Automatic labeling of brain tissues in MRI using an encoder-segmented neural network

Quantitative estimation of tissue labeling heavily depends on the efficiency of image segmentation technique. In this paper, an encoder-segmented neural network was proposed to improve the efficiency of image segmentation. The features are ranked according to the encoder indicators by which the insignificant feature vector will be eliminated from the original feature vectors and the important feature vectors can be re-organized as the encoded feature vectors for the subsequent clustering. ESNN developed can improve the exist FCM algorithm in feature extraction and the cluster's number selection. This method was successfully implemented automatic labeling of tissue in brain MRIs. Examples of the results are also presented for diagnosis of brain using MR images.