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

  • Martin Maška
  • Jan Hubený
  • David Svoboda
  • Michal Kozubek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


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.


image segmentation level set method active contours 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Martin Maška
    • 1
  • Jan Hubený
    • 1
  • David Svoboda
    • 1
  • Michal Kozubek
    • 1
  1. 1.Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, BrnoCzech Republic

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