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

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.

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

• 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: electronic version available online
• RIV identification code: RIV/00216224:14330/16:00087753
• Organization unit: Faculty of Informatics
• ISBN: 978-1-4799-2350-2
• ISSN: 1945-7928
• 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
• Tags: cbia-web, firank_B
• Tags: International impact, Reviewed

Abstract

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