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研究生:孫靜儀
研究生(外文):Jing-Yi Sun
論文名稱:利用空間分叢演算法自動分割腹部磁振造影影像中的脂肪含量
論文名稱(外文):Automatic Abdominal Fat Segmentation in MagneticResonance Images Using Spatial Clustering Algorithm
指導教授:吳杰
指導教授(外文):Jay Wu
學位類別:碩士
校院名稱:中臺科技大學
系所名稱:放射科學研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:89
中文關鍵詞:脂肪影像分割磁振造影k-means 分叢影像不均勻
外文關鍵詞:magnetic resonance imagingsegmentationabdominal fat
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高血壓、動脈硬化、第二型糖尿病等新陳代謝症候群與腹部脂肪含量有關,因此,正確的分析腹部脂肪含量為監測代謝性症候群之重要指標。本研究設計一套全自動化腹部脂肪分割演算法,應用於T2加權腹部磁振造影影像(magnetic resonance image, MRI)以分析腹部皮下脂肪與內臟脂肪含量。自動分割演算法依序執行下列步驟,首先利用新型模糊c平均(Modified Fuzzy c-means, MFCM)演算法校正不均勻磁場;接著使用邊緣函數Canny演算法製作體遮罩,藉由k平均叢集(k-means cluster)演算法取得脂肪組織與非脂肪組織;最後一個步驟利用體遮罩與脂肪組織做布林邏輯運算以分割腹部脂肪。利用線性迴歸分析(simple linear regression)與布蘭德-奧特曼(Bland-Altman)評估手動與自動分割方法的關聯性、一致性與相對百分誤差。SAT與VAT的分析結果顯示,手動與自動分割的皮爾森相關係數分別為0.999(p < 0.05)與0.997(p < 0.05)。除此之外,布蘭德-奧特曼分析顯示自動分割與手動分割的一致性。皮下脂肪之百分誤差為-1.7% ~ 2.1%以及內臟脂肪為-4.2 % ~ 5.2%。本研究所設計的自動分割演算法可有效測量腹部脂肪分佈並可減少手動分割的誤差,因此,此演算法應可作為脂肪計量分析的研究工具,進一步可提升新陳代謝相關疾病之診斷率。
Metabolic syndrome, such as hypertension, arteriosclerosis, and type-two diabetes, are related with the amount of abdominal fat. Thus, calculation of the abdominal fat provides an important index for preventing metabolic diseases. This research was to measure abdominal adipose tissues for T2-weighted magnetic resonance imaging (MRI) by applying an accurate unsupervised method. The proposed automatic procedures were divided into the following steps. First process was the image inhomogeneity correction using the Modified Fuzzy C-means (MFCM). Second step was to creation the body masks using Canny edge algorithm. Third, the adipose tissue and non-adipose tissue was detected by k-means cluster algorithm. Final process was the abdomen fat segmentation by the body mask and the non-adipose tissue mask. The simple linear regression and Blot-Altmen plot were used to analyze and compare the consistency and correlation between the manual and automatic segmentation methods. The Pearson correlation coefficients for SAT and VAT between manual and automatic segmentations were 0.999 (p < 0.05) and 0.997 (p < 0.05), respectively. The Blot-Altmen plot showed that manual and automatic segmentations were consistent for SAT and VAT. The percent error for SAT and VAT were -1.7% ~ 2.1% and -4.2% ~ 5.2%, respectively. The proposed algorithm can obtain the abdominal fat distribution efficiently and can lower the error of manual segmentation. Therefore, this approach could be used as an effective tool for quantitative research. Furthermore it could be used to prevent the metabolic related diseases.
1 前言 1
1.1研究背景 1
1.2研究目的 1
1.3論文架構 2
2 文獻回顧 3
2.1人體的化學組成 3
2.2脂肪的功能 4
2.3肥胖與新陳代謝症候群 5
2.4分析脂肪之方法 8
2.4.1身體測量法(anthropometry) 9
2.4.2生物電阻抗(bioelectric impedance analysis, BIA) 10
2.4.3水底稱量法(underwater weighing) 11
2.4.4皮摺量度(skinfold measures) 11
2.4.5雙能量X光儀器(dual energy x-ray absorptiometry, DEXA) 12
2.4.6質子磁振頻譜(proton magnetic resonance spectroscopy, proton MRS) 12
2.4.7電腦斷層掃描(computed tomography, CT) 12
2.4.8磁振造影(magnetic resonance image, MRI) 13
2.5影像來源與分析 13
2.5.1影像來源:核磁共振掃描(magnetic resonance image, MRI) 13
2.5.2影像分析法 15
2.6 全自動分割之相關文獻 19
3 理論 23
3.1 k-means分叢演算法 23
3.2 Modified Fuzzy c-Means(MFCM) 24
4 材料與方法: 28
4.1 實驗材料 28
4.1.1 影像取得與資料 28
4.1.2 MR參數 28
4.2 實驗方法 29
4.2.1 感興趣範圍圈選 31
4.2.2 磁場不均勻校正 34
4.2.3 遮罩製作 35
4.2.4 脂肪組織與非脂肪組織 37
4.3 手動分割 41
4.4 統計分析 44
4.4.1 簡單線性回歸 (simple liner regression model) 45
4.4.2 Bland-Altman Plot 46
4.4.3 變異係數與組內相關係數 46
5 結果 48
5.1 自動分割與手動分割之簡單線性回歸 48
5.2.1 性別 51
5.2.2 年齡 51
5.2.3 BMI 53
5.3 脂肪於不同性別、年齡與BMI之分佈情形 54
5.4 不同部位之單張影像與體積比較 57
5.5 組間差異與組內差異 58
6 討論 59
6.1 手動分割與自動分割之數據分析討論 59
6.2 分割數據與他人的比較 60
6.2 磁振造影頻譜HASTE 64
6.3 邊緣偵測 65
6.4 使用MFCM與不使用MFCM之間的差異 66
6.5 手動分割與自動分割之客觀及主觀 69
6.6 結論 70
7 未來展望 71
8 參考文獻 72
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