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

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：6

、訪客IP：18.204.56.185

姓名

羅振洲( Chen-chou Lo) 查詢紙本館藏

畢業系所

電機工程學系

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
(Automatic Segmentation of Oral Magnetic Resonance Image Based on Shape Prior Active Contour Model and Fuzzy C-means Clustering)

相關論文

★ 獨立成份分析法於真實環境中聲音訊號分離之探討	★ 口腔核磁共振影像的分割與三維灰階值內插
★ 數位式氣喘尖峰氣流量監測系統設計	★ 結合人工電子耳與助聽器對中文語音辨識率的影響
★ 人工電子耳進階結合編碼策略的中文語音辨識成效模擬--結合助聽器之分析	★ 中文發聲之神經關聯性的腦功能磁振造影研究
★ 利用有限元素法建構3維的舌頭力學模型	★ 以磁振造影為基礎的立體舌頭圖譜之建構
★ 腎小管之草酸鈣濃度變化與草酸鈣結石關係之模擬研究	★ 口腔磁振影像舌頭構造之自動分割
★ 微波輸出窗電性匹配之研究	★ 以軟體為基準的助聽器模擬平台之發展-噪音消除
★ 以軟體為基準的助聽器模擬平台之發展-回饋音消除	★ 模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響
★ 用類神經網路研究中文語音聲調產生之神經關聯性	★ 教學用電腦模擬生理系統之建構

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ Image Segmentation
★ MRI
★ Level Set Method
★ GVFS
★ Shape Prior
★ Fuzzy Clustering
★ Active Contour Model

論文目次

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

參考文獻

Ahmed, M.N. (2002). “A modified fuzzy c-means algorithm for bias field estimate and segmentation of MRI data”, IEEE Transaction on Medical Imaging, Vol. 21, 193-199.

Bade, R., Haase, J. & Preim, B. (2006). “Comparison of fundamental mesh smoothing algorithm for medical surface models”, Conference on Simulation and Visualization, Vol. 6, 289-304.

Bezdek, J.C., Ehrlich, R. & Full, W. (1984). “FCM: The fuzzy c-means clustering algorithm”, Journal of Computers & Geosciences, Vol. 10, 191-203.

Caselles, V., Kimmel, R. & Sapiro, G. (1997). “Geodesic active contours”, Journal of Computer Vision, Vol. 22, 61-79.

Cates, J.E., Lefohn, A.E. & Whitaker, R.T. (2004). “GIST: an interactive, GPU-based level set segmentation tool for 3D medical images”, Journal of Medical Image Analysis, Vol. 8, 217-231.

Chan, T.F. & Vese, L.A. (2001). “Active contours without edges”, IEEE Transactions on Image Processing, Vol. 10, 266-277.

Chan, T.F. & Zhu, W. (2005). “Level set based shape prior segmentation”, Conference on Computer Vision and Pattern Recognition, Vol. 2, 1164-1170.

Chen, Q., Sun, Q.S. & Xia, D.S. (2013). “Serial slice image segmentation of digital human based on adaptive geometric active contour tracking”, Journal of Computers in Biology and Medicine, Vol. 43, 635-648.

Chuang, K.S., Tzeng, H.L., Chen, S. & Wu, J. (2006). “Fuzzy c-means clustering with spatial information for image segmentation”, Journal of Computerized Medical Imaging and Graphics, Vol. 30, 9-15.

Cohen, L.D. (1991). “On active contour models and balloons”, Conference on Computer Vision , Graphics, and Image Processing, Vol. 53, 211–218.

Cohen, L.D. & Cohen, I. (1993). “Finite-element methods for active contour models and balloons for 2-D and 3-D images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, 1131-1147.

Cremers, D., Sochen, N. & Schnorr, C. (2003). “Towards recognition based variational segmentation using shape prior and dynamic labeling”, Conference on Scale Space Methods in Computer Vision, Vol. 2695, 388-400.

