Name
Abstract | ||
|---|---|---|
In this work we present a method for the image anal- ysis of Magnetic Resonance Imaging (MRI) of fetuses. Our goal is to segment the brain surface from multiple volumes (axial, coronal and sagittal acquisitions) of a fe- tus. To this end we propose a two-step approach: first, a Finite Gaussian Mixture Model (FGMM) will segment the image into 3 classes: brain, non-brain and mixture voxels. Second, a Markov Random Field scheme will be applied to re-distribute mixture voxels into either brain or non-brain tissue. Our main contributions are an adapted energy computation and an extended neigh- borhood from multiple volumes in the MRF step. Pre- liminary results on four fetuses of different gestational ages will be shown. |
Year | Venue | Field |
|---|---|---|
2008 | European Signal Processing Conference | Voxel,Computer vision,Scale-space segmentation,Coronal plane,Markov random field,Segmentation,Computer science,Artificial intelligence,Sagittal plane,Mixture model,Magnetic resonance imaging |
DocType | ISSN | Citations |
Conference | 2219-5491 | 0 |
PageRank | References | Authors |
0.34 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. Ferrario | 1 | 0 | 0.34 |
| M. Bach Cuadra | 2 | 47 | 4.47 |
| Marie Schaer | 3 | 62 | 4.87 |
| Nawal Houhou | 4 | 0 | 0.34 |
| Dominique Zosso | 5 | 273 | 16.60 |
| Stephan Eliez | 6 | 57 | 4.78 |
| L. Guibaud | 7 | 3 | 0.71 |
| Jean-Philippe Thiran | 8 | 2320 | 257.56 |