研究生: |
郭泰榕 Tai-Jung Kuo |
---|---|
論文名稱: |
自組性類神經網路應用於乳房X光影像之偵測 The Application of Self-Organizing Neural Network on Detection of Mammography |
指導教授: |
莊謙本
Chuang, Chien-Pen |
學位類別: |
碩士 Master |
系所名稱: |
機電工程學系 Department of Mechatronic Engineering |
論文出版年: | 2007 |
畢業學年度: | 95 |
語文別: | 中文 |
論文頁數: | 67 |
中文關鍵詞: | 醫學影像辨認系統 、乳房X光影像 、自組性類神經網路 、特徵擷取 |
英文關鍵詞: | Medical Image Identification Systems, Mammography, Self-Organization Neural Network, Feature Extraction |
論文種類: | 學術論文 |
相關次數: | 點閱:226 下載:22 |
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醫學影像的研究,從早期的X光片、超音波顯像,到現在的核磁共振(MRI)、電腦斷層掃瞄(CT),使得全世界在醫學技術及醫療品質有了大幅的提昇。然而在國內,此等醫療技術的應用仍大量仰賴國外高科技產品的輸入,例如MRI的掃描器,仍是完全倚靠國外的輸入;相關的使用軟體也是購自國外。為了解決此問題,並降低全民醫療成本,實有必要自行發展醫學影像辨認系統。本論文結合圖形識別與類神經網路之演算法則,提出一套改善乳房X光影像的辨認技術,以供乳癌診斷參考。
本論文所提出的概念,主要利用自組性類神經網路(SOM)的演算法則,實施特徵萃取、分類定義及聚類的工作,使乳癌的鈣化組織與腫瘤區塊較準確的顯示出來,並配合影像處理之分析,達到提升辨識速度及診斷準確度的目標。所開發出來的影像處理工具箱,包括影像濾波、SOM特徵擷取與影像邊界描述等。經由本研究的模擬實驗後,在運算複雜度與速度上,均已獲得改善。
The progress of medical imaging technologies, from X-ray radiography, ultrasonic sonography to modern age's Magnetic resonance imaging (MRI) and Computed Axial Tomography (CT/CAT) scan has helped the advance of the medical technology as well as the improvement of medical care quality all over the world. However, in Taiwan, the state of art of such technologies are still far behind those of advanced nations. All important medical tools and instruments still rely heavily on the import from other countries, such as Japan and United States. For example, NMR spectroscopy of the MRI machine is still wholly imported from those countries. Even the related soft-wares are also more than 90% purchased from other countries. It is essential to develop our own medical imaging identification systems so as to reduce the future overall medical expense of our country. To contribute to this effort, we propose a new medical imaging technologies, which combines the advanced technologies of pattern recognition and modern numerical methods in neural networking, to improve the power of discretion in analyzing mammography so as to reduce the false rate in the diagnostics of breast cancer.
In this study, we applied the methodologies commonly used in computer-aided design systems and self-organizing mapping (SOM) artificial neural networks, such as feature extraction, clustering and filtering, to the ramification of various mammograms. We show that more accurate diagnostics can be achieved with better sensitivity in separating calcified tissues and tumor masses. Higher detection resolution, better recognition efficiency and fast processing speed for mammography are also realized with the aid of new imaging techniques. We also developed an imaging processing toolbox, which contains image filtering, SOM feature extraction and rendering of (blur) image boundary. All newly developed numerical methods and functions (including SOL) can be easily retrieved for image analysis. The result of our computer simulations clearly shows that the complexity of mammography imaging processing algorithm and calculation speed can be significantly improved based on our proposed methods.
[1]San-Kan Lee, “A computer-aided design mammography screening system for detection and classification of micro –calcifications.”International Journal of Medical Informatics, May 2000, pp 29-57.
[2]Szekely, N ; Toth, N ; Pataki, B, “A hybrid system for detecting masses in mammo -graphic images.” lnstmrnenlillion and Measuremm IEEE Trans. Volume 55, Issue 3, June 2006 pp. 944 – 952.
[3]Dau American College of Radiology (ACR), ACR BI-RADS-Mammography. 4th Edition. In: ACR Breast Imaging Reporting and Data System, Breast Imaging Atlas. Reston, VA. American College of Radiology; 2003.
[4]陳勁宇,“使用Microsoft Office開發乳房攝影之BI-RADS資料監測系統”,中華放射醫誌,台南,2005。
[5]Sameer Singh, Keir Bovis, “An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses.” IEEE Trans. on Information Technology in Biomedicine, Vol. 9, No. 1, March 2005 pp. 109-119.
[6]X. Q. Zhou and H. K. Huang, “Authenticity and Integrity of Digital Mammography Images.” IEEE Trans. On Medical Imaging, vol. 20, NO. 8, August 2001.
[7]李三剛,“乳房X光攝影影像品質提升及人員培訓計畫”,中華民國放射線醫學會,台北,2006。
[8]張曼莉,“多功能「乳房X光攝影篩檢教學研究系統」建構與應用之研究”,國立台灣師範大學工業教育學系博士論文,2006。
[9] 揚敏宏,“影像輪廓偵測與分割”,私立逢甲大學通訊工程學系碩士論文,2005。
[10]于南書,“最佳特徵選擇:乳房X光片腫瘤偵測”,國立成功大學資訊工程學系碩士論文,2004。
[11]Rafael C. Gonzalez原著,謬紹綱譯, “數位影像處理”,台灣培生教育出版股份有限公司,台北,2003。
[12]Poynton, C. A., A Technical Introduction to Digital Video, John Wiley & Sons, New York, 1996.
[13]Ritter, G. X., and Wilson, J. N., Handbook of Computer Vision Algorithms in Image Algebra, CRC Press, Boca Raton, Fla, 2001.
[14]Bassart, J. P., Chacklackal, M. S., and Gonzalez, R. C., “Introduction to Gray-Scale Morphology.” Advances in Image Analysis, Y. Mahdavieh and R. C. Gonzalez, SPIE Press, Bellingham, Wash., 1992 pp. 306-354.
[15]Cheriet, M., said, J. N., and Suen, C. Y., “A Recursive Thresholding Technique for Image Segmentation.” IEEE Trans. Image Processing, Vol. 7, no. 6, 1998 pp. 918-921.
[16]Shapiro, L. G., and Stockman, G. C., Computer Vision, Prentice Hall, Upper Saddle River, N.J. 2001.
[17]張斐章、張麗秋,“類神經網路”,臺灣東華書局股份有限公司,台北,2005。
[18]Meng-Ju Wu, “Computer Aided Mammography Enhancement and Mass Detection System.” Institute of Electrical Engineering National Tsing Hua University Hsinchu, Taiwan, R.O.C, June 2001.
[19]Baig, M. H., Rasool, A., and Bhatti, M. I., “Classification of Electro- cardiogram Using SOM, LVQ and Beat Detection Methods in Localization of Cardiac Arrhythmias.” Proceedings of the 23rd Annual EMBS International Conference, October 2002 pp.25-28.
[20]Peter Pressman原著,廖舜茹譯,“乳癌全書”,英屬蓋曼群島商家庭傳媒股份有限公司,台北,2006。
[21]葉倍宏,“MATLAB7程式設計”,台灣全華科技圖書股份有限公司,台北,2006。
[22]林連源,“模糊式加解密模式之研究”,國立台灣師範大學應用電子科技研究所碩士論文,2006。