Mri Segmentation Using Supervised And Unsupervised Methods

F'altern recognition techniques were used for segmentation of MR images; the classification was based on multi-spectral image intensities. Segmentation of image data into tissue types was done using two supervised artificial neural network algorithms, back-propagation and cascade correlation, and an unsupervised clustering algorithm, fuzzy c-means. Input data consisted of TI-weighted, T2-weighted. and spin-density-weighted images. The segmentation was based on the pixel intensities in each image. With the supervised algorithms, cluster analysis was also run using interslice and interpatient training data. Tissue classification was presented as colorcoded anatomically mapped images.