An application of the supervoxel-based Fuzzy C-Means with a GPU support to segmentation of volumetric brain images.
Anna Fabijańska, Jarosław Gocławski
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 777–785 (2016)
Abstract. In this paper the problem of segmentation of volumetric medical images is considered. The fast and effective segmentation is obtained by applying the proposed approach which combines the idea of supervoxels and the Fuzzy CMeans algorithm. In particular, Fuzzy C-Means is used to cluster supervoxels produced by the fast 3D region growing. Additional acceleration of the method is achieved with the support of graphical processor (GPU). The detailed description of the proposed approach is given. The results of applying the method to volumetric CT and MRI brain images and CT images of various phantoms are presented, analysed and discussed. The issues related to accuracy of the method, memory workload and the running time are also considered.
- D. D. Burdescu, L. Stanescu, M. Brezovan, C. S. Spahiu, “Efficient Volumetric Segmentation Method”, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, pp. 659–668, 2014, http://dx.doi.org/10.15439/978-83-60810-58-3
- K. Xiao, A. E. Hassanien, N. I. Ghali, “Medical Image Segmentation Using Information Extracted from Deformation”, Proceedings of the 2011 Federated Conference on Computer Science and Information Systems, pp. 157–163, 2014.
- M. R. Ogiela, T. Hachaj, “Automatic segmentation of the carotid artery bifurcation region with a region-growing approach”, Journal of Electronic Imaging, vol. 22(3), 033029, 2013, http://dx.doi.org/10.1117/1.JEI.22.3.033029
- J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
- R. Nock, and F. Nielsen, “On weighting clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28(8), pp. 1–13, 2006, http://dx.doi.org/10.1109/TPAMI.2006.168
- C. Couprie, L. Grady, L. Najman, and H. Talbot, “Power watershed: A unifying graph-based optimization framework”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33(7), pp. 1384–1399, 2011, http://dx.doi.org/10.1109/TPAMI.2010.200
- W. Tao, H. Jin, and Y. Zhang, “Image segmentation based on mean shift and normalized cuts”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 37(5), pp. 1382–1389, 2007, http://dx.doi.org/10.1109/TSMCB.2007.902249
- R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk,“SLIC superpixels compared to state of the art superpixel methods”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34(11), pp. 2274–2282, 2012, http://dx.doi.org/10.1109/TPAMI.2012.120
- A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, and K. Siddiqi,“Turbopixels: Fast superpixels using geometric flows” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31(12), pp. 2290–2297, 2009, http://dx.doi.org/10.1109/TPAMI.2009.96
- P. F. Felzenszwalb, and D. P. Huttenlocher, “Efficient graph-based image segmentation” International Journal of Computer Vision, vol. 59(2), pp. 167–181, 2004, http://dx.doi.org/10.1023/B:VISI.0000022288.19776.77
- A. Fabijańska, and J. Gocławski, “The segmentation of 3D images using the random walking technique on a randomly created image adjacency graph” IEEE Transactions on Image Processing, vol. 24(2), pp. 524–537, 2015, http://dx.doi.org/10.1109/TIP.2014.2383323
- W. Pratt, Digital Image Processing, 4th ed. Los Altos, California: John Wiley & Sons Inc., 2007.
- J. Sanders and E. Kandrot, Cuda by Example. An Introduction to General Purpose GPU programming, NVIDIA Coropration, 2011.
- N. Wilt, The CUDA Handbook. A Comprehensive guide to GPU Programming, Addison-Wesley, Upper Saddle River, NJ, 2013.
- Brachytherapy QA Phantom CIRS 045, http://www.cirsinc.com/products/all/71/brachytherapy-qa-phantom
- Electron Density Phantom CIRS 062, http://www.cirsinc.com/products/all/24/electron-density-phantom