系統識別號 U0026-1308201413021700
論文名稱(中文) 應用EMD於擴散張量造影作精神分裂症特徵量化
論文名稱(英文) The Application of EMD to Diffusion Tensor Imaging for Schizophrenia Characterization
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 何其祐
研究生(英文) Chi-Yu Ho
學號 P86011113
學位類別 碩士
語文別 英文
論文頁數 55頁
口試委員 指導教授-鄭國順
中文關鍵字 擴散張量造影  精神分裂症  經驗模態分解  本質模態函數 
英文關鍵字 Diffusion tensor imaging  schizophrenia  empirical mode decomposition  intrinsic mode function 
中文摘要 精神分裂症是一種慢性、嚴重的精神疾病。近年來,精神分裂症中白質神經的連結性更成為了研究的重要議題,而擴散張量造影(DTI)則提供了在研究白質變化上的有效技術。在分析方法上,全腦分析方法相較於傳統ROI方法雖然提供了一種較客觀的分析方式,但在某些步驟如影像對位與平滑卻不免喪失資訊,為了改善全腦分析的缺點,我們發展出一套基於經驗模態分解(EMD)與濾波反投影的特徵擷取系統。在本篇研究我們探討了經驗模態分解於應用時所遇到的問題,如三次樣條、邊界效應與混模問題,並提出改善的方法。藉由這套系統,我們可以取得帶有不同生理意義的本質模態函數(IMF)影像,然後結合Tract-based spatial statistics 的全腦分析方法,探討經過不同本質模態函數增強後的影像,會對分析結果有何影響。從結果發現,較高頻的本質模態函數如IMF1與IMF2能增強較細小的神經管束,並協助我們找出原本方法學難以發現的白質區域。在本研究中,我們發現在經由我們方法於大腦中的穹窿、左側下縱束、左側上縱束等區域,能夠找出原始方法所無法發現之區域。而低頻成分如IMF3與IMF4,則是增強白質主要神經束,但統計結果並未與原本方法學有太大不同。總結來說,本方法能提取不同特徵並加強特定資訊於全腦分析方法上,提供另一種有別於傳統分析精神分裂症白質異常的方法。
英文摘要 Schizophrenia is a chronic, severe brain illness. In recent years, connectivity of nerve in white matter becomes an important issue in schizophrenia and DTI have been proved as a powerful technique on white matter. Although, the voxelwise analysis method can provide a more objective method comparing to the ROI method, it would lost the information when do the steps, such like registration and smoothing. To improve the shortcomings in voxelwise analysis, we developed a feature characterization system for image based on empirical mode decomposition (EMD) and filtered back projection. In this study, we discussed the problems in EMD such like cubic spline, boundary effect and mode mixing and proposed the modified method. With the proposed system, we can obtained the intrinsic mode function (IMF) images respectively, which is thought to contain different physical meanings in different IMFs. Then, we combined with TBSS and observed the IMF enhanced image would result in any effect on this method. From results, we found that the high frequency components, such like IMF1 and IMF2 can enhance the minor tract information and help us find the abnormal region in schizophrenia which is hard to find in traditional method. In this study, we found that the abnormal regions in fornix, left inferior longitudinal fasciculus, left superior longitudinal fasciculus which were hard to find in traditional method. The low frequency components, such like IMF3 and IMF4 enhance the major white matter tract, but the statistic results were almost same as traditional method. In summary, our method can extract the feature and enhance the specific information on the voxelwise analysis which can provide another way to analyze the abnormality area in schizophrenia different from traditional way.
論文目次 Contents
Chapter 1. Introduction...1
1.1 Introduction...1
1.1.1 Schizophrenia...1
1.1.2 Grey matter and white matter...3
1.1.3 Diffusion tensor imaging (DTI)...3
1.2 Literature review...7
1.3 Motivation and Purpose...9
Chapter 2. Materials and Methods...11
2.1 Participants...11
2.2 Imaging parameters...12
2.3 Imaging preprocessing...13
2.4 Hilbert-Huang transform (HHT)...14
2.4.1 Intrinsic mode function (IMF)...14
2.4.2 Empirical mode decomposition (EMD)...15
2.5 Radon transform...16
2.6 Filtered back projection...17
2.7 Combination of EMD and filtered back projection...20
2.8 Problems and improvement of EMD...24
2.8.1 Cubic spline...24
2.8.2 Boundary effect...25
2.8.3 Mode mixing...28
2.9 Tract-based spatial statistics (TBSS)...33
2.10 Statistics...36
Chapter 3. Results and Discussion...38
3.1 Software system...38
3.2 FA skeletons created by original and different IMF enhanced image...41
3.3 Statistic results...44
3.4 Discussion...48
Chapter 4. Conclusion and Prospect...50
4.1 Conclusion...50
4.2 Prospect...51
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