在本論文中我們結合多解析度法（multiresolution method）與模糊c-means群聚法（fuzzy c-means clustering method）提出一種新的MRI影像分割方法。首先在影像的前處理方面，我們用多解析度法將腦部T1、T2影像在不破壞影像結構並減低雜訊的情況下，轉換為較低解析度的影像。分別以處理過後的T1、T2影像的像素值為一特徵平面上x、y軸的值。再用模糊c-means群聚法將該特徵平面進行群聚分類，各群聚分別用以代表特定組織。最後將分類結果依多解析度法的性質還原成與原始輸入影像同解析度並和原始影像比對，將雜訊部分篩選出來，再轉換成各組織的分割影像。為了得知群聚分類的效果，我們使用均勻數據函數（uniform data functional，UDF）作為分類效果的好壞依據。由UDF的值顯示出當我們將所設計的特徵平面作群聚分類時，最佳的群聚數目的值與組織的種類數相吻合。分割的結果顯示，與其他使用模糊c-means群聚法的分割方法相比，雜訊與分割效果有明顯改善。

In this thesis, a multiresolution method（or called pyramid-based method）in combination with fuzzy c-means clustering method （FCM）for 3D MRI segmentation is described. A pyramid of the image is first constructed and smoothed by using a low-pass filter. The pixel values of the second layer of the pyramid of the T1 and T2 images are viewed as the two axes of the feature plane. The fuzzy c-means algorithm is then applied to the feature plane. Each cluster represents one tissue. Finally, we transform the images back to the original resolution and compare the difference with the segmented original image based on the distance from the cluster center to sift the noise. To examine the cluster result, the uniform data functional （UDF）is used for measuring the quality of the FCM algorithm. It shows that the ideal number of the clusters is the same as the brain tissues. The segmentation result of this method is better then other FCM-based methods in terms of noise reduction.

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