3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto t...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2015-02, Vol.150, p.16-25
Main Authors: Angelopoulou, Anastassia, Psarrou, Alexandra, Garcia-Rodriguez, Jose, Orts-Escolano, Sergio, Azorin-Lopez, Jorge, Revett, Kenneth
Format: Article
Language:English
Subjects:
3D surface reconstruction
Brain
Filtering
Filtration
Growing neural gas
Landmarking
Landmarks
Medical
Medical shapes
MRI
Networks
Reconstruction
Three dimensional
Voxel Grid
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Summary:In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.03.078