Name
Title | ||
|---|---|---|
Segmentation of cerebrospinal fluid from 3D CT brain scans using modified Fuzzy C-Means based on super-voxels |
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.15439/2015F154 | FedCSIS |
DocType | Volume | ISSN |
Conference | 5 | 2300-5963 |
Citations | PageRank | References |
0 | 0.34 | 22 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdelkhalek Bakkari | 1 | 0 | 0.34 |
| Anna Fabijańska | 2 | 33 | 7.30 |