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Magyarul

Selected Publications of the Department of Image Processing and Computer Graphics of the year 2000

Articles in journal or book chapters

1. Isabelle Catalaa, Robert I. Grossman, Dennis L. Kolson, Jayaram K. Udupa, Laszlo G. Nyul, Luogang Wei, Xuan Zhang, Marcia Polansky, Lois J. Mannon, and Joseph C. McGowan. Multiple Sclerosis: Magnetization Transfer Histogram Analysis of Segmented Normal-appearing White Matter. Radiology, 216(2):351-355, August 2000. [PDF]
   Abstract: PURPOSE: To investigate and characterize the global distribution of magnetization transfer (MT) ratio values of normal-appearing white matter (NAWM) in patients with relapsing-remitting multiple sclerosis (MS) and test the hypothesis that the MT histogram for NAWM reflects disease progression. MATERIALS AND METHODS: Conventional and MT magnetic resonance (MR) images were obtained in 23 patients and 25 healthy volunteers. Clinical tests for comparison with the MT histogram parameters included the Extended Disability Status Scale and the ambulation index. Lesion load calculated with T2-weighted MR images and whole-brain and white matter volumes were measured. RESULTS: The location of the MT histogram peak and the mean MT ratio for NAWM were significantly lower in patients with MS than in control subjects. In longitudinal studies, the histogram peak location and mean MT ratio shifted in the direction of normal values as the duration of disease increased. A mean of 26.5% of the volume of new lesions identified on the later studies were demonstrated to have originated in NAWM corresponding to ``lost'' pixels on the histogram. CONCLUSION: MT histogram analysis of NAWM, including longitudinal analysis, may provide new prognostic information regarding lesion formation and increase understanding of the course of the disease.
   @ARTICLE{Catalaa:2000:MSM,
   AUTHOR = {Isabelle Catalaa and Robert I. Grossman and Dennis L. Kolson and Jayaram K. Udupa and Laszlo G. Nyul and Luogang Wei and Xuan Zhang and Marcia Polansky and Lois J. Mannon and Joseph C. McGowan},
   JOURNAL = {Radiology},
   TITLE = {Multiple Sclerosis: Magnetization Transfer Histogram Analysis of Segmented Normal-appearing White Matter},
   YEAR = {2000},
   MONTH = {August},
   NUMBER = {2},
   PAGES = {351--355},
   VOLUME = {216},
   }
   


2. Yulin Ge, Jayaram K. Udupa, Laszlo G. Nyul, Luogang Wei, and Robert I. Grossman. Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale. Journal of Magnetic Resonance Imaging, 12(5):715-721, November 2000. [PDF]
   Abstract: Image intensity standardization is a recently developed postprocessing method that is capable of correcting the signal intensity variations in MR images. We evaluated signal intensity of healthy and diseased tissues in 10 multiple sclerosis (MS) patients based on standardized dual fast spin-echo MR images using a numerical postprocessing technique. The main idea of this technique is to deform the volume image histogram of each study to match a standard histogram and to utilize the resulting transformation to map the image intensities into standard scale. Upon standardization, the coefficients of variation of signal intensities for each segmented tissue (gray matter, white matter, lesion plaques, and diffuse abnormal white matter) in all patients were significantly smaller (2.3-9.2 times) than in the original images, and the same tissues from different patients looked alike, with similar intensity characteristics. Numerical tissue characterizability of different tissues in MS achieved by standardization offers a fixed tissue-specific meaning for the numerical values and can significantly facilitate image segmentation and analysis.
   @ARTICLE{Ge:2000:NTC,
   AUTHOR = {Yulin Ge and Jayaram K. Udupa and Laszlo G. Nyul and Luogang Wei and Robert I. Grossman},
   JOURNAL = {Journal of Magnetic Resonance Imaging},
   TITLE = {Numerical Tissue Characterization in MS via Standardization of the MR Image Intensity Scale},
   YEAR = {2000},
   MONTH = {November},
   NUMBER = {5},
   PAGES = {715--721},
   VOLUME = {12},
   }
   


