Detailed Information on Publication Record
2007
A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy
MAŠKA, Martin, Jan HUBENÝ, David SVOBODA and Michal KOZUBEKBasic information
Original name
A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy
Name in Czech
Porovnání rychlých aproximací Level Set metody pro segmentaci obrazu ve fluorescenční mikroskopii
Authors
MAŠKA, Martin (203 Czech Republic, guarantor, belonging to the institution), Jan HUBENÝ (203 Czech Republic, belonging to the institution), David SVOBODA (203 Czech Republic, belonging to the institution) and Michal KOZUBEK (203 Czech Republic, belonging to the institution)
Edition
Berlin, Heidelberg, 3rd International Symposium on Visual Computing, p. 571-581, 11 pp. 2007
Publisher
Spinger-Verlag
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
References:
RIV identification code
RIV/00216224:14330/07:00022769
Organization unit
Faculty of Informatics
ISBN
978-3-540-76855-5
UT WoS
000251785200056
Keywords in English
image segmentation; level set method; active contours
Tags
International impact, Reviewed
Změněno: 13/12/2015 02:07, doc. RNDr. Martin Maška, Ph.D.
V originále
Image segmentation, one of the fundamental task of image processing, can be accurately solved using the level set framework. However, the computational time demands of the level set methods make them practically useless, especially for segmentation of large threedimensional images. Many approximations have been introduced in recent years to speed up the computation of the level set methods. Although these algorithms provide favourable results, most of them were not properly tested against ground truth images. In this paper we present a comparison of three methods: the Sparse-Field method [1], Deng and Tsui's algorithm [2] and Nilsson and Heyden's algorithm [3]. Our main motivation was to compare these methods on 3D image data acquired using fluorescence microscope, but we suppose that presented results are also valid and applicable to other biomedical images like CT scans, MRI or ultrasound images. We focus on a comparison of the method accuracy, speed and ability to detect several objects located close to each other for both 2D and 3D images. Furthermore, since the input data of our experiments are artificially generated, we are able to compare obtained segmentation results with ground truth images.
In Czech
Segmentaci obrazu, jednu ze základních úloh zpracování obrazu, lze přesně řešit pomocí Level Set metody.
Links
| LC535, research and development project |
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| MSM0021622419, plan (intention) |
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| 2B06052, research and development project |
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