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Selected Publications of the Department of Image Processing and Computer Graphics of the year 1999

Books and proceedings

1. Gabor T. Herman and Attila Kuba, editors. Discrete Tomography: Foundations, Algorithms, and Applications, Applied and Numerical Harmonic Analysis. Birkhauser, Boston, December 1999. [WWW]
   @BOOK{Herman1999,
   PUBLISHER = {Birkhauser},
   TITLE = {Discrete Tomography: Foundations, Algorithms, and Applications},
   YEAR = {1999},
   EDITOR = {Gabor T. Herman and Attila Kuba},
   ADDRESS = {Boston},
   MONTH = {December},
   SERIES = {Applied and Numerical Harmonic Analysis},
   PAGES = {479},
   URL = {http://www.springer.com/west/home/birkhauser/computer+science?SGWID=4-40353-22-1598657-0},
   }
   


2. Attila Kuba, Martin Samal, and Andrew Todd-Pokropek, editors. Information Processing in Medical Imaging: 16th International Conference, IPMI'99, Visegrad, Hungary, June/July 1999. Proceedings, volume 1613 of Lecture Notes in Computer Science. Springer Verlag, 1999. [WWW]
   Abstract: This book constitutes the refereed proceedings of the 16th International Conference on Information Processing in Medical Imaging, IPMI'99, held in Visegrad, Hungary in June/July 1999. The 24 revised full papers and the 28 posters presented have been carefully reviewed and selected from a total of 82 submissions. The volume addresses the full range of current topics in the area in particular new imaging techniques, 3D ultrasound and PET, segmentation, image analysis of the brain cortex, registration, feature, detection and modelling, cardiovascular image analysis, shape modelling and analysis, segmentation and detection, measurement and quantitative analysis, and analysis of image sequences and functional imaging.
   @BOOK{Kuba1999,
   PUBLISHER = {Springer Verlag},
   TITLE = {Information Processing in Medical Imaging: 16th International Conference, IPMI'99, Visegrad, Hungary, June/July 1999. Proceedings},
   YEAR = {1999},
   EDITOR = {Attila Kuba and Martin Samal and Andrew Todd-Pokropek},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1613},
   PAGES = {508},
   URL = {http://www.springerlink.com/content/53dvm6qnje36/},
   }
   

Articles in journal or book chapters

1. Attila Kuba. Reconstruction of two-valued functions and matrices. In Gabor T. Herman and Attila Kuba, editors, Discrete Tomography: Foundations, Algorithms, and Applications, Applied and Numerical Harmonic Analysis, pages 133-158. Birkhauser, Boston, 1999.
   @INCOLLECTION{Kubabirk1999,
   AUTHOR = {Attila Kuba},
   BOOKTITLE = {Discrete Tomography: Foundations, Algorithms, and Applications},
   PUBLISHER = {Birkhauser},
   TITLE = {Reconstruction of two-valued functions and matrices},
   YEAR = {1999},
   ADDRESS = {Boston},
   EDITOR = {Gabor T. Herman and Attila Kuba},
   PAGES = {133-158},
   SERIES = {Applied and Numerical Harmonic Analysis},
   }
   


2. Attila Kuba and Gabor T. Herman. Discrete Tomography: A historical overview. In Gabor T. Herman and Attila Kuba, editors, Discrete Tomography: Foundations, Algorithms, and Applications, Applied and Numerical Harmonic Analysis, chapter 1, pages 1-30. Birkhauser, Boston, 1999.
   @INCOLLECTION{KubaHerman19991,
   AUTHOR = {Attila Kuba and Gabor T. Herman},
   BOOKTITLE = {Discrete Tomography: Foundations, Algorithms, and Applications},
   PUBLISHER = {Birkhauser},
   TITLE = {Discrete Tomography: A historical overview},
   YEAR = {1999},
   ADDRESS = {Boston},
   CHAPTER = {1},
   EDITOR = {Gabor T. Herman and Attila Kuba},
   PAGES = {1-30},
   SERIES = {Applied and Numerical Harmonic Analysis},
   }
   


3. Zoltan Kato, Josiane Zerubia, and Mark Berthod. Unsupervised Parallel Image Classification Using Markovian Models. Pattern Recognition, 32(4):591-604, April 1999. [PDF] Keyword(s): Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification.
   Abstract: This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation-maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images.
   @ARTICLE{Kato-etal99,
   AUTHOR = {Zoltan Kato and Josiane Zerubia and Mark Berthod},
   JOURNAL = {Pattern Recognition},
   TITLE = {Unsupervised Parallel Image Classification Using Markovian Models},
   YEAR = {1999},
   MONTH = {April},
   NUMBER = {4},
   PAGES = {591--604},
   VOLUME = {32},
   KEYWORDS = {Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification},
   }
   


