Automatic Vector Seeded Region Growing for Parenchyma Classification in Brain MRI
Chuin-Mu Wang, Ruey-Maw Che · InTech eBooks · 2012
Nuclear magnetic resonance (NMR) can be used to measure the nuclear spin density, the interactions of the nuclei with their surrounding molecular environment and those between close nuclei, respectively. It produces a sequence of multiple spectral images of tissues with a variety of contrasts using several magnetic resonance parameters. 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 [1-3] are hence employed to collect spectral information for classification and of clinically critical values. In this paper, a new classification approach was proposed, it is called unsupervised Vector Seeded Region Growing (UVSRG). The UVSRG mainly select seed pixel vectors by means of standard deviation and relative Euclidean distance. Through the UVSRG 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 show that the proposed approach is a promising and effective technique for MR image classification.