臺灣博碩士論文加值系統

目的：以區域成長演算法為基礎發展出參數調整簡單的影像分割演算法，並針對本演算法特性設計互動式編輯工具，同時將演算法與工具加以整合，提供完整的分割程序給予使用者使用。

背景簡介：醫學影像分割是電腦輔助診斷與電腦輔助手術中不可或缺的影像處理程序，但醫學影像具有高雜訊、低對比與幾何形狀複雜的特性，因此目前為止沒有演算法可以完美的處理所有醫學影像分割問題。雖然越複雜的演算法可以更有效的分割影像，但是其內部參數也越難調整，因此我們想減少使用者的負擔，發展出便於使用的方法。

方法：本研究使用邊緣資訊當作區域成長的成長條件，並對每階段區域成長的邊界進行一維高斯平滑濾波。此外我們針對演算法的特性設計了補洞、剪裁、一定區域向內成長等的工具，以利使用者分割影像及調整分割結果。

結果：將演算法與編輯工具整合後，依照分割流程在CT影像以及MR影像進行測試，其結果在邊緣資訊良好的位置有不錯的分割效果，但在軟組織等對比度不明顯的區域，分割效果較差，但整體而言，改善了傳統區域演算法對雜訊敏感的問題。

結論：我們成功的發展出一種使用邊緣資訊的區域成長演算法，並整合編輯工具與影像處理工具，依照設計過的分割流程，循序漸進的提供工具給予使用者使用，減少非工程背景使用者在使用上的困難。

Object. Two aims will be achieved in the present study: 1) to develop an edge-based region growing algorithm for medical image segmentation, which has advantage of instinctive parameters adjusting; 2) to construct a framework integrated with the newly developed segmentation algorithm and an interactive image editing tool for practical application.

Background. Medical image segmentation is an important image procedure for computer aided surgery and computer aided diagnosis. It is always a difficult issue to develop an algorithm for segmentation because medical images are characterized with high noise, low contrast and complex geometry. Algorithm often improves their performance in medical segmentation at the cost of increasing difficulties in internal parameters adjustment. Therefore, it is necessary to develop a user-friendly segmentation framework which also comprises the good ability to segment medical images.

Method. The edge information of medical image is used as growing criterion. The boundary of grown regions was smoothed by 1D Gaussian filter at the end of each growing process. In addition to the image segmenting algorithm, interactive tools such as patching, cutting and inward region growing were developed on the basis of algorithm characteristics. To verify the efficiency of the developed algorithm and the interactive editing tools, they were applied to segment CT images and MR images.

Result. The performance of algorithm was better on the regions with sufficient edge information than the regions surrounded with low contrast of soft tissue. The present developed algorithm had lower sensitivity to noise than classical region growing algorithms did.

Conclusion. The region growing algorithm with good performance of medical image segmentation as well as instinctive parameters adjustment was successfully developed. Furthermore, the framework integrated with the developed algorithms and interactive editing tools can provides users with step-by-step instructions to process medical image. Therefore , users without engineering back ground can also use it easily.

圖 次......................................I
第一章 導論.....................................1
1-1醫學影像分割簡介...............................1
1-2 研究目的......................................1
1-3 文獻回顧......................................2
第二章 使用邊緣資訊的區域成長演算法...............3
2-1基本概念.......................................3
2-2 一維高斯平滑濾波..............................4
2-3 邊緣檢測......................................5
2-4 搜尋邊界的連續性質............................8
2-5 在人造影像上的結果...........................11
第三章 區域演算法編輯工具.......................13
3-1對比度擴展....................................13
3-2單點向外成長..................................14
3-3 一定區域往內成長.............................14
3-4剪裁工具......................................15
3-5 補洞工具.....................................16
第四章 醫學影像分析結果..........................17
4-1 使用流程.....................................17
4-2 在醫學影像上的應用...........................18
4-3 與傳統區域成長演算法的比較...................23
第五章 討論......................................25
5-1 參數的調整與影響.............................25
5-2 影像的分割效果 ...............................27
5-3 本演算法的限制 ...............................27
第六章 結論....................................29
參考文獻 ........................................30

[1]M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active Contour Models," International journal of computer vision, pp. 321-331, 1988.
[2]T. McInerney and D. Terzopoulos, "Topologically Adaptable Snakes," presented at Computer Vision, Cambridge, 1995.
[3]S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed:algorithms baded on Hamilton-Jacobi formulations," Journal of Computational Physics, vol. 79, pp. 12-49, 1988.
[4]M. I. Chowdhury and J. A. Robinson, "Improving image segmentation using edge information," presented at Electrical and Computer Engineering, Canada, 2000.
[5]T. Pavlidis and Y.-T. Liow, "Integrating Region Growing and Edge Detection," IEEE Transactions on pattern analysis and machine intelligence, vol. 12, pp. 225-233, 1990.
[6]J. Xuan, T. Adali, and Y. Wang, "Segmentation of magnetic resonance brain image: Integrating region growing and edge detection," presented at Image Processing, Washington, 1995.
[7]Y.-W. Yu and J.-H. Wang, "Image segmentation Based on Region Growing and Edge detection," presented at Systems, Man, and Cybernetics 1999.
[8]Y. Xiaohan and J. Yla-Jaaski, "Image segmentation combining region growing and edge detection," presented at Pattern Recognition, Conference C: Image, Speech and Signal Analysis, The Hague, 1992.
[9]Z. Xiang, Z. Dazhi, T. Jinwen, and L. Jian, "A Hybrid Method for 3D Segmentation of MRI Brain Images," presented at Signal Processing, 2002.
[10]C. Revol-Muller, F. Peyrin, Y. Carrillon, and C. Odet, "Automated 3D region growing algorithm based on an assessment function," Pattern Recognition Letters, vol. 23, pp. 137-150, 2002.
[11]E. J. Pauwels, P. Fiddelaers, and L. J. V. Gool, "Autonomous Grouping of Contour-Segments Using an Adaptive Region-Growing Algorithm," presented at Pattern Recognition 1996.
[12] Gonzalez, Woods, "Digital Image Processing 2/e", published by Prentice Hall,2002
[13]鍾國亮,"影像處理與電腦視覺",東華書局,2002.