4. Laszlo G. Nyul and Jayaram K. Udupa. On Standardizing the MR Image Intensity Scale. Magnetic Resonance in Medicine, 42(6):1072-1081, December 1999. [PDF]
   Abstract: The lack of a standard image intensity scale in MRI causes many difficulties in image display and analysis. A two-step postprocessing method is proposed for standardizing the intensity scale in such a way that for the same MR protocol and body region, similar intensities will have similar tissue meaning. In the first step, the parameters of the standardizing transformation are ``learned'' from a set of images. In the second step, for each MR study these parameters are used to map their histogram into the standardized histogram. The method was tested quantitatively on 90 whole-brain studies of multiple sclerosis patients for several protocols and qualitatively for several other protocols and body regions. Measurements using mean squared difference showed that the standardized image intensities have statistically significantly (P < 0.01) more consistent range and meaning than the originals. Fixed gray level windows can be established for the standardized images and used for display without the need of per case adjustment. Preliminary results also indicate that the method facilitates improving the degree of automation of image segmentation.
   @ARTICLE{Nyul:1999:OSM,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa},
   JOURNAL = {Magnetic Resonance in Medicine},
   TITLE = {On Standardizing the MR Image Intensity Scale},
   YEAR = {1999},
   MONTH = {December},
   NUMBER = {6},
   PAGES = {1072--1081},
   VOLUME = {42},
   }
   


5. Kalman Palagyi and Attila Kuba. A parallel 3D 12-subiteration thinning algorithm. Graphical Models and Image Processing, 61:199-221, 1999. [PDF]
   Abstract: Thinning on binary images is an iterative layer by layer erosion until only the "skeletons" of the objects are left. This paper presents an efficient parallel thinning algorithm which produces either curve skeletons or surface skeletons from 3D binary objects. It is important that a curve skeleton is extracted directly (i.e., without creating a surface skeleton). The strategy which is used is called directional: each iteration step is composed of a number of subiterations each of which can be executed in parallel. One iteration step of the proposed algorithm contains 12 subiterations instead of the usual six. The algorithm makes easy implementation possible, since deletable points are given by 3L3L3 matching templates. The topological correctness for (26,6) binary pictures is proved.
   @ARTICLE{PalagyiKubaGMIP1999,
   AUTHOR = {Kalman Palagyi and Attila Kuba},
   JOURNAL = {Graphical Models and Image Processing},
   TITLE = {A parallel 3D 12-subiteration thinning algorithm},
   YEAR = {1999},
   PAGES = {199-221},
   VOLUME = {61},
   }
   

Conference articles

1. Endre Katona and Gyorgy Hudra. An Interpretation System for Cadastral Maps. In Proceedings of the International Conference on Image Analysis and Processing, Venice, Italy, pages 792-797, September 1999. IEEE. [PDF]
   Abstract: To create a spatial database for some GIS application, it is a big challenge to recognize automatically all the simple and complex map objects on scanned maps. This paper presents a robust map interpretation system developed to process Hungarian land register maps (cadastral maps). Processing starts with a raster-to-vector conversion generating a raw vector image from the scanned map. All recognition steps are performed on this raw vector image: segmentation, recognition of separated and not separated symbols, recognition of more complex objects (buildings and parcels). Finally, drawing quality is enhanced utilizing the recognition results. Interpretation is supported by a special data structure - called DG - ensuring dynamic description of hierarchical structures of drawing objects. This data structure is an essential part of our concept, therefore it is discussed in the paper.
   @INPROCEEDINGS{Katona1999,
   AUTHOR = {Endre Katona and Gyorgy Hudra},
   BOOKTITLE = {Proceedings of the International Conference on Image Analysis and Processing},
   TITLE = {An Interpretation System for Cadastral Maps},
   YEAR = {1999},
   ADDRESS = {Venice, Italy},
   MONTH = {September},
   PAGES = {792-797},
   PUBLISHER = {IEEE},
   }
   


2. Attila Kuba. Reconstruction in Different Classes of 2D Discrete Sets. In Gilles Bertrand, Michel Couprie, and Laurent Perroton, editors, Proceedings of the International Conference on Discrete Geometry for Computer Imagery, volume 1568 of Lecture Notes in Computer Science, Marne-la-Vallee, France, pages 153, March 1999. Springer Verlag. [WWW] [PDF]
   Abstract: The problem of reconstruction of two-dimensional discrete sets from their two projections is considered in different classes. The reconstruction algorithms and complexity results are summarized in the case of hv-convex sets, hv-convex polyominoes, hv-convex 8-connected sets, and directed h-convex sets. We show that the reconstruction algorithms used in the class of hv-convex 4-connected sets (polyominoes) can be used, with small modifications, for reconstructing hv-convex 8-connected sets. Finally, it is shown that the directed h-convex sets are uniquely reconstructible with respect to the row and column sum vectors.
   @INPROCEEDINGS{Kuba1999a,
   AUTHOR = {Attila Kuba},
   BOOKTITLE = {Proceedings of the International Conference on Discrete Geometry for Computer Imagery},
   TITLE = {Reconstruction in Different Classes of 2D Discrete Sets},
   YEAR = {1999},
   ADDRESS = {Marne-la-Vallee, France},
   EDITOR = {Gilles Bertrand and Michel Couprie and Laurent Perroton},
   MONTH = {March},
   PAGES = {153},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1568},
   URL = {http://www.springerlink.com/content/bfqwrp67x06vewcf},
   }
   


