2-STAGE NEURAL-NETWORK FOR VOLUME SEGMENTATION OF MEDICAL IMAGES

A new system to segment and label CT/MRI brain slices using feature ex traction and unsupervised clustering is presented. Each volume element (voxel) is assigned a feature pattern consisting of a scaled family o f differential geometrical invariant features. The invariant feature p attern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal compone nts analysis (SOPCA) network that is used to project the feature vecto r 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) wh ich automatically clusters the input vector into different regions. A 3D connected component labeling algorithm is then applied to ensure re gion connectivity. We demonstrate the power of this approach to volume segmentation of medical images. (C) 1997 Elsevier Science B.V.