Interactive Neural Network Texture Analysis and Visualization for Surface Reconstruction in Medical Imaging
Artificial Neural Networks, Cluster Analysis, Texture Analysis, Magnetic
Resonance Imaging, Subspace Mapping, Visualization of Multidimensional
Feature Spaces, Tissue Classification, 3D-Reconstruction, Marching Cubes,
The paper describes a new approach for the automatic segmentation and tissue
classification of anatomical objects such as brain tumors from magnetic
resonance imaging (MRI) data sets using artificial neural networks. These
segmentations serve as an input for 3D-reconstruction algorithms. Since MR
images require a careful interpretation of the underlying physics and
parameters, we first give the reader a tutorial style introduction to the
physical basics of MR technology. Secondly, we describe our approach that is
based on a two-pass method including non-supervised cluster analysis,
dimensionality reduction and visualization of the texture features by means of
nonlinear topographic mappings. An additional classification of the MR data
set can be obtained using a post-processing technique to approximate the Bayes
decision boundaries. Interactions between the user and the network allow an
optimization of the results. For fast 3D-reconstructions, we use a modified
marching cubes algorithm but our scheme can easily serve as a preprocessor for
any kind of volume renderer.
The applications we present in our paper aim at the automatic extraction and
fast reconstruction of brain tumors for surgery and therapy planning. We use
the neural networks on pathological data sets and show how the method
generalizes to physically comparable data sets.
Proceedings of the EUROGRAPHICS '93, Computer Graphics Forum, Vol.12, No.3,pp.C49-C60, (1993)
A PostScript version, compressed with gzip, can be found here.
Last modified: Wed June 25 1997 11:49:44
For comments or questions about this page, please send me email.