3. Laszlo G. Nyul and Jayaram K. Udupa. MR Image Analysis in Multiple Sclerosis. Advances in Multiple Sclerosis, 10(4):799-816, November 2000.
   @ARTICLE{Nyul:2000:MIA,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa},
   JOURNAL = {Advances in Multiple Sclerosis},
   TITLE = {MR Image Analysis in Multiple Sclerosis},
   YEAR = {2000},
   MONTH = {November},
   PAGES = {799--816},
   VOLUME = {10(4)},
   ADDRESS = {Philadelphia, PA},
   EDITOR = {J. A. Frank},
   PUBLISHER = {W. B. Saunders Company},
   SERIES = {Neuroimaging Clinics of North America},
   }
   


4. Laszlo G. Nyul, Jayaram K. Udupa, and Xuan Zhang. New Variants of a Method of MRI Scale Standardization. IEEE Transactions on Medical Imaging, 19(2):143-150, February 2000. [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. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning.We have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) 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 meaning. Standardized 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 abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.
   @ARTICLE{Nyul:2000:NVM,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa and Xuan Zhang},
   JOURNAL = {IEEE Transactions on Medical Imaging},
   TITLE = {New Variants of a Method of MRI Scale Standardization},
   YEAR = {2000},
   MONTH = {February},
   NUMBER = {2},
   PAGES = {143--150},
   VOLUME = {19},
   }
   


5. Tamas Sziranyi, Josiane Zerubia, Laszlo Czuni, David Geldreich, and Zoltan Kato. Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures. Real Time Imaging, 6(3):195-211, June 2000. [PDF]
   Abstract: Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips. As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 s. In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures. In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel.
   @ARTICLE{Sziranyi-etal2000,
   AUTHOR = {Tamas Sziranyi and Josiane Zerubia and Laszlo Czuni and David Geldreich and Zoltan Kato},
   JOURNAL = {Real Time Imaging},
   TITLE = {Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures},
   YEAR = {2000},
   MONTH = {June},
   NUMBER = {3},
   PAGES = {195--211},
   VOLUME = {6},
   }
   

Conference articles

1. Attila Kuba and Maurice Nivat. Reconstruction of Discrete Sets with Absorption. In Gunilla Borgefors, Ingela Nystrom, and Gabriella Sanniti di Baja, editors, Proceedings of the International Conference on Discrete Geometry for Computer Imagery, volume 1953 of Lecture Notes in Computer Science, Uppsala, Sweden, pages 137, 2000. Springer Verlag. [WWW] [PDF]
   Abstract: A generalization of a classical discrete tomography problem is considered: Reconstruct binary matrices from their absorbed row and columns sums, i.e., when some known absorption is supposed. It is mathematically interesting when the absorbed projection of a matrix element is the same as the absorbed projection of the next two consecutive elements together. We show that, in this special case, the non-uniquely determined matrices contain a certain configuration of 0s and 1s, called alternatively corner-connected components. Furthermore, such matrices can be transformed into each other by switchings the 0s and 1s of these components.
   @INPROCEEDINGS{Kuba2000,
   AUTHOR = {Attila Kuba and Maurice Nivat},
   BOOKTITLE = {Proceedings of the International Conference on Discrete Geometry for Computer Imagery},
   TITLE = {Reconstruction of Discrete Sets with Absorption},
   YEAR = {2000},
   ADDRESS = {Uppsala, Sweden},
   EDITOR = {Gunilla Borgefors and Ingela Nystrom and Sanniti di Baja, Gabriella},
   PAGES = {137},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1953},
   URL = {http://www.springerlink.com/content/ghtmmgeadc4d8ngx},
   }
   


