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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:189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.

    目錄 摘要……………………………………………………………………………i Abstract……………………………………………………………………ii 誌謝…………………………………………………………………………iv 目錄……………………………………………………………………………v 圖目錄………………………………………………………………………vii 表目錄………………………………………………………………………ix 第一章 緒論…………………………………………………………………1 第一節 研究背景與動機………………………………………………1 第二節 研究目的………………………………………………………3 第三節 研究架構………………………………………………………4 第二章 相關研究探討………………………………………………………6 第一節 乳房X光攝影教學系統之應用………………………………6 第二節 影像輪廓之偵測………………………………………………12 第三節 乳房X光腫瘤偵測之最佳特徵選擇………………………17 第三章 影像處理之方法剖析………………………………………………23 第一節 影像對比模式…………………………………………………23 第二節 形態學的理論基礎……………………………………………28 第三節 影像分割之方法與步驟………………………………………34 第四章 自組性類神經演算法………………………………………………38 第一節 SOM基本理論………………………………………………38 第二節 LVQ基本理論………………………………………………42 第三節 SOL演算法…………………………………………………44 第五章 偵測可疑區域之依據及方法………………………………………46 第一節 影像之研究依據……………………………………………46 第二節 目標物之偵測與描述………………………………………47 第六章 研究成果與討論……………………………………………………52 第一節 開發之影像基本處理工具IMPT……………………………52 第二節 影像中區域偵測改善之結果…………………………………54 第三節 演算法速度改進之成果………………………………………61 第七章 結論與未來研究建議………………………………………………64 第一節 結論……………………………………………………………64 第二節 未來研究建議…………………………………………………65 參考文獻……………………………………………………………………66 圖目錄 圖1-1 研究流程圖……………………………………………………………5 圖2-1教學系統模組與應用模式…………………………………………8 圖2-2 ImageViewer系統畫面與功能………………………………………9 圖2-3 「ImageViewer系統」信度分析的百分比統計……………………11 圖2-4 特徵擷取之方法架構………………………………………………12 圖2-5 方向通道擷取結構示意圖…………………………………………13 圖2-6 邊與線使用Teager能量組合法之結果……………………………14 圖2-7 關鍵點可能生長方向之示意圖……………………………………17 圖2-8 關鍵點矯正之示意圖………………………………………………17 圖2-9 紋路頻譜的編碼……………………………………………………19 圖3-1 乳房X光影像之反白………………………………………………24 圖3-2 轉換函數之圖形……………………………………………………25 圖3-3 對比增強之影像……………………………………………………26 圖3-4 影像之直方圖………………………………………………………27 圖3-5 直方圖匹配增強之影像與直方圖…………………………………28 圖3-6 集合論之演算………………………………………………………29 圖3-7 以ASF濾波之影像…………………………………………………32 圖3-8 以OCRF濾波之影像………………………………………………33 圖3-9 有臨界值 區隔的影像直方圖……………………………………35 圖3-10 以臨界值分割的影像………………………………………………36 圖3-11 以區域成長分割的影像……………………………………………37 圖4-1 SOM架構圖…………………………………………………………39 圖4-2 LVQ架構圖…………………………………………………………42 圖4-3 SOL之示意圖…………………………………………………………44 圖4-4 SOL特徵空間映射圖…………………………………………………44 圖5-1連通成份……………………………………………………………48 圖5-2以四元樹執行MPP…………………………………………………49 圖5-3區域紋路之分析………………………………………………………50 圖6-1 IMPT的介面圖示……………………………………………………52 圖6-2 IMPT執行影像反白功能……………………………………………53 圖6-3 IMPT執行影像增強功能……………………………………………54 圖6-4 影像濾波之預處理…………………………………………………55 圖6-5 重建影像……………………………………………………………56 圖6-6 感興趣區域之描述…………………………………………………57 圖6-7 經SOL演算後感興趣區域之描述………………………………58 圖6-8 預處理之良性鈣化影像……………………………………………59 圖6-9 良性鈣化影像區域偵測之表示……………………………………59 圖6-10 預處理之腫瘤區影像………………………………………………60 圖6-11 腫瘤影像區域偵測之表示………………………………………61 表目錄 表2-1 教學系統模組功能介紹………………………………………………8 表5-1 分期的TNM系統列表………………………………………………46 表5-2 影像在紋路統計的五個特徵輸入之量測值………………………50 表6-1 主成份分析之特徵值………………………………………………56 表6-2 各聚類演算法對點集合聚類之時間分析………………………62

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