Title :
Segmentation of cerebrospinal fluid from 3D CT brain scans using modified Fuzzy C-Means based on super-voxels
Author :
Abdelkhalek Bakkari;Anna Fabijańska
Author_Institution :
Lodz University of Technology, Insitute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924, Poland
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 Cerebro-Spinal Fluid (CSF) from the brain ventricles are presented and discussed.
Keywords :
"Three-dimensional displays","Image segmentation","Clustering algorithms","Feature extraction","Computed tomography","Biomedical imaging","Brain"
Conference_Titel :
Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on