Two-stage neural network for volume segmentation of medical images
A new system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering is presented. Each volume element (voxel) is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which automatically clusters the input vector into different regions. A 3D connected component labeling algorithm is then applied to ensure region connectivity. We demonstrate the power of this approach to volume segmentation of medical images. (C) 1997 Elsevier Science B.V.
neural networks principal component analysis image segmentation