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研究生:羅振洲
研究生(外文):Chen-chou Lo
論文名稱:基於先驗形狀主動輪廓模型及模糊分群之口腔磁振影像自動分割
論文名稱(外文):Automatic Segmentation of Oral Magnetic Resonance Image Based on Shape Prior Active Contour Model and Fuzzy C-means Clustering
指導教授:吳炤民
指導教授(外文):Chao-min Wu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:178
中文關鍵詞:影像分割磁振影像等位函數法梯度向量流蛇模型先驗形狀模糊分群主動輪廓模型
外文關鍵詞:Image SegmentationMRILevel Set MethodGVFSShape PriorFuzzy ClusteringActive Contour Model
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為了建構人體真實大小的舌頭模型以及了解舌頭肌肉分佈,本研究利用影像分割演算法來自動分割口腔磁振影像中舌頭的部分,再將舌頭二維影像建構為三維舌頭模型。為了改善先前研究對於口腔磁振影像的問題,本研究依據先前研究之建議於等位函數法中結合先驗形狀法與模糊分群法。每組個案之首張切面先經由模糊分群法處裡,使得影像中灰階對比增加進而讓等位函數演化較容易;非首張切面之初始輪廓則利用前張切面之輪廓結果來自動計算初始輪廓,並且透過先驗形狀等位函數法讓輪廓演化至目標邊界,最後利用梯度向量流蛇模型平滑化輪廓,達到全自動地對口腔磁振影像分割出舌頭構造。結果評估是將本研究結果與專家手動分割結果做比較,主要評估方法有百分比差、均方根差以及相似係數,其中以相似係數對於分割結果較靈敏,而八組個案結果之平均相似係數值為0.898,顯示本研究自動分割結果對於專家手動分割結果有很高的相似度,而分割結果之三維重建舌頭影像外型上與手動分割之結果亦大致相同。本研究成功地利用模糊分群法有效提升等位函數對於首張切面的分割結果以及利用前張分割結果作為先驗形狀,並且透過自動計算初始輪廓來提高等位函數法的分割結果。
In this study, we applied image segmentation algorithm to automatically segment the tongue contour from the oral magnetic resonance images (MRI) in order to construct a three dimensional (3-D) tongue in real human size and to study the anatomical structures of tongue muscles and reconstruct these 2-D slice results into a 3-D tongue. Based on the suggestion of the previous study from our laboratory, we adopted shape prior and fuzzy clustering knowledge into level set algorithm for solving the problems of previous research. We enhanced the pixel contrast of the first slice of each subject with fuzzy clustering to let level set contour evolve easier. For each non-first slice, we calculated the initial contour from the segmented tongue contour of the previous slice, and the segmented tongue contour of the previous slice also worked as the shape prior energy term to improve the current contour evolution. After contour evolutions, we used gradient vector flow snake to smooth the contour, and achieved automatic segmentation of oral MRIs. We evaluated the results of this study with the ground truth of tongue with the similarity index, percentage of difference and root mean square error. The similarity index is more sensitive to the accuracy of the segmented results among other evaluation methods, and the average similarity index of 8 subjects was 0.898 which indicated the similarity of the segmented results of this study is quite promising when compared to the ground truth, and the shape of the reconstructed 3-D tongue is similar to the one segmented with manual approach. This study used fuzzy clustering could improve the segmented results of level set for the first slice of each subject and the segmented tongue contour of the previous slice as a shape prior term successfully, and also calculated initial contour automatically to enhance the result of original level set method.
目錄
摘要 I
Abstract II
誌謝 IV
圖目錄 IX
表目錄 XIV
第一章 緒論 1
1.1 研究動機 1
1.2 文獻探討 4
1.2.1參數式主動輪廓模型 4
1.2.2幾何式主動輪廓模型 5
1.2.3 先驗形狀知識 8
1.2.4模糊分群 10
1.3研究目的 12
1.4論文架構 13
第二章 主動輪廓模型 14
2.1.參數式主動輪廓 14
2.2幾何式主動輪廓 20
2.2.1 不用重初始化的等位函數 24
2.3形狀先驗法 27
第三章 演算法架構與數值方法 32
3.1 磁振影像以及軟硬體規格說明 32
3.2 演算法流程與初始輪廓選擇 34
3.3 先驗形狀等位函數法之數值方法與其參數選擇 40
3.3.1 先驗形狀等位函數法之數值方法 40
3.3.2 先驗形狀等位函數法之實驗參數選擇 42
3.4 梯度向量流蛇模型數值方法與其參數選擇 53
3.4.1 梯度向量流蛇模型之數值方法 53
3.4.2 梯度向量流蛇模型之實驗參數 54
3.5 模糊分群法之介紹與其數值方法 60
3.5.1 硬式c均值 60
3.5.2 模糊c均值 62
3.5.3 模糊c均值之數值方法與影像應用 63
3.6 評估方法 66
第四章 研究結果與評估 72
4.1 女生個案1之分割結果與評估說明 72
4.2 全部個案分割結果分析 77
4.2.1 女生個案3評估結果分析 79
4.2.2 分割較差切面分析 85
第五章 研究結果討論 89
5.1模糊分群與先驗形狀對於等位函數法之影響 89
5.1.1加入模糊分群法之影響 90
5.1.2加入先驗形狀法之影響 91
5.2之前研究之改良與討論 94
5.2.1舌頭肌肉與下巴骨內側中心接觸之地方 95
5.2.2舌頭連接牙齒的部分 96
5.2.3舌頭擺放位置偏移的部分 97
5.3 二維切面與三維模型討論 99
5.3.1二維切面討論 99
5.3.2三維模型討論 102
5.4利用手動分割結果作為首張切面來運算 107
5.5本研究演算法對於其他器官之分析 111
5.6其他演算法對於本研究個案之分析 113
5.6.1 C-V模型 113
5.6.2 C-V模型結合Li模型 115
第六章 結論與未來展望 117
6.1 結論 117
6.2 未來展望 118
參考文獻 120
附錄 A 124
附錄 B 128
附錄 C 133
附錄 D 135

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