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Selected Publications of the Department of Image Processing and Computer Graphics of the year 2004

Articles in journal or book chapters

1. Attila Kuba, Antal Nagy, and Emese Balogh. Reconstruction of hv-convex binary matrices from their absorbed projections. Discrete Applied Mathematics, 139:137-148, April 2004. [PDF] [doi:10.1016/j.dam.2002.11.001]
   Abstract: The reconstruction of hv-convex binary matrices from their absorbed projections is considered. Although this problem is NP-hard if the non-absorbed row and column sums are available, it is proved that such a reconstruction problem can be solved in polynomial time from absorbed projections when the absorption is represented by $\beta = (1+sqrt{5})/2$. Also a reconstruction algorithm is given to determine the whole structure of hv-convex binary matrices from such projections.
   @ARTICLE{Kuba2004,
   AUTHOR = {Attila Kuba and Antal Nagy and Emese Balogh},
   JOURNAL = {Discrete Applied Mathematics},
   TITLE = {Reconstruction of hv-convex binary matrices from their absorbed projections},
   YEAR = {2004},
   MONTH = {April},
   PAGES = {137-148},
   VOLUME = {139},
   DOI = {10.1016/j.dam.2002.11.001},
   }
   


2. Burkhard Schillinger, N. Kardjilov, and Attila Kuba. Region of interest tomography of bigger than detector samples. Applied Radiation and Isotopes, 61(4):561-565, October 2004. [PDF] [doi:10.1016/j.apradiso.2004.03.092]
   Abstract: For many neutron tomography setups, the maximum sample size for tomography is limited by a comparatively small beam cross section. However, it is not well known outside the medical field that it is possible to perform region-of-interest tomography of sections inside the object that fit into the beam and detector area. Approximately valid reconstruction data appear in a circle with a diameter of approximately the detector width, but with incomplete data and strong artifacts outside that circle. These artifacts can be removed either by mathematical means or by simple geometrical cutting of the reconstructed data, enabling the examination of samples bigger than the detector or beam area.
   @ARTICLE{Schillinger2004,
   AUTHOR = {Burkhard Schillinger and N. Kardjilov and Attila Kuba},
   JOURNAL = {Applied Radiation and Isotopes},
   TITLE = {Region of interest tomography of bigger than detector samples},
   YEAR = {2004},
   MONTH = {October},
   NUMBER = {4},
   PAGES = {561-565},
   VOLUME = {61},
   DOI = {10.1016/j.apradiso.2004.03.092},
   }
   


3. Attila Tanacs. Kijelolt pontparokon alapulo kepregisztracios modszerek. Alkalmazott Matematikai Lapok, 21:237-260, 2004. [PDF]
   Abstract: Registration is a fundamental task in digital image processing. Its purpose is to find a geometrical transformation that relates the points of an image to their corresponding points of another image. A general and easy to use solution for registration problems is selecting pairs of points as features. In this paper, after a short general introduction which describes the most common features of the registration methods, we discuss some of the point based methods proposed by us and other authors. We successfully use these in the field of medical image registration but they also can be used in other fields including computer vision and remotely sensed data processing. We didn't inted to describe all the available methods. For some transformation types such as rigid-body, there are several others with similar complexity and reliability. We intended to give at least one useful method for every transformation types.
   @ARTICLE{Tanacs:2004:AlkMatLapok,
   AUTHOR = {Attila Tanacs},
   JOURNAL = {Alkalmazott Matematikai Lapok},
   TITLE = {Kijelolt pontparokon alapulo kepregisztracios modszerek},
   YEAR = {2004},
   PAGES = {237--260},
   VOLUME = {21},
   }
   

Conference articles

1. Zoltan Kato. Reversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image Segmentation. In Andreas Hoppe, Sarah Barman, and Tim Ellis, editors, Proceedings of the British Machine Vision Conference, volume 1, Kingston, UK, pages 37-46, September 2004. BMVA. [PDF] [PS]
   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. Experimental results are promising, we have obtained accurate results on a variety of real color images.
   @INPROCEEDINGS{Kato2004a,
   AUTHOR = {Zoltan Kato},
   BOOKTITLE = {Proceedings of the British Machine Vision Conference},
   TITLE = {Reversible Jump Markov Chain Monte Carlo for Unsupervised MRF Color Image Segmentation},
   YEAR = {2004},
   ADDRESS = {Kingston, UK},
   EDITOR = {Andreas Hoppe and Sarah Barman and Tim Ellis},
   MONTH = {September},
   ORGANIZATION = {BMVA},
   PAGES = {37--46},
   VOLUME = {1},
   PS = {../papers/bmvc2004.ps},
   }
   


