A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy

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Authors

MAŠKA Martin HUBENÝ Jan SVOBODA David KOZUBEK Michal

Year of publication 2007
Type Article in Proceedings
Conference 3rd International Symposium on Visual Computing
MU Faculty or unit

Faculty of Informatics

Citation
Web http://dx.doi.org/10.1007/978-3-540-76856-2_56
Field Informatics
Keywords image segmentation; level set method; active contours
Description 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.
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