本論文旨在開發－套裝電腦輔助系統，應用於MR影像腦部組織分割，以期正確執行之腦部灰、白質體積衡量，而有助於腦部定量形態學之研究。在標準合成MR影像的實驗結果發現，使用隸屬於多頻譜影像分析技術之獨立成份分析(Independent Component Analysis, ICA)方法搭配「支援向量器(Support Vector Machine, SVM)」之演算法，能有效分割MR影像腦部組織，再配合「分水嶺演算法(Watershed Algorithm)」去除非腦組織後，在背景雜訊為0%和3%等兩種MR影像中，其灰/白質分割效果量化指標(Tanimoto Index)分別為0.82/0.89和0.73/0.80，結果優於過去文獻其他技術的報告。
In this paper we develop a computer-aided brain tissues classification system for MR images and hence make possible for more accurate volume measurement on gray/white matter, and cerebrospinal fluid. A correct classification of brain tissues is an important step in quantitative morphological study of brain. From the synthetic brain MR images experiment, it shows that using multispectral image processing technique, independent component analysis (ICA), and coupling with support vector machine (SVM) method can effectively classify brain tissues for brain MR images. In addition, we also demonstrated that the best performance can be achieved by using watershed algorithm as a pre-processing method for striping non-brain tissues. The Tanimoto index of GM/WM in synthetic MR images with noise level 0% and 3% are 0.82/0.89 and 0.73/0.80, separately which shows better performance than those what we have seen in the literatures.
