MAJTNER, Tomáš, Roman STOKLASA and David SVOBODA. RSurf - the Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells. In 22nd International Conference on Pattern Recognition. Los Alamitos, California: IEEE Computer Society, 2014, p. 1194-1199. ISBN 978-1-4799-5208-3. Available from: https://dx.doi.org/10.1109/ICPR.2014.215.
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Basic information
Original name RSurf - the Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells
Authors MAJTNER, Tomáš (703 Slovakia, guarantor, belonging to the institution), Roman STOKLASA (703 Slovakia, belonging to the institution) and David SVOBODA (203 Czech Republic, belonging to the institution).
Edition Los Alamitos, California, 22nd International Conference on Pattern Recognition, p. 1194-1199, 6 pp. 2014.
Publisher IEEE Computer Society
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form storage medium (CD, DVD, flash disk)
RIV identification code RIV/00216224:14330/14:00073550
Organization unit Faculty of Informatics
ISBN 978-1-4799-5208-3
ISSN 1051-4651
Doi http://dx.doi.org/10.1109/ICPR.2014.215
UT WoS 000359818001053
Keywords in English texture descriptor;rsurf;hep-2
Tags best, best2, cbia-web, firank_A
Tags International impact, Reviewed
Changed by Changed by: RNDr. Ing. Bc. Tomáš Majtner, Ph.D., učo 172786. Changed: 9/1/2015 15:37.
Abstract
In biomedical image analysis, object description and classification tasks are very common. Our work relates to the problem of classification of Human Epithelial (HEp-2) cells. Since the crucial part of each classification process is the feature extraction and selection, much attention should be concentrated to the development of proper image descriptors. In this article, we introduce a new efficient texture-based image descriptor for HEp-2 images. We compare proposed descriptor with LBP, Haralick features (GLCM statistics) and Tamura features using the public MIVIA HEp-2 Images Dataset. Our descriptor outperforms all previously mentioned approaches and the classifier based solely on the proposed descriptor is able to achieve the accuracy as high as 87.8%.
Links
GBP302/12/G157, research and development projectName: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii
Investor: Czech Science Foundation
MUNI/A/0765/2013, interní kód MUName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Category A
MUNI/A/0855/2013, interní kód MUName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace III. (Acronym: FI MAV III.)
Investor: Masaryk University, Category A
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