A Competitive Study of Graph Reduction Methods for Min S-T Cut Image Segmentation

• Autorzy: Węgliński T. , Fabijańska A. , Goclawski J.
• Języki publikacji: EN

Abstrakty

EN

When applied to the segmentation of 3D medical images, graph-cut segmentation algorithms require an extreme amount of memory and time resources in order to represent the image graph and to perform the necessary processing on the graph. These requirements actually exclude the graph-cut based approaches from their practical application. Hence, there is a need to develop the dedicated graph size reduction methods. In this paper, several techniques for the graph size reduction are proposed. These apply the idea of superpixels. In particular, two methods for superpixel creation are introduced. The results of applying the proposed methods to the segmentation of CT datasets using min-cut/max-flow algorithm are presented, compared and discussed.

Słowa kluczowe

EN

3D medical imagery; graph theory; medical applications

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2014
• Tom: Vol. 19, no. 2-3
• Strony: 97--105
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Węgliński T.

• Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland

autor

Fabijańska A.

• Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland

autor

Goclawski J.

• Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland

Bibliografia

• [1] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274-2282
• [2] Boykov, Y., Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124-1137
• [3] Felzenszwalb, P. F., Huttenlocher, D. P. (2004). Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, 59(2), 167-181
• [4] Fishbain, B., Hochbaum, D.S., Yang, Y.T. (2012). Graph-cuts target tracking in videos through joint utilization of color and coarse motion data. UC Berkeley Manuscript
• [5] Goldberg, A.V. (2008). The partial augmentrelabel algorithm for the maximum flow problem. Algorithms-ESA 2008, 466-477
• [6] Grundmann, M., Kwatra, V., Mei Han, and Essa, I. (2010). Efficient hierarchical graph-based video segmentation. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), 2141-2148
• [7] Harris, J., Hirst, J.L., Mossinghoff, M. (2008). Combinatorics and Graph Theory (2nd ed.). Springer, New York, ISBN: 0-387-79710-6
• [8] Mu, Y., Zhang, H., Wang, H., Zuo, W. (2007). Automatic video object seg-mentation using graph cut. In: IEEE international conference on image processing (ICIP 2007), 3, III-377-III-380