Hybrid wavelet/neural network analysis of differences in functional brain images

Functional neuroimaging is a powerful biological tool to investigate the regions of the brain responsible for performing different mental functions. The traditional interpretation method is statistical analysis in the spatial domain, which is computationally expensive. We present a hybrid wavelet/neural network scheme to analyze functional brain images. Features are extracted in the wavelet domain and then fed to a neural network for detection. The proposed method is examined by exploring the differences between positron emission tomography (PET) images acquired under different experimental conditions during a tone recognition task. The performance shows its potential in the fast developing functional neuroimaging area