Texture Analysis of 3D Fluorescence Microscopy Images Using
RSurf 3D Features

D 2016

Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features

STOKLASA, Roman and Tomáš MAJTNER

Basic information

Original name

Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features

Authors

STOKLASA, Roman (703 Slovakia, guarantor, belonging to the institution) and Tomáš MAJTNER (703 Slovakia, belonging to the institution)

Edition

Los Alamitos, California, International Symposium on Biomedical Imaging (ISBI'16), p. 1212-1216, 5 pp. 2016

Publisher

IEEE Computer Society

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/16:00087753

Organization unit

Faculty of Informatics

ISBN

978-1-4799-2350-2

ISSN

DOI

http://dx.doi.org/10.1109/ISBI.2016.7493484

UT WoS

000386377400286

Keywords in English

RSurf features;HeLa cell images;object recognition;classification;fluorescence microscopy

Abstract

V originále

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.

Links

GA14-22461S, research and development project

Name: Vývoj a studium metod pro kvantifikaci živých buněk (Acronym: Live Cell Quantification)

Investor: Czech Science Foundation

MUNI/A/0935/2015, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)

Investor: Masaryk University, Category A

MUNI/A/0945/2015, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V.

Investor: Masaryk University, Category A

MUNI/A/1159/2014, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IV.

Investor: Masaryk University, Category A

Citovat

STOKLASA, Roman and Tomáš MAJTNER. Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features. Online. In International Symposium on Biomedical Imaging (ISBI'16). Los Alamitos, California: IEEE Computer Society, 2016, p. 1212-1216. ISBN 978-1-4799-2350-2. Available from: https://dx.doi.org/10.1109/ISBI.2016.7493484.

@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 and 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, p.~1212-1216. ISBN~978-1-4799-2350-2. Available from: https://dx.doi.org/10.1109/ISBI.2016.7493484.

Detailed Information on Publication Record