Features Determination from Super-Voxels Obtained with Relative Linear Interactive Clustering

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In this paper, the problem of segmentation of 3D Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) brain images is considered. A supervoxel-based segmentation is regarded. In particular, a new approach called Relative Linear Interactive Clustering (RLIC) is introduced. The method, dedicated to image division into super-voxels, is an extension of the Simple Linear Interactive Clustering (SLIC) super-pixels algorithm. During RLIC execution firstly, the cluster centres and the regular grid size are initialized. These are next clustered by Fuzzy C-Means algorithm. Then, the extraction of the super-voxels statistical features is performed. The method contributes with 3D images and serves fully volumetric image segmentation. Five cases are tested demonstrating that our Relative Linear Interactive Clustering (RLIC) is apt to handle huge size of images with a significant accuracy and a low computational cost. The results of applying the suggested method to segmentation of the brain tumour are exposed and discussed.


  • [1] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S. (2010). Slic superpixels (No. EPFL-REPORT-149300)

  • [2] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2274-2282

  • [3] Ayed, I. B., Punithakumar, K., Li, S. (2015). Distribution matching with the bhattacharyya similarity: a bound optimization framework. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1777-1791

  • [4] Bakkari, A., Braiek, E. B., Njeh, I., Hamida, A. B. (2014, March). Automatic brain mr perfusion image segmentation using adaptive diffusion flow active contours based on modified fuzzy c means. In Advanced Technologies for Signal and Image Processing (ATSIP), 2014 1st International Conference on (pp. 214-218). IEEE

  • [5] Bakkari, A., Fabijańska, A. (2015, September). Segmentation of cerebrospinal fluid from 3D CT brain scans using modified Fuzzy C-Means based on super-voxels. In Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on (pp. 809-818). IEEE

  • [6] Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media

  • [7] Liu, B., Hu, H., Wang, H., Wang, K., Liu, X., Yu, W. (2013). Superpixel-based classification with an adaptive number of classes for polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 51(2), 907-924

  • [8] Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P. (2012). Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. IEEE transactions on medical imaging, 31(2), 474-486

  • [9] Taha, A.A., Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC medical imaging, 15(1), 29

  • [10] Schick, A., Fischer, M., Stiefelhagen, R. (2014). An evaluation of the compactness of superpixels. Pattern Recognition Letters, 43, 71-80

  • [11] Schick, A., Fischer, M., Stiefelhagen, R. (2012, November). Measuring and evaluating the compactness of superpixels. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 930-934). IEEE

Image Processing & Communications

The Journal of University of Technology and Life Sciences in Bydgoszcz

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