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研究生:程麟傑
研究生(外文):Lin-Jie Cheng
論文名稱:基於資訊融合技術之乳房核磁共振影像的腫瘤檢測
論文名稱(外文):Tumor Detection Based on Data Fusion Technique for MRI Breast Imaging
指導教授:林國祥林國祥引用關係蔡興國蔡興國引用關係
指導教授(外文):Guo-Shiang LinSin-Kuo Daniel Chai
學位類別:碩士
校院名稱:大葉大學
系所名稱:資訊工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:117
中文關鍵詞:核磁共振腫瘤檢測資訊融合紋理分析
外文關鍵詞:MRITumor detectionData fusionTexture analysis
相關次數:
  • 被引用被引用:2
  • 點閱點閱:121
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
中文摘要

  本論文提出一個利用空間域分析、切片間分析、紋理分析及多模式資訊融合所構成的系統,實現在核磁共振乳房影像上之腫瘤區域檢測。空間域分析是根據腫瘤區域內像素灰階值之亮度和腫瘤區域之面積大小進行評估,並且提出一個基於Ellipse Fitness 之區域生長演算法則,找出較精確之腫瘤區域。切片間分析是基於連續特性驗證腫瘤區域之靜態特性。紋理分析是利用腫瘤區域的特殊紋理,提升腫瘤區域的識別率,主要是擷取小波領域及共發生矩陣之相關統計特徵作為紋理性質之描述子,配合類神經網路的辨識,篩選可疑之腫瘤區域。
集合空間域分析、切片間分析及紋理分析所提供的物件亮度資訊、物件連續資訊、物件紋理資訊及物件大小變化資訊,結合資訊融合技術,建構出乳房腫瘤檢測系統。實驗結果顯示,本論文所提出的系統可以有效地檢測出乳房腫瘤區域。

關鍵字:核磁共振,腫瘤檢測,資訊融合,紋理分析
ABSTRACT

In this thesis, we proposed a scheme composed of the spatial, inter-slice, texture analyses, and multi-mode data fusion technique to achieve tumor region identification in MRI breast images. Our spatial analysis evaluates the intensity of the pixels and size information of candidate regions. To find a precise region, a region growing algorithm is proposed based on ellipse fitness. In the texture analysis, texture features are extracted form co-occurrence matrix and wavelet coefficients and combined with a neural network to filter out some regions resulting form normal tissue and noises. The inter-slice analysis is based on the continuity characteristic to verify the static behavior of tumor regions. The experimental results show that our proposed scheme can correctly identify tumor regions.

Key Words : MRI, Tumor detection, Data fusion, Texture analysis
目錄

封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
圖目錄 x
表目錄 xii

第一章 緒論 1
1.1研究動機與目的 1
1.2文獻回顧 5
1.2.1乳房X光攝影(Mammography) 5
1.2.2乳房超音波(Ultrasound) 7
1.3乳房核磁共振造影流程說明 8
1.4相關技術之簡介 10
1.4.1離散小波轉換 [26] 10
1.4.2類神經網路簡介 [22] 12
第二章 系統架構 15
第三章 空間域分析 18
3.1感興趣區域選取(ROI Selection) 18
3.2前景擷取 22
3.3適應性腫瘤區域篩選 25
3.4相連單元標記 27
3.5形狀調整 28
第四章 紋理分析 32
4.1共發生矩陣(Co-occurrence Matrix) 32
4.2空間領域之特徵擷取 34
4.3小波轉換領域之特徵擷取 35
4.4共發生矩陣之特徵擷取 37
4.5紋理特徵之整合 39
第五章 切片間分析 42
5.1連續存在之特性 42
5.2面積大小變化之性質 44
第六章 基於資訊融合之分類系統 45
6.1模糊理論 45
6.2模糊推論系統簡介 46
6.3模糊推論應用於腫瘤檢測之分類 48
6.3.1歸屬函數定義 52
6.3.2模糊規則定義 57
6.3.3解模糊化定義 58
第七章 實驗結果與分析 60
7.1定義評估標準 60
7.2實驗說明 62
7.3實驗結果與分析 64
7.3.1紋理特徵之整合結果 64
7.3.2方法一(串接型) 65
7.3.3方法二(資訊融合型) 69
第八章 結論與未來研究方向 77
8.1結論 77
8.2未來研究方向 78
附錄A 模糊規則 79
附錄B 病例編號1腫瘤切片影像與檢測結果 81
附錄C 病例編號2腫瘤切片影像與檢測結果 86
附錄D 病例編號3腫瘤切片影像與檢測結果 90
附錄E 病例編號4腫瘤切片影像與檢測結果 98
附錄F 病例編號5腫瘤切片影像與檢測結果 109
參考文獻 112
參考文獻

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