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研究生:張玉成
研究生(外文):Yu-Cheng Chang
論文名稱:應用於乳房X光攝影之階層式腫塊偵測法則
論文名稱(外文):Hierarchical-Based Mass Detection for Digital Mammogram
指導教授:林國祥林國祥引用關係
指導教授(外文):Guo-Shiang Lin
口試委員:張世旭張軒庭林國祥
口試委員(外文):Shih-Hsu ChangHsuan T. ChangGuo-Shiang Lin
口試日期:2012-07-16
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:2012
畢業學年度:100
語文別:中文
論文頁數:88
中文關鍵詞:腫塊檢測紋理分析類神經網路分類器
外文關鍵詞:mass detectiontexture analysisneural classifier
相關次數:
  • 被引用被引用:0
  • 點閱點閱:116
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出一套針對乳房X光醫學影像之階層式腫塊區域檢測方法。此檢測方法包含三大部分:感興趣區域選取、紋理特徵擷取和類神經網路為基礎之分類器。感興趣區域 (region of interest) 選取用於降低系統運算複雜度。為了擷取有效的紋理特徵,本論文由空間域 (spatial domain) 和小波域 (wavelet domain) 分別擷取出多種紋理特徵。多種紋理特徵配合監督式訓練之類神經網路分類器,則可以進行腫塊區域的檢測。
實驗結果顯示,本系統對於腫塊檢測之召回率與精確率分別為86% 和27%。因此,本論文提出的輔助診斷系統能夠有效地檢測腫塊區域。

In this thesis, we proposed a mass detection method based on texture analysis and neural classifier. The proposed mass detection method is composed of two parts: ROI selection, feature extraction, and neural classifier. ROI selection is used to reduce the computational complexity of the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to find the candidates of mass regions. These texture features are extracted and combined with a supervised neural network to be classifier. The experimental result shows that the average recall rate of our proposed scheme is more than 86%. The result demonstrates that our proposed method can achieve mass detection.
封面內頁
簽名頁
中文摘要 iii
ABSTRACT iv
誌謝 v
目錄 vi
圖目錄 viii
表目錄 x

第一章 緒論 1
1.1 研究動機與目的 1
1.2 乳癌介紹 2
1.3 乳癌檢測方式 7
1.4 文獻回顧 9
1.4.1 乳房X光攝影 9
1.4.2 乳房超音波 10
1.4.3 乳房磁振造影 11
1.5 乳癌跟腫塊的差異性 12
第二章 系統架構 13
2.1 前處理 14
2.1.1 階層式處理 15
2.1.2 感興趣區選取 20
2.2 特徵選取 24
2.2.1 紋理分析 25
2.3 多層式類神經網路 33
第三章 實驗結果 36
3.1 評估標準之定義 36
3.2 實驗結果與分析 37
3.2.1 二值化偵測結果 37
3.2.2 感興趣區域結果 39
3.2.3 整體系統結果 41
第四章 結論 53
參考文獻 54
附錄 59


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