2. Laszlo G. Nyul, Alexandre X. Falcao, and Jayaram K. Udupa. Fuzzy-connected 3D Image Segmentation at Interactive Speeds. In K. M. Hanson, editor, Proceedings of Medical Imaging 2000: Image Processing, volume 3979 of SPIE Proceedings, San Diego, USA, pages 212-223, June 2000. [PDF]
   Abstract: Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 algorithms under two categories -- label correcting and label setting. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20-360 fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue specific meaning (such as CT or standardized MR images) are utilized, all parameters for the segmentation method can be fixed once for all, all intermediate data can be computed before the user interaction is needed, and the user can be provided with more information at the time of interaction.
   @INPROCEEDINGS{Nyul:2000:FC3,
   AUTHOR = {Laszlo G. Nyul and Alexandre X. Falcao and Jayaram K. Udupa},
   BOOKTITLE = {Proceedings of Medical Imaging 2000: Image Processing},
   TITLE = {Fuzzy-connected 3D Image Segmentation at Interactive Speeds},
   YEAR = {2000},
   ADDRESS = {San Diego, USA},
   EDITOR = {K. M. Hanson},
   MONTH = {June},
   PAGES = {212--223},
   SERIES = {SPIE Proceedings},
   VOLUME = {3979},
   }
   


3. Laszlo G. Nyul and Jayaram K. Udupa. Standardizing the MR Image Intensity Scales: Making MR Intensities Have Tissue-specific Meaning. In S. K. Mun, editor, Proceedings of Medical Imaging 2000: Image Display and Visualization, volume 3976 of SPIE Proceedings, San Diego, USA, pages 496-504, April 2000. [doi:10.1117/12.383076]
   Abstract: One of the major drawbacks of Magnetic Resonance Imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities such as x-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations, and therefore, the absolute intensity values do not have a fixed meaning. 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 meaning. Standardized images can be displayed with fixed windows without the need of per case adjustment. More importantly, extraction of quantitative information with fixed windows without the need of per case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.
   @INPROCEEDINGS{Nyul:2000:SMI,
   AUTHOR = {Laszlo G. Nyul and Jayaram K. Udupa},
   BOOKTITLE = {Proceedings of Medical Imaging 2000: Image Display and Visualization},
   TITLE = {Standardizing the MR Image Intensity Scales: Making MR Intensities Have Tissue-specific Meaning},
   YEAR = {2000},
   ADDRESS = {San Diego, USA},
   EDITOR = {S. K. Mun},
   MONTH = {April},
   PAGES = {496--504},
   SERIES = {SPIE Proceedings},
   VOLUME = {3976},
   DOI = {10.1117/12.383076},
   }
   


4. Kalman Palagyi. A 3D 3-subiteration thinning algorithm for medial surfaces. In Gunilla Borgefors, Ingela Nystrom, and Gabriella Sanniti di Baja, editors, Proceedings of the International Conference on Discrete Geometry for Computer Imagery, volume 1953 of Lecture Notes in Computer Science, Uppsala, Sweden, pages 406-417, December 2000. Springer Verlag. [PDF]
   Abstract: Thinning on a binary picture is an iterative layer by layer erosion to extract a reasonable approximation to its skeleton. This paper presents an efficient 3D parallel thinning algorithm which produces medial surfaces. Three-subiteration directional strategy is proposed: each iteration step is composed of three parallel subiterations according to the three deletion directions. The algorithm makes easy implementation possible, since deletable points are given by matching templates containing twentyeight elements. The topological correctness of the algorithm for (26,6) binary pictures is proved.
   @INPROCEEDINGS{PalagyiDGCI2000,
   AUTHOR = {Kalman Palagyi},
   BOOKTITLE = {Proceedings of the International Conference on Discrete Geometry for Computer Imagery},
   TITLE = {A 3D 3-subiteration thinning algorithm for medial surfaces},
   YEAR = {2000},
   ADDRESS = {Uppsala, Sweden},
   EDITOR = {Gunilla Borgefors and Ingela Nystrom and di Baja, Gabriella Sanniti},
   MONTH = {December},
   PAGES = {406-417},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1953},
   }
   


