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研究生:吳炅儒
研究生(外文):Chiung-ju Wu
論文名稱:利用相關性頻譜擴張之獨立成分分析於多頻譜核磁共振顯影像之應用
論文名稱(外文):Correlation Based Band Expansion on Independent Component Analysis Applied to Multispectral Magnetic Resonance Images
指導教授:詹寶珠詹寶珠引用關係
指導教授(外文):Pau-choo Chung
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:47
中文關鍵詞:多頻譜影像核磁共振顯影像獨立成分分析維度擴張
外文關鍵詞:Band ExpansionMagnetic Resonance Image (MRI)Multispectral ImagesIndependent Component Analysis (ICA)
相關次數:
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獨立成分分析方法(Independent Component Analysis, ICA)原來是訊號處理
領域中,作為來分離盲信號(Blind Source Separation, BSS)的方法。然而最近亦應
用於核磁共振顯影像的分類上。在這篇論文中,我們應用獨立成分分析方法於多
頻譜的腦部及胸部核磁共振顯影像,作為腦部及胸部的組織分類。透過獨立成分
分析,各類的組織應從原來的頻譜影像中,被分類於各張成分影像。不過頻譜影
像的不足,可能造成多種組織混合於一張成分影像。
因此我們發展出一套藉由相關性的預測方法,得知頻譜影像的不足數目,
以幾可能混合在一起的組織。並且經由相關性來來進行維度擴張,以取得額外的
頻譜影像。經過獨立成分分析產生成分影像後,我們再應用等位階函數(Level Set)
方法來圈選最重要的區域。
Independent Component Analysis (ICA) is a Blind Source Separation (BSS)
method in signal processing field. And it is also applied in Magnetic Resonance Images
for classification. In this paper, we introduce ICA to multispectral brain MR images for
brain tissue classification. Through ICA, each brain tissue is supposed to be classified
into each component image from original band images. But deficiency of band images
would lead that more than one tissue are mixed in one component image.
We develop a prediction method by correlation to reveal how many band images
are deficient and which different tissues may be separated into one same component
image. Then extra band images are generated by Band Expansion Process (BEP) with
the nonlinear function we proposed. Then through ICA, the component images are
generated. At last, we introduce the level set method to indicate the most significant
section.
Chapter 1. Introduction ..................................................................................................1
Chapter 2. Independent Component Analysis..............................................................4
2.1 ICA Model ..............................................................................................................4
2.2 FastICA Algorithm .................................................................................................6
2.3 Implementation Architecture of Image...................................................................9
Chapter 3. Classification with ICA..............................................................................12
3.1 Previous Band Expansion Process........................................................................12
3.2 Number of Classes, Prediction by Correlation .....................................................13
3.3 Proposed Band Expansion Process .......................................................................14
3.4 Level Set Indication ..............................................................................................16
Chapter 4. Experiment Results ....................................................................................19
4.1 Brain Experiment Results .....................................................................................19
4.2 Breast Experiment Results....................................................................................31
Chapter 5. Conclusions .................................................................................................42
References ......................................................................................................................44
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