臺灣博碩士論文加值系統

　　現今社會中人們對外觀越來越重視，顱顏修復手術不僅是為了完美的外觀，也是為了建立人們的自信心；透過不同的顱顏手術修整臉部缺陷，如正顎手術。但大部分臨床醫師在顱顏修復過程中缺少三維的觀點評估病人，仍然使用測顱術(cephalometry)評估臉部特徵；測顱術是一種描繪顱顏正面或側面型態的方法，但基本限制在於測顱術僅能做二維的評估，若以二維觀點評估將容易缺少重要的資訊。
　　唇腭裂(cleft lip and palate)是最普遍的先天性顱顏異常，在亞洲地區每500到600名新生兒就會有一名患者產生；勒福一式(Le Fort I)截骨手術是唇腭裂治療程序其中一項矯正術式，本論文以勒福一式的軟硬組織比率數據對唇腭裂患者的臉部缺陷做手術模擬。
對醫師與病患來說，若能在術前準確預測軟組織的改變，將會對手術有很大的幫助。本論文以軟硬組織相對關係數據為依據，並強調臉部的上顎與下顎部位，在本論文開發系統當中以顱顏特徵點的方式重建軟組織三維模型。
　　本論文提出空間位移校正法(STCM)讓醫師能調整臉部特徵點部位，並將這個方法結合至系統，如顱顏手術規劃：透過在系統中模擬勒福一式截骨手術，計算處理後獲得預測的術後軟組織結果並顯示，使得醫師與病患將更清楚術後顱顏患者的外觀改變。

　　Human face needs facial surgery are not only a quest for perfection, but also in major part for establishment of self-esteem by corrective operations, e.g., orthognathic surgery to improve facial esthetic. Most of current clinical practices lack three-dimensional(3D) perspectives of the patient in that the craniofacial physicians employ the cephalometry to assess features, e.g., craniofacial surface and bones. Cephalometry is used to depict the craniofacial morphology of a side, frontal or lateral. But the inherent limitation of cephalometry is two-dimensionality(2D). Besides, a 2D feature on a cephalometric view lacks some precise information.
　　Cleft lip and palate is the most common congenital craniofacial anomalies. It happens in about every 500 to 600 new born babies in Asia. Le Fort I is a adjective procedure to rectify such defects. It is important in clinical practices to accurately predict the changes of soft tissue for both the physician and the patient. In this work, we further the studies in the relativity of soft-and-hard tissues, with emphasis in maxilla and mandible areas of the face. We incorporate the feature-based soft-tissue reconstruction into the developed 3D modeling system.
　　In this paper, we propose a spatial translation- calibration and modeling (STCM) method, which the physicians can adjust the feature points of the face; i.e., planning of the craniofacial surgery, and the developed system can automatically determine estimated result of the Le Fort I osteotomy. The user of the developed system, i.e., the physicians and the patients, are then allowed to observe the approximate profiles of the craniofacial rectification well before surgery.

第一章 序論 4
1.1 研究背景 4
1.2 研究動機 4
1.3 研究目的 4
1.4 論文架構 4
第二章 文獻探討 4
2.1 影像前置處理 4
2.1.1 影像濾波器 (Image Filter) 4
2.1.2 形態學處理 (Morphological Processing) 4
2.2 影像切割 4
2.2.1 植基於閥值的切割法 (Threshold-Based) 4
2.2.2 植基於邊界的切割法 (Edge-Based) 4
2.2.3 植基於群域的切割法 (Region-Based) 4
2.3 三維模型成像 4
2.3.1 表面重構法 (Surface Rendering) 4
2.3.2 體積重構法 (Volume Rendering) 4
2.3.3 正面與側面兩張人臉影像重建三維模型 4
2.4 顱顏影像量測與校正 4
2.4.1 測顱術 (Cephalometry) 校正 4
2.4.2 分散與對稱法 (Distraction and Symmetry) 4
2.4.3 切面調整法 4
2.5 臨床上常用之顱顏影像分析軟體 4
2.5.1 Amira 4
2.5.2 i-CAT 4
2.5.3 Simplant 4
第三章 研究方法 4
3.1 系統流程與總論 4
3.2 影像切割與三維模型重建 4
3.2.1 閥值裝箱切割法 (Binning Thresholding) 4
3.2.2 具容忍性的閥值切割法 (Thresholding with Tolerance ) 4
3.2.3 三維模型重建 4
3.3 三維模型校正 4
3.4 三維軟組織模型術後預估 4
第四章 系統實作與結果 4
4.1 系統平台架構 4
4.2 影像取得與系統軟硬體需求 4
4.3 唇腭裂病患案例分析 4
4.3.1 三維模型重建方法比較分析 4
4.3.2 唇腭裂患者校正與術後軟組織預測探討 4
第五章 結論與未來發展 4
第六章 參考書目及文章 4
附錄A (軟硬組織比率數據資料) 4
附錄B (生醫影像處理平台程式架構說明) 4

