Segmentation of Cerebrospinal Fluid from 3D CT Brain Scans Using Modiﬁed Fuzzy C-Means Based on Super-Voxels
Abdelkhalek Bakkari, Anna Fabijańska
Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 809–818 (2015)
Abstract. In this paper, the problem of segmentation of 3D Computed Tomography (CT) brain datasets is addressed using the fuzzy logic rules. In particular, a new method which combines Fuzzy C-Means clustering and the idea of super-voxels is introduced. Firstly, the method applies the extended Simple Linear Iterative Clustering (SLIC) method to divide image into super-voxels, which are next clustered by Modified Fuzzy C-Means algorithm. The method deals with 3D images and performs fully three dimensional image segmentation. Ten samples are supplied proving that our Modified Fuzzy C-Means (MFCM) together with super-voxels are apt to take into account a large diversity of special domains that appear and which are inappropriate solved adopting classical Fuzzy C-Means approach.The results of applying the introduced method to segmentation of the CerebroSpinal Fluid (CSF) from the brain ventricles are presented and discussed.