Comparative Evaluation of Interactive Segmentation Approaches

Amrehn M, Glasbrenner J, Steidl S, Maier A (2016)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Publisher: Springer

City/Town: Berlin Heidelberg

Pages Range: 68-73

Conference Proceedings Title: Bildverarbeitung für die Medizin 2016

Event location: Charité - Universitätsmedizin Berlin DE

ISBN: 978-3-662-49465-3


DOI: 10.1007/978-3-662-49465-3

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Image segmentation is a key technique in image processing with the goal to extract important objects from the image. This evaluation study focuses on the segmentation quality of three different interactive segmentation techniques, namely Region Growing, Watershed and the cellular automaton based GrowCut algorithm.

Three different evaluation measures are computed to compare the segmentation quality of each algorithm: Rand Index, Mutual Information, and the Dice Coefficient. For the images in the publicly available ground truth data base utilized for the evaluation, the GrowCut method has a slight advantage over the other two.

The presented results provide insight into the performance and the characteristics with respect to the image quality of each tested algorithm.

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How to cite


Amrehn, M., Glasbrenner, J., Steidl, S., & Maier, A. (2016). Comparative Evaluation of Interactive Segmentation Approaches. In Bildverarbeitung für die Medizin 2016 (pp. 68-73). Charité - Universitätsmedizin Berlin, DE: Berlin Heidelberg: Springer.


Amrehn, Mario, et al. "Comparative Evaluation of Interactive Segmentation Approaches." Proceedings of the Bildverarbeitung für die Medizin, Charité - Universitätsmedizin Berlin Berlin Heidelberg: Springer, 2016. 68-73.

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