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研究生:賴振源
研究生(外文):Jen-Yuan Lai
論文名稱:以影像處理評估乳癌Her-2螢光原位雜交影像基因表現
論文名稱(外文):Analysis of HER2/neu in Breast Cancer from Fluorescence in situ Hybridization Image
指導教授:柯建全柯建全引用關係
指導教授(外文):Chien-Chuan Ko
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:資訊工程學系研究所
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:2010
畢業學年度:98
語文別:中文
中文關鍵詞:螢光原位雜交法分水嶺運算K-Means支援向量機軸幅式類神經網路
外文關鍵詞:Fluorescence in situ hybridizationwatershed transformK-Meanssupport vector machineradial basis function neural network
相關次數:
  • 被引用被引用:0
  • 點閱點閱:518
  • 評分評分:
  • 下載下載:103
  • 收藏至我的研究室書目清單書目收藏:1
依據行政院衛生署的婦女癌症發病率統計,乳癌為國內女性癌症發病率的第一位,死亡率第四位,乳癌大多發生在40~60歲的婦女,近年來乳癌的發症年齡已經逐漸下降至30歲。而免疫組織化學染色法與螢光原位雜交法是目前最普遍的乳癌症檢測技術,免疫組織化學染色法可以提供簡易且有效的檢測,螢光原位雜交法則能提供更精準的分析結果。螢光原位雜交法是藉由螢光顯微鏡觀察細胞核內部的染色體反應,協助臨床醫師決定適合治療方法與適合的藥物,此種檢測方式能提供乳癌預後分析的參考。
儘管如此,傳統的分析過程僅能依靠醫師在螢光顯微鏡下進行大量螢光原位雜交影像觀察,這不僅需要花大量的時間,也需要消耗相當的人力,這使得螢光原位雜交影像自動分析在乳癌預後上有相當的必要性。在本研究中,我們提出一自動化分割與分析流程,藉由螢光原位雜交影像上染色體與細胞核的特性,運用一系列影像處理,降低染色體影像的背景雜訊影響,並清除細胞核影像上的破洞,順利從影像中擷取出所感興趣的細胞核與染色體區域,擷取與HER2基因放大有關的參數,再透過SVM與RBF進行分類與判讀,實驗結果證明本研究能協助醫師臨床判讀與預後治療的資訊。

目錄
中文摘要 i
英文摘要 ii
表目錄 vii
圖目錄 viii
第一章、緒論 - 1 -
1.1研究背景 - 1 -
1.2研究動機與目的 - 4 -
1.3相關研究 - 6 -
1.3.1 IHC研究現況 - 6 -
1.3.2 FISH研究現況 - 6 -
第二章、研究材料與相關影像處理技術 - 9 -
2.1研究材料 - 9 -
2.2 FISH染色 - 10 -
2.3 RGB色彩模型 - 11 -
2.4形態學處理 - 13 -
2.4.1形態學高帽運算 - 13 -
2.4.2形態學平滑 - 14 -
2.5分水嶺轉換 - 15 -
2.6距離變換 - 16 -
2.7直方圖等化 - 17 -
2.8 K-Means分群演算法 - 18 -
2.9連通成份標記 - 19 -
2.10支援向量機 - 20 -
2.11輻狀基底函數類神經網路 - 22 -
第三章、研究方法 - 24 -
3.1染色體分割 - 26 -
3.1.1濾除人工雜訊 - 30 -
3.2細胞核分割 - 32 -
3.2.1形態學平滑化處理 - 32 -
3.2.2細胞核分割運算 - 34 -
3.2.3補洞 - 35 -
3.2.4分水嶺運算 - 37 -
3.2.5標記 - 39 -
第四章、自動化分析 - 43 -
4.1病例影像分析 - 43 -
4.2特徵擷取 - 44 -
4.3 交叉驗證 - 46 -
第五章、實驗結果與討論 - 47 -
5.1分割準確度評估 - 47 -
5.2 交叉驗證實驗結果 - 53 -
5.3線性支持向量機分析結果 - 54 -
5.4輻狀基底函數支持向量機分析結果 - 56 -
5.5輻狀基底函數類神經網路分析結果 - 58 -
第六章、結論 - 60 -
6.1結論 - 60 -
6.2未來研究方向 - 61 -
6.2.1其他乳癌細胞增生與凋零預後指標探討 - 61 -
6.2.2乳癌術後預後復發預測 - 62 -
參考文獻 - 63 -
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