改善多通道相位陣列線圈之腦部磁振造影像組織分類 ICA+SVM 績效之研究

Abstract

Since the multi-channel coil of magnetic resonance imaging (MRI) became a mainstream, methods to reduce inhomogeneity in MRI without using the body coil for additional scan have developed. The interference of signals made the appearance of MR image badly. As a result it serious affects the classifications for MR images. In this study we would find the most adaptable method to improve inhomogeneity for MRI and get improvement for brain tissue classification by using independent component analysis (ICA) and support vector machine (SVM) method under multiple-channel phase-array coil. We chose three inhomogeneity correction methods: discrete wavelet transform (DWT), local entropy minimize with B-spline (LEM-BS) and local entropy minimize with cubic spline (LEM-CS). In our experiment, these three methods were used as the pre-processing method before applying ICA + SVM to correct the inhomogeneity of MR image. Web site images are first applied in the experiment and the Tanimoto index was used to measure the performance for the three methods. Real phantom MR images were also applied in this experiment. The results show that the LEM-BS is the best choice. Instead of using the average filter for LEM-BS, we use Gaussian filter to get better classification result. The LEM-BS method can be applied not only in single slice but also in multiple slices and sometimes it shows better result.