臺灣博碩士論文加值系統

在腦神經外科的臨床應用上，腦部圖譜一直是重要的輔助工具。對於可見病灶(如腫瘤)的治療，病人在手術前戴上具有刻度的框式頭架，藉由電腦斷層(CT)或磁振造影(MR)來檢視其病灶的所在及其結構，藉由三維影像重建技術，進行可見病灶座標之定位。但是對於不可見病灶，則需藉由醫生在臨床上的經驗以及腦部圖譜的輔助，方能順利完成，然而由於經解剖而描繪的圖譜，並不一定完全吻合於所有的病人，且尺度的不一致性，使得醫師只能憑經驗推斷病灶所在的位置，如此一來，不僅增添手術的危險性，也使得微創手術的自動化難以實現。
在本論文中，建立一腦部立體定位、對位以及影像融合之系統，用於輔助醫生於腦部之臨床手術。本系統主要之架構如下：
(1)腦部圖譜三維資料之重建。
(2)CT影像之定位，使得CT影像定位到手術用之框架座標。
(3)CT影像與腦部圖譜之對位，根據病人之腦部結構，將圖譜資料進行平移、旋轉及延伸收縮之動作對位到病人之CT影像。
(4)將圖譜資料與CT影像進行融合
(5)提供框架上探針實際路徑規劃，對應至腦部圖譜，以期能達到手術自動化之目的。
藉由本系統，利用圖譜與CT影像融合之技術，不僅可以縮短搜尋不可見病灶的時間，更可增加手術的精確度及安全性。

In the clinical application of brain surgery, stereotactic surgery is the tool for the deep brain operation based on localization apparatus. Recently, using a probe from outer brain to the specific target to do some treatment is safe and becomes a new medical resource-reduced technology. In this brain surgery, brain atlas always be an important and auxiliary tool. Because the size of one’s brain is different from others, a surgeon just can conjecture the surgical target by his experience. The danger of surgical operation is increased and the automation is hard to do.
In this thesis, we combined the patient’s CT image and SW brain atlas, and used the technique of 3-D localization, coordinates registration and image fusion to create a stereotactic system. This system can help a surgeon on a brain mini-invasive procedure and disease diagnosis.
In summary, the following tasks have been accomplished in the thesis.
(1)3D reconstruction of brain atlas.
(2)3D localization of patient’s brain CT image.
(3)Registration of brain atlas and CT image.
(4)Fusion of brain atlas and CT image.
(5)Surgical path planning.
This stereotactic system can provide the accurate location of targeted tissue for driving the probe to the optimal treatment position. The success of this scheme is helpful in automating the surgery system and improving the quality and efficiency of the surgery.

第一章序論
1-1 研究動機及目的
1-2 背景
1-3 系統流程
1-4 論文架構
第二章腦部圖譜三維重建與修正
2-1 圖譜輪廓內插
2-2 圖譜之三維修正
第三章腦部CT影像立體定位及其與圖譜對位
3-1 CT影像立體定位演算法
3-2 圖譜與CT影像對位與座標轉換
第四章腦部CT影像分割
4-1 DICOM影像
4-2 Fuzzy C-Means演算法
4-3 決定初始質心
4-4 自動決定分割數目
第五章實驗結果及系統之應用
5-1 腦部組織之立體重建結果
5-2 腦部組織之修正
5-3 腦部CT影像分割
5-4 腦部CT影像與圖譜的融合
5-4.1 融合腦部組織於CT影像
5-4.2 融合CT影像於RW腦部圖譜
5-4.3 融合結果之三維顯示
5-5 路徑規劃
第六章結論參考文獻

[1] Bruce A. Kall, Patrick J. Kelly, Stephan Goerss, and Gideon Frieder, “Methodology and clinical experience with computed tomography and a computer-resident stereotactic atlas,” Neurosugery, vol.17, no3, pp.400-407, 1985.
[2]Lennar Thurfjell, Christian Bohm, Torgny Greitz and Lars Eriksson, “Transformation and algorithms in a computerized brain atlas,” IEEE transactions on nuclear science, vol.40, no.4, pp.1187-1191, August, 1993.
[3]G. Schaltenbrand, W. Wahren, “Atlas for stereotaxy of the human brain,” 2nd Ed., G Thieme, 1977.
[4]Donald Hearn, M. Pauline Baker, Computer Graphics, 2nd Ed., Prentice Hall, Inc, 1994.
[5]W.L. Chang, “The 3-D reconstruction of serial cross-sectional medical image by wavelet transform,” Master Thesis, Chang Gung University, 1998.
[6]J.M. White, and G.D. Rohrer “Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction.” IBM J. Res. Devel., vol. 27, no.4, pp. 400-411, 1983.
[7]K.S. Fu, and J.K. Mui “A Survey of Image Segmentation.” Pattern Recog., vol. 13, no. 1, pp.3-16, 1981.
[8]S. A. Shafer and T. Kanade, Color vision, Encyclpedia of Artificial Intelligence, S. C. Shapiro and D. Eckroth, Eds, pp. 124-131, Wiley, New York, 1987.
[9]Y.W. Lim and S.U. Lee, “On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques” Pattern Recognition, vol. 23, no.9, pp.935-952, 1990.
[10]Scott E. Umbaugh, Randy H. Moss, William V.Stoecker, Gregory A. Hance, “Automatic Color Segmentation Algorithms with Application to skin tumor feature identification,” IEEE Engineering in Medicine and Biology, pp. 75-82, September, 1993.
[11]Jianqing Liu, Yee-Hong Yang, “Multiresolution color image segmentation”, IEEE Transaction on Pattern Analysis And Machine Intelligence, vol. 16,No.7, July, 1994.
[12] M. Borsotti, P. Campadelli, R. Schettini, “Quantitative evaluation of color image segmentation results.” Pattern Recognition Letters vol 19, pp. 741-747, 1998.
[13]Willian E. Lorensen, “Marching cubes: a high resolution 3d surface graphics,” Computer graphics, vol. 21, no.4, pp.163-169, July, 1987.