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研究生:謝玫琦
研究生(外文):Mei-Chi Hsieh
論文名稱:利用線性空間投影子及獨立元件分析技術從核磁共振影像的像素完成物質含量之偵測
論文名稱(外文):Linear Subspace Projectors and Independent Component Analysis Technique for Abundance Detection from MR images pixels
指導教授:章定遠
指導教授(外文):Din-Yuen Chan
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊工程學系
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:2002
畢業學年度:90
語文別:英文
論文頁數:52
中文關鍵詞:斜子空間投影法最大訊號和誤差比投影法獨立元件分析投影法
外文關鍵詞:OB ProjectorMSIR ProjectorIC Projector
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對於多維度的腦部核磁共振影像,特定的物質的含量偵測及組織分析方法是非常重要的。
在這篇論文中﹐我們建構了三種對於純粹的腦部物質含量的測量的投影子方法,按照預先所需的專家知識量的大小,依序遞減加以介紹。
第一種投影子方法是斜子空間投影法(OB Projector),第二種投影子方法是最大訊號和誤差比投影法 (MSIR Projector),第三種投影子方法是獨立元件分析投影法(IC Projector ),第三種投影子方法是在未知的情況下,也就是盲目的情形下是很適當的方法。
實驗的結果顯示,物質的圖素的含量若是沒有和另一個物質混合的狀況下,藉由 IC Projector 的方法是可以突顯出來的,尤其是當有異常組織或疾病徵候群存在的時候,所以﹐透過 IC Projector能有效地產生我們所要偵測的特徵影像,也可以有效的抽出影像裡的不正常的組織,並且發現疾病及併發症狀。

The abundance detection of specified materials in multispectral brain magnetic resonance (MR) images is very important for the tissue separation approach. For the detection applications from supervised to blind environments, the construction procedures of the oblique subspace projector (OB Projector), maximal signal-to-interference ratio projector (MSIR Projector) and the independent component analysis projector (IC Projector) were subsequently proposed. They are performed with the decreasing order of requirement for the prior medical knowledge. These proposed projectors are regarded as the linear unmixing operations for the MRI study such that the realizations of them are low complicated. Through the unmixing, the relative abundance of pure brain substances can be measured. And, the individual substance is separated from each other such that the efficiencies of these projectors are further qualitatively compared. In the simulations, the experimental results demonstrate that the three projectors are indeed feasible for the MRI applications. Particularly, as the abnormal tissues or pathological changes exist due to the lesions or diseases, the unmixing of partial volume pixels by IC Projector is salient over the others. Therefore, IC Projector, which is constructed by the unsupervised learning of Fast ICA algorithm to obtain its projecting vectors, can effectively facilitate the exploration of abnormality in MR images.

摘要 I
ABSTRACT II
ACKNOWLEDGEMENTS III
CONTENTS IV
LIST OF FIGURES VI
Chapter 1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 2
1.3 Organization 3
Chapter 2 THE PHYSICS OF MAGNETIC RESONANCE IMAGING 5
2.1 Introduction 5
2.2 The Basic Principles of MRI 7
2.3 The Concept of Generating MR Images 9
2.4 Significant MRI Parameters 12
Chapter 3 OB PROJECTOR and MSIR PROJECTOR 15
3.1 Linear Mixture Model 15
3.2 Oblique Subspace Projector 17
3.3 MSIR Projector 20
Chapter 4 UNSUPERVISED PROJECTOR FROM ICA 23
4.1 The Mixture Model Statement for ICA 23
4.2 IC Projector for Signatures Extraction 25
EXPERIMENTAL RESULTS 27
CONCLUSION 38
REFERENCES 39

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