Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8819
標題: 探討以結合獨立成份分析與K平均及模糊聚類方式做腦部切片分類之效能
Exploring the Effectiveness of Combining ICA with K-means and Fuzzy C-means in Brain Tissue Classification
作者: 吳孟謙
Wu, Meng-Chian
關鍵字: K-means;K-平均;Fuzzy C-means;Independent Component Analysis;Fisher Linear Discriminant Analysis;模糊C平均;獨立成分分析;費雪線性辨別分析
出版社: 電機工程學系所
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摘要: 
本論文將敘述兩種被廣泛應用的方法,稱為K-means與Fuzzy C-means,使多種影像可以被分類為二維影像。首先,我們使用獨立成份分析來強化原始影像中的成份以減少雜訊影響。但如果原始影像有雜訊成份,在做完K-means與Fuzzy C-means分類之後的影像都很明顯可以看出雜訊成份。我們利用數次Fisher’s Linear Discriminate Analysis來實現分類來得到最後結果。實驗結果將驗證我們提出的方法在非監督式分類中將可達到我們預期的效果。

This paper presents an application of using a widely used clustering algorithm, called k-means and fuzzy c-means to get several types of binary images for MR brain image classification. First, we use independent component analysis (ICA) to enhance the components of our original images to reduce the influence of noise. Then we use k-means or fuzzy c-means to get the first classified images. But if the original images have noise, k-means and fuzzy c-means can't deal with it very well. Fisher's linear discriminate analysis (FLDA) which performs classification iteratively to produce final results. The experimental results show the proposed approach has great promise in unsupervised classification.
URI: http://hdl.handle.net/11455/8819
其他識別: U0005-1307201018485200
Appears in Collections:電機工程學系所

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