Generation of Synthetic Image Datasets for Time-Lapse
Fluorescence Microscopy

D 2012

Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy

SVOBODA, David and Vladimír ULMAN

Basic information

Original name

Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy

Authors

SVOBODA, David (203 Czech Republic, guarantor, belonging to the institution) and Vladimír ULMAN (203 Czech Republic, belonging to the institution)

Edition

LNCS 7325, Part II. Heidelberg, Proceedings of 9th International Conference on Image Analysis and Recognition, p. 473-482, 10 pp. 2012

Publisher

Springer-Verlag

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

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/12:00057285

Organization unit

Faculty of Informatics

ISBN

978-3-642-31297-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-642-31298-4_56

Keywords in English

Simulation; Optical flow; 3D image sequences; Fluorescence optical microscopy

Abstract

V originále

In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered. In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales. Such datasets can be then processed by selected segmentation or motion tracking algorithms. The results can be compared with the GT and the quality of the applied algorithm can be measured.

Links

GBP302/12/G157, research and development project

Name: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii

Investor: Czech Science Foundation

Citovat

SVOBODA, David and Vladimír ULMAN. Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy. In Campilho, Aurélio; Kamel, Mohamed. Proceedings of 9th International Conference on Image Analysis and Recognition. LNCS 7325, Part II. Heidelberg: Springer-Verlag, 2012, p. 473-482. ISBN 978-3-642-31297-7. Available from: https://dx.doi.org/10.1007/978-3-642-31298-4_56.

@inproceedings{974481,
   author = {Svoboda, David and Ulman, Vladimír},
   address = {Heidelberg},
   booktitle = {Proceedings of 9th International Conference on Image Analysis and Recognition},
   doi = {http://dx.doi.org/10.1007/978-3-642-31298-4_56},
   edition = {LNCS 7325, Part II},
   editor = {Campilho, Aurélio; Kamel, Mohamed},
   keywords = {Simulation; Optical flow; 3D image sequences; Fluorescence optical microscopy},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Heidelberg},
   isbn = {978-3-642-31297-7},
   pages = {473-482},
   publisher = {Springer-Verlag},
   title = {Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy},
   year = {2012}
}

TY  - JOUR
ID  - 974481
AU  - Svoboda, David - Ulman, Vladimír
PY  - 2012
TI  - Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy
PB  - Springer-Verlag
CY  - Heidelberg
SN  - 9783642312977
KW  - Simulation
KW  - Optical flow
KW  - 3D image sequences
KW  - Fluorescence optical microscopy
N2  - In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered. In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales. Such datasets can be then processed by selected segmentation or motion tracking algorithms. The results can be compared with the GT and the quality of the applied algorithm can be measured.
ER  -

SVOBODA, David and Vladimír ULMAN. Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy. In Campilho, Aurélio; Kamel, Mohamed. \textit{Proceedings of 9th International Conference on Image Analysis and Recognition}. LNCS 7325, Part II. Heidelberg: Springer-Verlag, 2012, p.~473-482. ISBN~978-3-642-31297-7. Available from: https://dx.doi.org/10.1007/978-3-642-31298-4\_{}56.

Detailed Information on Publication Record