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@inproceedings{726645,
author = {Maška, Martin and Hubený, Jan and Svoboda, David and Kozubek, Michal},
address = {Berlin, Heidelberg},
booktitle = {3rd International Symposium on Visual Computing},
keywords = {image segmentation; level set method; active contours},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Berlin, Heidelberg},
isbn = {978-3-540-76855-5},
note = {LNCS 4842},
pages = {571-581},
publisher = {Spinger-Verlag},
title = {A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy},
url = {http://dx.doi.org/10.1007/978-3-540-76856-2_56},
year = {2007}
}
TY - JOUR
ID - 726645
AU - Maška, Martin - Hubený, Jan - Svoboda, David - Kozubek, Michal
PY - 2007
TI - A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy
PB - Spinger-Verlag
CY - Berlin, Heidelberg
SN - 9783540768555
N1 - LNCS 4842
KW - image segmentation
KW - level set method
KW - active contours
UR - http://dx.doi.org/10.1007/978-3-540-76856-2_56
N2 - 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.
ER -
MAŠKA, Martin, Jan HUBENÝ, David SVOBODA and Michal KOZUBEK. A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy. Online. In \textit{3rd International Symposium on Visual Computing}. Berlin, Heidelberg: Spinger-Verlag, 2007, p.~571-581. ISBN~978-3-540-76855-5.
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