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標題: 使用限制能量最小化法偵測MRI影像的腦部病變部位
Brain Lesions Detection Using Constrained Energy Minimization for MRI
作者: 黃健揚
Huang, Jian-Yang
關鍵字: MRI;MRI;限制能量最小化 (Constrained Energy Minimization, CEM);腦部病變偵測;Constrained Energy Minimization;CEM;brain lesion detection
出版社: 電機工程學系所
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本篇論文提出一種方法,以限制能量最小化法來突顯白質病變的分布位置。由於突顯後的影像為灰階影像,不是二值化影像,所以在合成影像的部分以2D Tanimoto的方法找出其最佳的Threshold;而在實際影像的部分則是預先設定一個Threshold對結果影像做二值化的處理。最後再比較於一般情況下以及執行過維度擴充後的偵測效率,結果發現,無論是在合成影像上的不同雜訊等級中的偵測狀況或者是在實際影像上的偵測表現,執行過維度擴充之後均能大幅提升其偵測效率。

One of the fundamental tasks of MRI is tissue classification. White matter lesions are small areas of dead cells found in parts of the brain that act as connectors. This thesis presents a method that uses constrained energy minimization (CEM) approach to highlight the location of white-matter lesions. The highlighted image is the gray level image rather than binary image therefore we use a 2D Tanimoto index to find the optimal threshold in the brain web image. A threshold should be selected prior to be used in the real image. Experimental results demonstrate that the CEM significantly improve over RBF kernels in SVM-classification. Most importantly, CEM should also joint with band expansion process (BEP) to offers a great increase in detection performance.
其他識別: U0005-0608201316495600
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