簡易檢索 / 詳目顯示

回結果列表
研究生： 陳乙慶
Chen, Yi-Ching
論文名稱： 以黑板架構為基礎之影像辨識系統
An Image Recognition System Based on a Blackboard Architecture
指導教授： 陳立祥
Chen, Lih-Shyang
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年： 2008
畢業學年度： 96
語文別： 中文
論文頁數： 86
中文關鍵詞： 影像標籤 、知識源 、髕骨 、膝蓋骨 、黑板系統 、影像辨識
外文關鍵詞： Image label, Image recognition, Blackboard, Knee, Patella, Knowledge Source
相關次數： 點閱：145  下載：17 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 醫學影像分割是一件頗為複雜的工作，必須結合影像處理、電腦圖學及解剖學等多方面的知識才能加以完成。在本論文中，我們將提出如何利用影像處理的技術，配合專家系統中的黑板架構，將使用者所感興趣區域的輪廓線產生出來，並藉由整合本實驗室另一套3D立體成像的系統，提供使用者在系統做影像辨識及三維物件結果的展示。
    在影像辨識方面，我們將介紹針對膝蓋骨所採用的影像處理方法，來幫助我們找出正確的骨頭的輪廓線。除此之外，我們還提出如何對影像分割之後所產生的輪廓線作評估的方法，並利用顏色的不同來表示結果的好壞及讓使用者作最後輪廓線正確性的確認。
    在黑板架構方面，我們將介紹如何將影像及分割結果做集中且一致性的管理，並讓外界能透過相關的存取介面對存放在黑板資料結構內的資料作存取，另外我們還討論如何將影像分割的方法以知識源的形式呈現，並利用統一的介面和控制機制互動以完成影像辨識的工作。

    The segmentation of a medical image is an integrated task. We need to integrate the knowledge of image processing, computer vision and anatomy to complete the task. This thesis describes how to use the techniques of image processing with a blackboard architecture to generate the contours of the regions of interest. We also integrate another system, 3D Builder, to provide the interface for the users so that we can communicate with our system interactively and view the results of the 3-dimensional reconstruction during the process of recognition.

    As far as image processing is concerned, we will describe the segmentation methods for the bone of knee, for helping us find out the correct regions. Furthermore, we also describe the methods to evaluate the contours generated by the segmentation knowledge sources and use different colors to show the user about the results of the segmentation.

    About the blackboard architecture, we describe how to manage the image data and the segmentation results and provide the interfaces to access them. We also implement the segmentation methods in the form of knowledge sources and provide the interface to enable users to confirm the results of the segmentation.

    § 中文摘要 § ..........................................................Ⅰ § 英文摘要 § ..........................................................Ⅱ § 誌謝 § ..............................................................Ⅲ § 目錄 § ..............................................................Ⅳ § 圖表目錄 § ..........................................................Ⅵ 第1章 導論..............................................1 1.1 概述.................................................1 1.2 研究動機與目的.......................................3 1.3 章節提要.............................................4 第2章 背景..............................................6 2.1 醫學造影簡介.........................................6 2.2 影像分割與處理分析方法...............................8 2.3 黑板架構.............................................9 2.4 輪廓線評估..........................................11 2.5 以輪廓線為主的三維物件重建系統（3D Builder）簡介....12 第3章 系統架構與設計...................................14 3.1 系統需求分析........................................14 3.2 系統架構............................................15 3.3 組成元件及其功能介紹................................17 3.4 系統實作............................................19 3.4.1 黑板資料結構......................................19 3.4.2 知識源............................................23 3.5 系統工作流程........................................27 第4章 針對膝蓋骨進行KS的研究與實作.....................30 4.1 問題的特性..........................................30 4.2 影像分割知識源......................................32 4.2.1 Knee's KS(膝蓋骨).................................33 第5章 影像標籤.........................................62 5.1 問題的特性..........................................62 5.2 演算法..............................................63 5.2.1 分析與需求........................................63 5.2.2 演算法改進........................................63 5.3 3D場景中的物件標籤..................................68 5.4 考題模式............................................71 第6章 結論..............................................80 6.1 研究成果............................................80 6.2 未來發展方向........................................80 § 參考文獻 §...........................................................83 § 作者簡介 §...........................................................86

