Segmenting the images obtained from magnetic resonance imaging (MRI) is an important process for visualization of the human soft tissues. For the application of MR, we often have to introduce a reasonable segmentation technique. Neural networks may provide us with superior solutions for the pattern classification of medical images than the conventional methods. For image segmentation with the aid of neural networks of a reasonable size, it is important to select the most effective combination of secondary indices to be used for the classification. In this paper, we introduce a vector quantized class entropy (VQCCE) criterion to evaluate which indices are effective for pattern classification, without testing on the actual classifiers. We have exploited a newly developed neural tree classifier for accomplishing the segmentation task. This network 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 on, the number of training data for each node decreases, which results in less weight update epochs and decreases the time consumption. The partitioning of the feature space at each node is done by a simple neural network; the appropriateness of which is measured by newly proposed estimation criterion, i. e. the measure for assessment of neuron (MAN). It facilitates the obtaining of a neuron with maximum correlation between a unit's value and the residual error at a given output. The application of this criterion guarantees adopting the best-fit neuron to split the feature space. The proposed neural classifier has achieved 95% correct classification rate on average for the white/gray matter segmentation problem. The performance of the proposed method is compared to that of a multilayered perceptron (MLP), the latter being widely exploited network in the field of image processing and pattern recognition. The experiments show the superiority of the introduced method in terms of less iterations and weight up dates necessary to train the neural network, i. e. lower computational complexity; as well as higher correct classification rate.