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

Abdelkhalek Bakkari, Anna Fabijańska


In this paper, the problem of segmentation of 3D Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) brain datasets 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.


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