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Tissue Segmentation of Brain MR Images by Using Hierarchical Classifiers
|Authors: ||Jian-An Lin|
|Contributors: ||NTOU:Department of Communications Navigation and Control Engineering|
MR image;independent component analysis;K-means
|Issue Date: ||2011-11-25T07:43:41Z
|Abstract: ||許多腦部病變的診斷皆可使用磁共振(MR)影像觀察，尤其是灰質 和白質組織會隨著不同疾病產生體積的變化，過去憑藉著專業醫師的 知識與經驗判別，但若能發展出腦組織分類的演算法，勢必會對醫學 的預防與治療上有所幫助。腦部各部分組織的特性相近不容易有效分 離，本論文研究的主旨即為將容易病變的灰質和白質組織分離出來。 本論文以高頻譜影像技術開發兩階層式的腦組織分類法，第一階 段採用非監督式的分類，先將影像用獨立成份分析(ICA)強化其特徵， 再透過群聚法K-means 分類。由第一階段的分類結果顯示，腦內部灰、白質分別與某外殼組織被視為同一類。為了有效除去外殼組織，第二階段使用監督式的分類法，針對第一階段的分類結果包含灰、白質的類別中再進行細部分類。模擬結果顯示腦內灰質、白質組織能有效與外殼組織分離。由實驗數據證明，階層式MR 影像的腦組織分類法能有效提昇腦內灰質與白質組織的分類效果，比僅使用第一階段分類法提高20%TI 值的正確率，並可提供醫學診斷的輔助。|
Currently, many brain lesions or diseases are diagnosed by observing the magnetic resonance (MR) images. Especially, the volume of gray matter and white matter tissue in brain is changed with different diseases. In contrast to the brain diseases diagnosed according the the physician’s knowledge and experience, the development of the brain tissue classification algorithm could provide a valuable medical help in prevention and treatment of diseases. However, the grayscale value of the various parts of brain tissue in MR images is close to and difficult to be separated effectively. The study is mainly on the segmentation of the gray and white matter tissue in brain by using MR images. The thesis developed a two-stage classification of brain tissues by using multispectral image processing. The classification procedure in the first stage is non-supervised, which applies independent component analysis (ICA) in enhancing the image features, and brain tissues are categorized through K-means clustering method. The classification results in the first stage show that the gray or white matter tissues within brain are recognized as the same class as other brain skull organization, respectively. In order the promote classification accuracy, it is essential to remove the brain skull organizations from MR image before the classification of gray or white matter tissues. In the second stage, a supervised classification is proposed to further categorize the classes which recognized as gray or white matter from the classification results in the first stage. Simulation results demonstrate that gray matter or white matter tissues within brain can be separated effectively form brain shell organizations. The proposed two-stage classification of brain tissues verified by the experimental data, can increase around 20% TI values of accuracy in the gray matter and white matter tissue classification compared the those results using only the first stage in classification. Furthermore, the developed classification method in this research can provide auxiliary in medical diagnosis. Keywords: MR image, independent component analysis, K-means.
|Appears in Collections:||[通訊與導航工程學系] 博碩士論文|
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