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研究生:陳政煒
研究生(外文):Cheng Wei Chen
論文名稱:整合影像對位及標記法之帕金森式症評估系統
論文名稱(外文):Computer-Aided Evaluation System for Parkinson's Disease Using Image Registration and Labeling
指導教授:李建德李建德引用關係
指導教授(外文):Jiann Der Lee
學位類別:碩士
校院名稱:長庚大學
系所名稱:電機工程研究所
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:2007
畢業學年度:95
語文別:中文
論文頁數:54
中文關鍵詞:電腦輔助系統帕金森氏症影像對位組織標記
外文關鍵詞:computer-aided systemParkinson''s diseaseimage registrationtissues labeling
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近年來,利用99mTc-TRODAT-1顯影劑進行單光子電腦斷層掃描影像(SPECT),已被證實能有效評估多巴胺(Dopamine)神經元的健全程度。在臨床上,醫師利用核磁共振影像(MRI)與注入顯影劑所照射出來的SPECT影像進行帕金森氏症評估。由於傳統的評估程序,必須從三維影像中進行手動對位及框選欲評估組織,因此導致了醫師大量的時間與精力耗費。
針對這個原因,本論文建構一個全自動的帕金森氏症評估系統,其中包含MRI與SPECT之間的剛性對位演算法,以及一個全自動的MRI組織標記演算法。論文的重心除了建構此評估系統,亦利用多層次的演算法概念建立一個自動的感興趣區域選取機制。與傳統程序相比,本文所提之系統能有效評估帕金森氏症在臨床上的表現。
實驗結果證實,本文所提之系統能有效獲得帕金森氏症病患在臨床上的評估。透過電腦輔助的人機介面,醫生亦可將本系統的結果當作評估病徵時的初始建議,或另外手動微調更精細的結果。
Single photon emission computed tomography image (SPECT) of dopamine transporter with 99mTc-TRODAT-1 has recently been proposed to be a valuable and feasible means of assessing the integrity of dopamine neurons. In order to measure the specific-to-nonspecific binding ratio of the nuclear medicine within the specific-binding tissues, i.e. putamens and caudate nuclei, the corresponding MRI is needed to be registered to SPECT for bounding the regions of interest. Therefore, an automatic labeling algorithm which enables contouring the putamens and caudate nuclei is necessary because segmenting these tissues manually from MRI costs tons of time and energy of physicians. We have built a computer-aided clinical diagnosis system which integrates MRI/SPECT registration and MRI labeling for the evaluation of Parkinson’s disease. Clinical MRI and SPECT data including eighteen healthy subjects and thirteen patients were involved to validate the performance of the proposed system.
指導教授推薦書 i
口試委員審定書 ii
授權書 iii
誌謝 iv
中文摘要 v
英文摘要 vi
目錄 vii
圖目錄 ix
表目錄 xi
第一章 緒論 1
1.1研究背景 1
1.2研究動機與目的 3
1.3系統流程 4
1.4論文架構 5
第二章 MRI/SPECT剛性對位 6
2.1簡介 6
2.2相關文獻 7
2.3剛性對位演算法 7
2.4相似性量測方法 9
2.5最佳化演算法 12
2.6轉換函數 13
2.7影像內插 14
第三章 MRI組織分割 15
3.1簡介 15
3.2相關文獻 15
3.3 MRI組織分割演算法 17
3.3.1邊界導向的分割演算法 18
3.3.2圖譜導向的分割演算法 22
3.3.2.1仿射對位 24
3.3.2.2彈性對位 25
3.3.3本論文採用的理論模型 29
3.3.4多重解析度之VOI選取機制 31
第四章 實驗結果與分析 32
4.1 軟硬體設備及影像格式 32
4.1.1軟硬體設備 32
4.1.2影像格式及實驗資料 32
4.2 MRI/SPECT對位實驗 34
4.2.1黃金標準 34
4.2.2準確度驗證 35
4.3 MRI組織標記實驗 38
4.3.1黃金標準 38
4.3.2驗證標準 38
4.3.3以交互訊息為基礎的標記結果 39
4.3.4 VOI選取策略實驗 45
4.4 帕金森氏症臨床評估 48
第五章 結論與未來展望 50
參考文獻 51
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