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Image Processing & Communications

The Journal of University of Technology and Life Sciences in Bydgoszcz

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2300-8709
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Features Determination from Super-Voxels Obtained with Relative Linear Interactive Clustering

Abdelkhalek Bakkari / Anna Fabijańska
Published Online: 2017-04-04 | DOI: https://doi.org/10.1515/ipc-2016-0017

Abstract

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.

Keywords: super-voxel-based segmentation; Relative Linear Interactive Clustering; SLIC super-pixels; volumetric image; super-voxels statistical features

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About the article

Published Online: 2017-04-04

Published in Print: 2016-09-01


Citation Information: Image Processing & Communications, ISSN (Online) 2300-8709, DOI: https://doi.org/10.1515/ipc-2016-0017.

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© 2016 Image Processing & Communications. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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