    [1] Hongyi Li, Deklerck, R. and Cornelis, J. ,” Integration of multiple knowledge sources in a system for brain CT-scan interpretation based on the blackboard model”, Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on , 1-4 March 1994, pp. 336-343.
    [2] Masaharu Kobashi and Linda G. Shapiro, “Knowledge-Based Organ Identification From CT Images”, Pattern Recognition, Vol. 28, No. 4, pp. 475-491,1995.
    [3] Lih-Shyang Chen, “A Distributed and Interactive Three-Dimensional Medical Image System”, Computerized Medical Imaging and Graphics, 1994.
    [4] Bernd Jahne, “Digital Image Processing” Fifth revised and extended Edition, Springer Inc., 2001.
    [5] Chun-Lung Lin, “An Interactive Image Recognition System Based on a Blackboard Architecture”, 2001.
    [6] Po-Yu Ke, “3D Reconstruction of human body system and interactive medical education game”, 2006.
    [7] Jing-MinYang, “The Applications of the Automatic Contour Recognition Mechanism on the Medical Images”, 2007.
    [8] E. Gamma, E. Helm, R. Johnson and J. Vlissides, “Design Patterns – Elements of Reusable Object-Oriented Software”, Reading, MA: Addison-Wesley, 1995.
    [9] F. Y. Kwan, K. C. Cheung, and I. Gibson, “Automatic Extraction of Bone Boundaries from CT Scans Using an Intelligence-based Approach”, Pattern Recognition, Vol. 3, pp. 437-440, Sept. 2000.
    [10] D. J. Michael and A. C. Nelson, “HANDX: A model-based system for automatic segmentation of bones from digital hand radiographs,” IEEE Trans. Med Image., vol 8, no. 1, pp. 64-69, 1989.
    [11] N. R. Pal and S. K. Pal, “A review of image segmentation techniques,” Pattern Recognit, vol. 26, no. 9 , pp. 1277-1294, 1993.
    [12] Z. Q. Liu, H. L. Liew, J. G. Clement, C. David, and L Thomas, “Bone image segmentation,” IEEE Trans. Biomed. Eng., vol 46, no. 50, pp. 565-573, 1999.
    [13] S. Baronti, A. Casini, and F. Lotti, “Variable Pyramid Structures for Image Segmentation,” Computer Vision, Graphics, and Image Processing 49, 346-356, 1990.
    [14] H. Li, R. Deklerck, B. D. Cuyper, A. Hermanus, E. Nyssen and J. Cornelis, “Object Recognition in Brain CT-Scans: Knowledge-Based Fusion of Data from Multiple Feature Extractors,” IEEE Transactions On Medical Imaging, Vol 14, No. 2, June 1995.
    [15] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision Second Edition, Cole Publishing Company 1998.
    [16] 鄭慶明，“實用醫學影像診斷學”，國立編譯館，11月，2001。
    [17] Shi-MingYang, “An Image Segmentation System Based on Blackboard Architecture”, 2001.
    [18] Hui Zhang*, Jason E. Fritts and Sally A. Goldman,“An Entropy-based Objective Evaluation Method for Image Segmentation”, Dept. of Computer Science and Engineeringm Washington University, One Brookings Drive, St. Louis, MO USA 63130.
    [19] Stanley B. Lippman, Josee Lajoie, “C++ Primer, 3rd Edition”,
    Addison-Wesley Professional, 1998.
    [20] Nicolai M. Josuttis, “The C++ Standard Library: A Tutorial and Reference”, Addison-Wesley Professional, 1999.
    [21] Chun-Hao Chen, “A Contour-Based 3D Reconstruction System”, Master thesis, National Cheng Kung University, Tainan, Taiwan, 2003.
    [22] Chong-Wei Li, “Object Contour Models and their 3D Images in Medical Applications”, 2007.

    下載圖示
    2013-05-02公開
    QR CODE