITLE = {Proceedings of the International Symposium on Visual Computing},
   TITLE = {Reconstruction of binary images with few disjoint components from two projections},
   YEAR = {2008},
   ADDRESS = {Las Vegas, NV, USA},
   EDITOR = {et al., George Bebis},
   MONTH = {December},
   PAGES = {1147-1156},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {5359},
   }


5. Peter Balazs and Mihaly Gara. Decision trees in binary tomography for supporting the reconstruction of hv-convex connected images. In S. Bourennane, W. Philips, D. Popescu, and P. Scheunders, editors, Proceedings of the Advanced Concepts for Intelligent Vision Systems, volume 5259 of Lecture Notes in Computer Science, Juan-les-Pins, France, pages 433-443, October 2008. Springer Verlag. [PDF]
   Abstract: 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.
   
   @INPROCEEDINGS{BalazsGara2008,
   AUTHOR = {Peter Balazs and Mihaly Gara},
   BOOKTITLE = {Proceedings of the Advanced Concepts for Intelligent Vision Systems},
   TITLE = {Decision trees in binary tomography for supporting the reconstruction of hv-convex connected images},
   YEAR = {2008},
   ADDRESS = {Juan-les-Pins, France},
   EDITOR = {S. Bourennane and W. Philips and D. Popescu and P. Scheunders},
   MONTH = {October},
   PAGES = {433-443},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {5259},
   }


6. Csaba Domokos and Zoltan Kato. Binary Image Registration Using Covariant Gaussian Densities. In A. Campilho and M. Kamel, editors, Proceedings of the International Conference on Image Analysis and Recognition, volume 5112 of Lecture Notes in Computer Science, Povoa de Varzim, Portugal, pages 455-464, June 2008. Springer Verlag. [PDF]
   Abstract: We consider the estimation of 2D affine transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. In this paper, we propose a novel approach where the exact transformation is obtained as a least-squares solution of a linear system. The basic idea is to fit a Gaussian density to the shapes which preserves the effect of the unknown transformation. It can also be regarded as a consistent coloring of the shapes yielding two rich functions defined over the two shapes to be matched. The advantage of the proposed solution is that it is fast, easy to implement, works without established correspondences and provides a unique and exact solution regardless of the magnitude of transformation.
   
   @INPROCEEDINGS{Domokos-Kato2008,
   AUTHOR = {Csaba Domokos and Zoltan Kato},
   BOOKTITLE = {Proceedings of the International Conference on Image Analysis and Recognition},
   TITLE = {Binary Image Registration Using Covariant Gaussian Densities},
   YEAR = {2008},
   ADDRESS = {Povoa de Varzim, Portugal},
   EDITOR = {A. Campilho and M. Kamel},
   MONTH = {June},
   PAGES = {455--464},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {5112},
   }


7. Csaba Domokos, Zoltan Kato, and Joseph M. Francos. Parametric Estimation of Affine Deformations of Binary Images. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Las Vegas, Nevada, USA, pages 889-892, April 2008. IEEE, IEEE. [PDF] [doi:10.1109/ICASSP.2008.4517753]
   Abstract: We consider the problem of planar object registration on binary images where the aligning transformation is restricted to the group of {\em affine} transformations. Previous approaches usually require established correspondences or the solution of nonlinear optimization problems. Herein we show that it is possible to formulate the problem as the solution of a system of up to third order polynomial equations. These equations are constructed in a simple way using some basic geometric information of {\em binary} images. It does not need established correspondences nor the solution of complex optimization problems. The resulting algorithm is fast and provides a direct solution regardless of the magnitude of transformation.
   
   @INPROCEEDINGS{Domokos-etal2008,
   AUTHOR = {Csaba Domokos and Zoltan Kato and Joseph M. Francos},
   BOOKTITLE = {Proceedings of the International Conference on Acoustics, Speech and Signal Processing},
   TITLE = {Parametric Estimation of Affine Deformations of Binary Images},
   YEAR = {2008},
   ADDRESS = {Las Vegas, Nevada, USA},
   MONTH = {April},
   ORGANIZATION = {IEEE},
   PAGES = {889--892},
   PUBLISHER = {IEEE},
   DOI = {10.1109/ICASSP.2008.4517753},
   }


8. Attila Fazekas, Kalman Palagyi, Gyorgy Kovacs, and Gabor Nemeth. Skeletonization Based on Metrical Neighborhood Sequences. In Antonios Gasteratos, Markus Vincze, and John K. Tsotsos, editors, Proceedings of the International Conference on Computer Vision Systems, volume 5008 of Lecture Notes in Computer Science, Santorini, Greece, pages 333-342, May 2008. Springer Verlag. [PDF]
   Abstract: Skeleton is a shape descriptor which summarizes the general form of objects. It can be expressed in terms of the fundamental morphological operations. The limitation of that characterization is that its construction based on digital disks such that cannot provide good approximation to the Euclidean disks. In this paper we define a new type of skeleton based on neighborhood sequences that is much closer to the Euclidean skeleton. A novel method for quantitative comparison of skeletonization algorithms is also proposed.
   
   @INPROCEEDINGS{FazekasEtalICVS2008,
   AUTHOR = {Attila Fazekas and Kalman Palagyi and Gyorgy Kovacs and Gabor Nemeth},
   BOOKTITLE = {Proceedings of the International Conference on Computer Vision Systems},
   TITLE = {Skeletonization Based on Metrical Neighborhood Sequences},
   YEAR = {2008},
   ADDRESS = {Santorini, Greece},
   EDITOR = {Antonios Gasteratos and Markus Vincze and John K. Tsotsos},
   MONTH = {May},
   PAGES = {333-342},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {5008},
   }

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