Gonzalez, R.C. & Woods, R.E. (2002). Digital image processing 2/e, Pearson Education Prentice Hall.

Huang, C.Z., Yan, B., Jiang, H. & Wang, D. (2008). “MR Image segmentation based on fuzzy c-means clustering and the level set method”, Conference on Fuzzy Systems and Knowledge Discovery, Vol. 1, 67-71.

Jinda-apiraksa, A., Ong, S.H., Hiew, L.T., Foong, K.W.C. & Kondo, T. (2009). “A segmentation technique for maxillary sinus using the 3-D level set method”, Conference on TENCON, 1-6.

Kass, M., Witkin, A. & Terzopoulos, D. (1988). ”Snakes: active contour models”, Journal of Computer Vision, Vol. 1, 321-331.

Kapur, J.N., Sahoo, P.K. & Wong, A.K.C. (1980). “A new method for grey-level picture thresholding using the entropy of the histogram”, Journal of Signal Processing, Vol. 2, 223-237.

Leventon, M.E., Grimson, W.E.L. & Faugeras, O. (2000). “Statistical shape influence in geodesic active contours”, Conference on Computer Vision and Pattern Recognition, Vol. 1, 316-323.

Li, C., Xu, C., Gui, C. & Fox, M.D. (2005). ? Level set evolution without re-initialization: a new variational formulation”, Conference on Computer Vision and Pattern Recognition,, Vol. 1, 430-436.

Li, B.N., Chui, C.K., Chang, S. & Ong, S.H. (2011). “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation”, Journal of Computers in Biology and Medicine. Vol. 41, 1-10.

Liu, X., Langer, D.L., Haider, M.A., an der Kwast, T.H., Evans, A.J., Wernick, M.N. & Yetik, I.S. (2009). “Unsupervised segmentation of the prostate using MR images based on level set with a shape prior”, Conference on Engineering in Medicine and Biology Society, 3613-3636.

Malladi, R., Sethian, J. & Vemuri, B. (1995). “Shape modeling with front propagation: A level set approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, 158-175.

Mumford, D. & Shah, J. (1989). “Optimal approximations by piecewise smooth function and associated variational problems”, Communications on Pure and Applied Mathematics, vol. 42, 577-685.

Osher, S.J. & Sethian, J.A. (1988). “Fronts propagation with curvature
dependent speed: Algorithms based on Hamilton–Jacobi formulations”, Journal of Computational Physics, Vol. 79, 12–49.

Otsu, N. (1979). “A thresholding selection method from gray-level histograms”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, 62-66.

Synder, W., Bilbro, G., Logenthiran, A. & Rajala, S. (1990). “Optimal thresholding - A new approach, Journal on Pattern Recognition Letters”, Vol. 11, 803-809.

Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, E. & Willsky, A. (2003). “A shape-based approach to the segmentation of medical imagery using level sets”, IEEE Transactions on Medical Imaging, Vol. 22, 137-154.

Xu, C., & Prince, J.L. (1998). Snakes, shapes, and gradient vector flow, IEEE Transactions on Image Processing, Vol. 7, 359-369.

Zhou, H., Schaefer, G., Sadka, A.H. & Celebi, M.E. (2009). “Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images”, Journal of Selected Topics in Signal Processing, Vol. 3, 26-34.

王菘義，(民96) 以磁振造影為基礎的立體舌頭圖譜之建構，國立中央大學電機系碩士論文。

吳漢哲，(民93) 口腔核磁共振影像的分割與三維灰階值內插，國立中央大學電機系碩士論文。

陳俊凱，(民99) 構音障礙的治療原則與技巧，特殊教育與輔助科技，第六期，第19- 23頁。

簡村誠，(民98) 口腔核磁影像舌頭構造之自動分割，國立中央大學電機系碩士論文。

指導教授

吳炤民(Chao-min Wu)

審核日期

2014-1-27

推文