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研究生:簡新府
研究生(外文):Hsin-Fu Chien
論文名稱:以傅利葉不變性為基礎之醫療影像校準
論文名稱(外文):A Medical Image Registration Method Based on Fourier Invariance
指導教授:鄭為民鄭為民引用關係
指導教授(外文):Wei-Min Jeng
學位類別:碩士
校院名稱:東吳大學
系所名稱:資訊科學系
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:2004
畢業學年度:92
語文別:中文
論文頁數:54
中文關鍵詞:正子放射斷層掃描核磁共振造影影像融合醫療影像校準解剖性新陳代謝
外文關鍵詞:PETMRIFusionmedical imageregistrationanatomicalmetabolic
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在解剖性影像中包括平面X-光照影、CT 及 MRI 影像,解析度相當高,可查覺細微的解剖細節的變化,而偵測到微小的病灶,但也常常因為缺乏功能或代謝的資訊,無法判別病灶的性質。核子醫學的功能及代謝影像檢查,就是利用各種核醫藥物來評估病灶的功能及代謝,以達到更正確的診斷,目前已成為疾病診斷的一項利器。正子放射斷層掃描就是核子醫學的功能及新陳代謝的斷層掃描造影。因此在CT、MRI、PET各個平台上所得影像是彼此互補。
影像融合是把在不同時間 、地點用不同影像系統,所拍攝的同一物體 ,做校準並且重疊顯示。多樣式醫療影像之合成是為了提供醫師較為完整之病人資料 ,彌補了目前單一樣式影像系統之不足。正子放射斷層掃描只提供功能性新陳代謝的資訊 ; 電腦斷層掃描及核磁共振造影只提供解剖性的資訊。所以 , 在研究和臨床上 , 解剖性與功能性醫療影像之整合有急迫性的需求。本論文應用傅利葉描述子之平移、旋轉、縮放大小等不變量之特性來作影像校準 ,主要目的是要把正子放射斷層掃描、核磁共振與電腦斷層掃瞄的物體 ,全部影像作一個影像融合。在本質上 ,影像融合就是影像校準的問題。將不同的影像系統,找出外圍輪廓,取得傅利葉描述子的相等,再依據傅利葉級數所產生的參數來達到醫療影像校準的目的。此結果將幫助醫師更有利於診斷病人的病灶。然而實驗數據證明,我們的研究方法之計算效率頗佳,並獲取令人滿意的結果。
Anatomic images include X-rays, CT and MRI images, which can detect minute focus on the basis of high resolution and the ability to detect fine changes of anatomic details. However, they often cannot determine the nature of focus due to the lack of information on function or metabolism. The functional and metabolic image examination of nuclear medicine is to evaluate the function and metabolism of focus with various nuclear drugs so as to make the diagnosis more accurate, and is now becoming an efficient tool in diagnosing diseases. Positron emission tomography (PET) is the tomographic imaging of function and metabolism of nuclear medicine. Therefore, the resulting images from CT, MRI and PET are complementary.
Fusion is to take the images of the same object taken by different image system at different time and location for registration and overlap display. Composition of multiple medical images is to provide physicians complete information on patients, and to offset the insufficiency of the single image system currently available. PET only provides functional metabolic information; CT and MRI only provide anatomical information. Therefore, in researches and clinical practice, the integration of anatomical and functional medical image calls for urgent attention. This paper uses the invariance of Fourier descriptor, such as linearity, rotation, and scaling. The purpose is to integrate images from PET, MRI, and CT onto one fusion. Fusion is indeed a problem with image registration. Different image systems are used to identify the image outline and obtain equal Fourier descriptor. Then use the parameters generated from Fourier series for medical image registration. The results are beneficial to physicians on diagnosis. The experimental data proves that the efficiency of the computation adopted in the research method is satisfactory.
目錄 I
圖目錄 III
表目錄 V
1. 緒論 1
1.1. 研究動機與目的 1
1.2. 問題背景陳述 2
2. 文獻探討 4
2.1. 以轉換和標記為基礎的影像校準做法 4
2.2. 影像輔助校正各種3D轉換的作法 5
2.3. 基本特徵的影像校正方法 6
2.4. 利用明顯標記的影像校正方法 7
2.5. ICP(Iterative Closest Point)演算法之探討 10
2.6. 影像校準之資料表示法 11
2.7. 傅利葉不變量之特性 12
3. 研究方法 13
3.1. 以傅利葉轉換為基礎的特質 13
3.1.1. FFT invariant 簡介 13
3.2. 封閉曲線之討論 14
3.2.1. 起始點 16
3.2.2. 平移 16
3.2.3. 旋轉 17
3.2.4. 縮放的比例變化 17
3.3. 方法比較 20
3.4. 影像校準示範圖例之概述 23
4. 實驗結果 24
4.1. 影像檔案格式 24
4.2. 鍊碼及MATLAB之介紹 26
4.3. CT平台之影像 28
4.4. MRI平台之影像 35
4.5. PET平台之影像 38
4.6. CT及MRI作影像融合 40
4.7. CT及PET作影像融合 42
4.8. 不完整輪廓圖之討論 47
4.8.1. 不封閉輪廓處理之探討 49
5. 結論與未來研究方向 51
6. 參考文獻及書目 52
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