Neural trees for image segmentation

In this paper we report the application of neural trees for image segmentation of magnetic resonance (MR) images. The network, built up during training, effectively partitions the feature space into subregions and each final subregion is assigned a class label according to the data routed to it. As the tree grows, the number of training data for each node decreases, which results in less weight update epochs and decreases the time consumption. A key point in the proposed algorithm is choosing the right neuron to divide the data. A general training coefficient set for all tree nodes may offer poor division. A different training coefficient should be chosen in order to gain purity of classes. A set of candidate-neurons is installed and the winner is taken to fill the node space in our tree. The network performance is compared to the multilayer perceptron (MLP) over the white/gray matter MRI segmentation problem