5. Attila Tanacs, Gabor Czedli, Kalman Palagyi, and Attila Kuba. Point-based registration assuming affine motion. In Gerald Sommer and Yehoshua Y. Zeevi, editors, Proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle (AFPAC), volume 1888 of Lecture Notes in Computer Science, Kiel, Germany, pages 329-338, September 2000. Springer Verlag. [PDF] [doi:10.1007/10722492_26]
   Abstract: Registration is a fundamental task in image processing. Its purpose is to find a geometrical transformation that relates the points of an image to their corresponding points of another image. The determination of the optimal transformation depends on the types of variations between the images. In this paper we propose a robust method based on two sets of points representing the images. One--to--one correspondence is assumed between these two sets. Our approach finds global affine transformation between the sets of points and can be used in any arbitrary dimension $k\ge 1$. A sufficient existence condition for a unique solution is given and proven. Our method can be used to solve various registration problems emerged in numerous fields, including medical image processing, remotely sensed data processing, and computer vision.
   @INPROCEEDINGS{Tanacs:2000:AFPAC,
   AUTHOR = {Attila Tanacs and Gabor Czedli and Kalman Palagyi and Attila Kuba},
   BOOKTITLE = {Proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle (AFPAC)},
   TITLE = {Point-based registration assuming affine motion},
   YEAR = {2000},
   ADDRESS = {Kiel, Germany},
   EDITOR = {Gerald Sommer and Yehoshua Y. Zeevi},
   MONTH = {September},
   PAGES = {329-338},
   PUBLISHER = {Springer Verlag},
   SERIES = {Lecture Notes in Computer Science},
   VOLUME = {1888},
   DOI = {10.1007/10722492_26},
   }
   


6. Jayaram K. Udupa, Laszlo G. Nyul, Yulin Ge, and Robert I. Grossman. Multiprotocol MR Image Segmentation in Multiple Sclerosis: Experience With Over 1000 Studies. In K. M. Hanson, editor, Proceedings of Medical Imaging 2000: Image Processing, volume 3979 of SPIE Proceedings, San Diego, USA, pages 1017-1027, June 2000. [doi:10.1117/12.387606]
   Abstract: Multiple Sclerosis (MS) is an acquired disease of the central nervous system. Subjective cognitive and ambulatory test scores on a scale called EDSS are currently utilized to assess the disease severity. Various MRI protocols are being investigated to study the disease based on how it manifests itself in the images. In an attempt to eventually replace EDSS by an objective measure to assess the natural course of the disease and its response to therapy, we have developed image segmentation methods based on fuzzy connectedness to quantify various objects in multiprotocol MRI. These include the macroscopic objects such as lesions, the gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and brain parenchyma as well as the microscopic aspects of the diseased WM. Over 1000 studies have been processed to date. By far the strongest correlations with the clinical measures were demonstrated by the Magnetization Transfer Ratio (MTR) histogram parameters obtained for the various segmented tissue regions emphasizing the importance of considering the microscopic/diffused nature of the disease in the individual tissue regions. Brain parenchymal volume also demonstrated a strong correlation with the clinical measures indicating that brain atrophy is an important indicator of the disease. Fuzzy connectedness is a viable segmentation method for studying MS.
   @INPROCEEDINGS{Udupa:2000:MMI,
   AUTHOR = {Jayaram K. Udupa and Laszlo G. Nyul and Yulin Ge and Robert I. Grossman},
   BOOKTITLE = {Proceedings of Medical Imaging 2000: Image Processing},
   TITLE = {Multiprotocol MR Image Segmentation in Multiple Sclerosis: Experience With Over 1000 Studies},
   YEAR = {2000},
   ADDRESS = {San Diego, USA},
   EDITOR = {K. M. Hanson},
   MONTH = {June},
   PAGES = {1017--1027},
   SERIES = {SPIE Proceedings},
   VOLUME = {3979},
   DOI = {10.1117/12.387606},
   }
   

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