Title
A comparison of fast level set-like algorithms for image segmentation in fluorescence microscopy
Abstract
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 three-dimensional 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.
Year
DOI
Venue
2007
10.1007/978-3-540-76856-2_56
ISVC
Keywords
Field
DocType
level set framework,image data,fluorescence microscopy,level set method,large three-dimensional image,image processing,ultrasound image,biomedical image,ground truth image,segmentation result,fast level set-like algorithm,image segmentation,ct scan,ground truth,biomedical imaging,3d imaging,level set,active contour
Scale-space segmentation,Computer science,Image processing,Segmentation-based object categorization,Level set,Image segmentation,Artificial intelligence,Computer vision,Pattern recognition,Segmentation,Level set method,Algorithm,Ground truth
Conference
Volume
ISSN
ISBN
4842
0302-9743
3-540-76855-6
Citations 
PageRank 
References 
2
0.39
9
Authors
4
Name
Order
Citations
PageRank
Martin Maška1323.32
Jan Hubený251.17
David Svoboda315321.05
Michal Kozubek411417.80