3. Laszlo G. Nyul and Jayaram K. Udupa. An Approach to Standardizing the MR Image Intensity Scale. In S. K. Mun and Y. Kim, editors, Proceedings of Medical Imaging 1999: Image Display, volume 3658 of SPIE Proceedings, San Diego, USA, pages 595-603, May 1999. [doi:10.1117/12.349472]
   Abstract: Despite the many advantages of MR images, they lack a standard image intensity scale. MR image intensity ranges and the meaning of intensity values vary even for the same protocol (P) and the same body region (D). This causes many difficulties in image display and analysis. We propose a two-step method for standardizing the intensity scale in such a way that for the same P and D, similar intensities will have similar meanings. In the first step, the parameters of the standardizing transformation are 'learned' from an image set. In the second step, for each MR study, these parameters are used to map their histogram into the standardized histogram. The method was tested quantitatively on 90 whole brain FSE T2, PD and T1 studies of MS patients and qualitatively on several other SE PD, T2 and SPGR studies of the grain and foot. Measurements using mean squared difference showed that the standardized image intensities have statistically significantly more consistent range and meaning than the originals. Fixed windows can be established for standardized imags and used for display without the need of per case adjustment. Preliminary results also indicate that the method facilitates improving the degree of automation of image segmentation.
   @INPROCEEDINGS{Nyul:1999:ASM,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa},
   BOOKTITLE = {Proceedings of Medical Imaging 1999: Image Display},
   TITLE = {An Approach to Standardizing the MR Image Intensity Scale},
   YEAR = {1999},
   ADDRESS = {San Diego, USA},
   EDITOR = {S. K. Mun and Y. Kim},
   MONTH = {May},
   PAGES = {595--603},
   SERIES = {SPIE Proceedings},
   VOLUME = {3658},
   DOI = {10.1117/12.349472},
   }
   


4. Laszlo G. Nyul and Jayaram K. Udupa. New Variants of a Method of MRI Scale Normalization. In A. Kuba, M. Samal, and A. Todd-Pokropek, editors, Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), volume 1613 of Lecture Notes in Computer Science, Visegrad, Hungary, pages 490-495, jun--jul 1999. Springer Verlag. [PDF]
   Abstract: One of the major drawbacks of Magnetic Resonance Imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. This causes many difficulties in image display and analysis. We have devised a two-step method wherein all images can be transformed in such a way that for the same protocol and body region, in the transformed images similar intensities will have similar tissue mean- ing. Normalized images can be displayed with fixed windows without the need of per case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormities, such as tumors, can considerably be simplified. This paper introduces and compares new variants of this normalization method that can help to overcome some of the problems with the original method.
   @INPROCEEDINGS{Nyul:1999:NVM_IPMI,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa},
   BOOKTITLE = {Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI)},
   TITLE = {New Variants of a Method of MRI Scale Normalization},
   YEAR = {1999},
   ADDRESS = {Visegrad, Hungary},
   EDITOR = {A. Kuba and M. Samal and A. Todd-Pokropek},
   MONTH = {jun--jul},
   PAGES = {490--495},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1613},
   }
   


5. Kalman Palagyi and Attila Kuba. Directional 3D thinning using 8 subiterations. In Gilles Bertrand, Michel Couprie, and Laurent Perroton, editors, Proceedings of the International Conference on Discrete Geometry for Computer Imagery, volume 1568 of Lecture Notes in Computer Science, Marne-la-Vallee, France, pages 325-336, March 1999. Springer Verlag. [PDF]
   Abstract: Thinning of a binary object is an iterative layer by layer erosion to extract an approximation to its skeleton. In order to provide topology preservation, different thinning techniques have been proposed. One of them is the directional (or border sequential) approach in which each iteration step is subdivided into subiterations where only border points of certain kind are deleted in each subiteration. There are six kinds of border points in 3D images, therefore, 6-subiteration parallel thinning algorithms were generally proposed. In this paper, we present two 8-subiteration algorithms for extracting "surface skeletons" and "curve skeletons", respectively. Both algorithms work in cubic grid for (26,6) images. Deletable points are given by templates that makes easy implementation possible.
   @INPROCEEDINGS{PalagyiKubaDGCI1999,
   AUTHOR = {Kalman Palagyi and Attila Kuba},
   BOOKTITLE = {Proceedings of the International Conference on Discrete Geometry for Computer Imagery},
   TITLE = {Directional 3D thinning using 8 subiterations},
   YEAR = {1999},
   ADDRESS = {Marne-la-Vallee, France},
   EDITOR = {Gilles Bertrand and Michel Couprie and Laurent Perroton},
   MONTH = {March},
   PAGES = {325-336},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1568},
   }
   

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