Brain surface segmentation of Magnetic Resonance images of the fetus

Author

Ferrario, D. ; Bach Cuadra, M. ; Schaer, M. ; Houhou, N. ; Zosso, D. ; Eliez, S. ; Guibaud, L. ; Thiran, J.-Ph

Author_Institution

Signal Process. Labs. (LTS5), Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland

fYear

2008

fDate

25-29 Aug. 2008

Firstpage

1

Lastpage

5

Abstract

In this work we present a method for the image analysis 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 fetus. 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 neighborhood from multiple volumes in the MRF step. Preliminary results on four fetuses of different gestational ages will be shown.

Keywords

Gaussian processes; Markov processes; biological tissues; biomedical MRI; brain; image segmentation; mixture models; obstetrics; FGMM; MRF step; MRI; Markov random field scheme; adapted energy computation; brain surface segmentation; extended neighborhood; fetuses; finite Gaussian mixture model; gestational ages; image segmentation; magnetic resonance imaging analysis; mixture voxel redistribution; multiple volumes; nonbrain tissue; two-step approach; Abstracts; Brain; Fetus; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Psychiatry;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2008 16th European

Conference_Location

Lausanne

ISSN

2219-5491

Type

conf

Filename

7080427