Magnetic Resonance Imaging (MRI) has become a useful modality because it provides unparallel capability of revealing soft tissue characterization as well as 3-D and has become an efficient instrument for clinical diagnoses and research in recent years. When tissues are classified by means of MRI, the images are multi-spectral. Therefore, if only a single image with a certain spectrum is processed, the goal of tissue classification will not be achieved because the single image can't provide adequate information. Consequently, it is necessary to integrate the information of all the spectral images to classify tissues. Multi-spectral image processing techniques are hence employed to collect spectral information for classification and of clinically critical values. This paper presents a new classification approach, it is called Vector Seeded Region Growing (VSRG). The VSRG mainly select seed pixel vectors by means of standard deviation and relative Euclidean distance. Through the VSRG processing, the data dimensionality of MRI can be decreased and the desired target of interest can be classified which the brain tissue and brain tumor segmentation. A series of experiments are conducted and compared to the commonly used c-means method for performance evaluation. The results indicate the possible usefulness of this method for MR image classification. (21 refs)