2. Kalman Palagyi, Juerg Tschirren, Eric A. Hoffman, and Milan Sonka. Assessment of Intrathoracic Airway Trees: Methods and In Vivo Validation. In Nicu Sebe, Michael S. Lew, and Thomas S. Huang, editors, Proceedings of the Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis: ECCV 2004 Workshops CVAMIA and MMBIA, volume 3117 of Lecture Notes in Computer Science, Prague, Czech Republic, pages 341-352, May 2004. Springer Verlag. [PDF]
   Abstract: A method for quantitative assessment of tree structures is reported allowing evaluation of airway tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in a computer phantom subjected to changes of its orientation as well as in repeatedly CT-scanned rigid and rubber plastic phantoms. In this paper, validation is reported in six in vivo scans of the human chest.
   @INPROCEEDINGS{PalagyiEtalECCV2004,
   AUTHOR = {Kalman Palagyi and Juerg Tschirren and Eric A. Hoffman and Milan Sonka},
   BOOKTITLE = {Proceedings of the Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis: ECCV 2004 Workshops CVAMIA and MMBIA},
   TITLE = {Assessment of Intrathoracic Airway Trees: Methods and In Vivo Validation},
   YEAR = {2004},
   ADDRESS = {Prague, Czech Republic},
   EDITOR = {Nicu Sebe and Michael S. Lew and Thomas S. Huang},
   MONTH = {May},
   PAGES = {341-352},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {3117},
   }
   


3. Ying Zhuge, Jayaram K. Udupa, and Laszlo G. Nyul. Multiple Sclerosis Lesion Quantification in MR Images by Using Vectorial Scale-based Relative Fuzzy Connectedness. In J. M. Fitzpatrick and M. Sonka, editors, Proceedings of Medical Imaging 2004: Image Processing, volume 5370 of SPIE Proceedings, San Diego, USA, pages 1764-1773, May 2004. [doi:10.1117/12.535655]
   Abstract: This paper presents a methodology for segmenting PD- and T2-weighted brain magnetic resonance (MR) images of multiple sclerosis (MS) patients into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and MS lesions. For a given vectorial image (with PD- and T2-weighted components) to be segmented, we perform first intensity inhomogeneity correction and standardization prior to segmentation. Absolute fuzzy connectedness and certain morphological operations are utilized to generate the brain intracranial mask. The optimum thresholding method is applied to the product image (the image in which voxel values represent T2 value x PD value) to automatically recognize potential MS lesion sites. Then, the recently developed technique -- vectorial scale-based relative fuzzy connectedness -- is utilized to segment all voxels within the brain intracranial mask into WM, GM, CSF, and MS lesion regions. The number of segmented lesions and the volume of each lesion are finally output as well as the volume of other tissue regions. The method has been tested on 10 clinical brain MRI data sets of MS patients. An accuracy of better than 96% has been achieved. The preliminary results indicate that its performance is better than that of the k-nearest neighbors (kNN) method.
   @INPROCEEDINGS{Nyul:2004:MSL,
   AUTHOR = {Ying Zhuge and Jayaram K. Udupa and Laszlo G. Nyul},
   BOOKTITLE = {Proceedings of Medical Imaging 2004: Image Processing},
   TITLE = {Multiple Sclerosis Lesion Quantification in MR Images by Using Vectorial Scale-based Relative Fuzzy Connectedness},
   YEAR = {2004},
   ADDRESS = {San Diego, USA},
   EDITOR = {J. M. Fitzpatrick and M. Sonka},
   MONTH = {May},
   PAGES = {1764--1773},
   SERIES = {SPIE Proceedings},
   VOLUME = {5370},
   DOI = {10.1117/12.535655},
   }
   

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