1. John G. Clement and Murray K. Marks, “Computer-Graphic Facial Reconstruction,” Elsevier Academic Press, Burlington, 2005.
2. Z. H. Cho, J. P. Jones, and M. Singh, Foundations of Medical Imaging, John Wiley & Sons, 1993.
3. R. A. Robb, and C. Barillot, “Interactive display and analysis of 3-D medical images,” IEEE Transactions on Medical Imaging, vol. 8, no. 3, pp. 217-226, sept.1989.
4. Noordhoff Craniofacial Foundation,“唇腭裂--病症介紹,” http://www.nncf.org/face/treat_01.htm
5. M. Ewing and R. B. Ross, “Soft Tissue Response to Orthognathic Surgery in Persons,” The Cleft palate-craniofacial journal, 1991.
6. A. Waheidi and Harradine, “Soft Tissue Profile Changes in Patients with Cleft Lip and Palate Following Maxillary Osteotomies,” Cleft Palate-Craniofacial Journal, vol. 35 No. 6, November 1998.
7. R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Reading, MA: Prentice-Hall, 2002.
8. M. J. Carlotto, “Histogram analysis using scale-space approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol9, no. 1, pp. 121-129, 1987.
9. N. Kanopoulos, N. Vasanthavada, R. L. Baker, “Design of an Image Edge Detection Filter using the Sobel Operator,” IEEE Journal of Solid-State Circuits, vol. 23, no. 2, pp. 358-367, April, 1988.
10. M. S. Su, C. Y. Chen and K. Y. Cheng, “The Reconstruction of 3D Head Model from Two Orthogonal-View 2D Face Images.” National Computer Symposium 2001, Taiwan, D320-D329, 2001.
11. O. Burgert et al., “A System for Facial Reconstruction using Distraction and Symmetry Consideration,” Institute of Real-Time Computer Systems & Robotics, Germany.
12. W. Schroeder, K. Martin and B. Lorensen, “The Visualization Toolkit: An Object-Oriented Approach To 3-D Graphics,” Prentice-Hall, Englewood Cliffs, NJ, 1996.
13. W. Schroeder, “The VTK User’s Guide,” Kitware, Inc. May, 2001.
14. C. H. Hou, “Modeling and Reconstruction of 3D Rat”, Master thesis, Computer Science and Information Engineering Dept., Univ. of Chung-Gung, Tao-Yuan, Taiwan, 2005.
15. C. Y. Liao, “Facial Modeling and Animation based on Muscle and Skull”, Master thesis, Computer Science and Information Engineering Dept., Univ. of Tsing-Hua, Hisn-Chu, Taiwan, 2002.
16. Y. C. Yu and John y. Chiang, “Human Facial Animation Based on Real Image Sequence,” Master thesis, Computer Science and Information Engineering Dept., Univ. of Sun Yat-sen, Kaohsiung, Taiwan, May, 2002.
17. S. Avidan and A. Shamir, “Seam Carving for Content-Aware Image Resizing,” ACM Transactions on Graphics, vol. 26, no. 3, Siggraph, 2007.
18. Y. T. Chen and C. S. Chen, “Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages,” IEEE Transactions on image processing, vol. 17, no. 8, August, 2008.
19. Q. Zhang and I. Couloigner, “Accurate Centerline Detection and Line Width Estimation of Thick Lines Using the Radon Transform,” IEEE Transactions on image processing, vol. 16, no. 2, February 2007.