Two - Stage Neural Network For
نویسندگان
چکیده
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 diierential geometrical invariant features. The invariant feature pattern is then assigned to a speciic region using a two-stage neural network system. The rst 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 eeective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which automatically clusters the input vector into diierent 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.
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