Using Edge Detection Methods to Resolve A Partial Volume Problem for Magnetic Resonance Brain Imaging
| Autor: | Chien-Nan Lin, 林建男 |
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| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 Support vector machine (SVM) has found great promise in magnetic resonance analysis recently. It works effectively depend on choosing proper parameter and kernel. However, there are two major challenge issues in SVM classification. One is the random select of training sample cause unstable and inconsistent result. It may lead to large variation from every experiment. The other is that classification errors via SVM usually distributed at the boundary between classes which is caused by partial volume effect. In this thesis, we have developed a new version of SVM, called iterative SVM (ISVM) that we could take only a few training samples to solve first issue. And using morphological or edge detection processing to deal with second issue, the idea is through above methods to find the boundary between classes. Then extract these areas from MR images for SVM to classify again. We combined above approaches with a preprocessing independent component analysis to improve SVM classification. And choose weighted RBF kernel instead of RBF kernel in SVM. The experimental results show the proposed method has great performance in magnetic resonance brain image classification. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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