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@inproceedings{1322011,
author = {Stoklasa, Roman and Majtner, Tomáš},
address = {Los Alamitos, California},
booktitle = {International Symposium on Biomedical Imaging (ISBI'16)},
doi = {http://dx.doi.org/10.1109/ISBI.2016.7493484},
keywords = {RSurf features;HeLa cell images;object recognition;classification;fluorescence microscopy},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Los Alamitos, California},
isbn = {978-1-4799-2350-2},
pages = {1212-1216},
publisher = {IEEE Computer Society},
title = {Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features},
year = {2016}
}
TY - JOUR
ID - 1322011
AU - Stoklasa, Roman - Majtner, Tomáš
PY - 2016
TI - Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features
PB - IEEE Computer Society
CY - Los Alamitos, California
SN - 9781479923502
KW - RSurf features;HeLa cell images;object recognition;classification;fluorescence microscopy
N2 - Classification tasks of biomedical images are still interesting topic of research with many possibilities of improvement. A very important part in this task is feature extraction process, where different image descriptors are used. Recently, a new approach of RSurf features was introduced with application in recognition of the 2D HEp-2 cell images. In this work, we present the extension of these features for the 3D volumetric images and demonstrate its superiority in recognition of sub-cellular protein distribution. The performance is tested on public HeLa dataset containing 9 different classes. The presented k-NN classifier based purely on the RSurf 3D features achieves more than 99% accuracy in recognition of the 3D HeLa images.
ER -
STOKLASA, Roman a Tomáš MAJTNER. Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features. Online. In \textit{International Symposium on Biomedical Imaging (ISBI'16)}. Los Alamitos, California: IEEE Computer Society, 2016, s.~1212-1216. ISBN~978-1-4799-2350-2. Dostupné z: https://dx.doi.org/10.1109/ISBI.2016.7493484.
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