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研究生:杜碩釗
研究生(外文):Du, Shuozhao
論文名稱:利用循血綠血管攝影做自動化息肉狀脈絡膜血管病變偵測之研究
論文名稱(外文):Automatic Polypoidal Choroidal Vasculopathy Detection in Indocyanine Green Angiography
指導教授:林維暘
指導教授(外文):Lin, Weiyang
口試委員:林維暘蔡佳玲陳世真余松年劉偉名
口試委員(外文):Lin, WeiyangTsai, ChialingChen, ShihchenYu, SungnienLiu, Weiming
口試日期:2012-07-27
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
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:2012
畢業學年度:100
語文別:中文
論文頁數:96
中文關鍵詞:息肉狀脈絡膜血管病變循血綠血管攝影檢查DBICP支持向量機EVEREST研究團隊
外文關鍵詞:Polypoidal choroidal vasculopathy(PCV)Indocyanine green angiography(ICG)Dual bootstrap iterative closest point(DBICP)Support vector machine(SVM)EVEREST study team
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隨著近年來眼科醫學技術的進步,發現息肉狀脈絡膜血管病變是華人地區造成失明的常見原因之一。息肉狀脈絡膜血管病變的特徵是在脈絡膜血管末端,産生息肉狀的病變,位置通常在黃斑部周圍。此外,息肉狀脈絡膜血管病變也常合併出現視網膜下出血和色素上皮細胞下積液。目前循血綠血管攝影檢查是醫生用來診斷息肉狀脈絡膜血管病變最好的方式。但是觀察影像的過程十分費時,因此造成醫生的使用上的不便。因此我們提出一個電腦輔助診斷系統,作為醫生評估息肉狀脈絡膜血管病變的工具。

我們的方法結合「DBICP」套合演算法和「支持向量機」學習演算法。我們利用循血綠血管攝影檢查之影像序列,其亮度隨時間變化曲線,材質,形狀等特徵來辨識息肉狀脈絡膜血管病變。於是系統將產生該病例的息肉狀脈絡膜血管病變分佈機率圖,提供醫生作為診斷上的參考。

我們根據EVEREST研究資料中的循血綠血管攝影檢查之影像序列產生了許多實驗數據,其結果顯示我們提出的方法,其效能比基準演算法更加提升。
With the advancement of ophthalmic technology in recent years, Polypoidal Choroidal Vasculopathy (PCV) has been reported as one of the common causes of blindness in Asian. The location of PCV is usually around macular location and it is characterized by the polypoidal vascular protrusion at the choroidal vessels. In addition, PCV is often associated with subretinal hemorrhage and Pigment Epithelial Detachment (PED). IndoCyanine Green Angiography (ICGA) is considered as the gold standard method in diagnosing PCV currently. But, the procedure is rather time-consuming and could making it inconvenient for physicians to use. For this reason, we develop a computer-aided system which assists the ophthalmologist in locating PCV and provides treatment information in management.

Our approach combines "DBICP" registration algorithm, and "Support Vector Machine" machine learning algorithm. We use the curve of intensity changing over time, texture, shape, etc., to charactering PCV in ICGA sequence.The distribution probability map of PCV in each case would provide the physician a useful diagnostic reference.

We have conducted several experiments using ICGA images from EVEREST study. The results are promising as compared to the baseline algorithm.
摘要 i
Abstract ii
第一章 導論 1
1.1 研究背景與動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 欲解決之問題 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 方法概述 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 論文貢獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
第二章 相關文獻回顧 8
第三章 系統方法 9
3.1 前處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 影像套合 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 特徵擷取 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.1 亮度 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.2 斜率 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.3 線性迴歸 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.4 亮度平移 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.5 具時間性之局部二值模式 . . . . . . . . . . . . . . . . . . . . . 20
3.3.6 亮度變異數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.7 局部二值模式 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.8 形狀 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 特徵選取 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.5 支持向量機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.5.1 樣本為兩個類別的支持向量機 . . . . . . . . . . . . . . . . . . . 27
第四章 實驗結果 31
4.1 病例影像資料之取得 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 實驗環境設定 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 評估效能 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.3.1 基準演算法的實驗結果 . . . . . . . . . . . . . . . . . . . . . . 36
4.3.2 Bayes 與 SVM 的比較結果 . . . . . . . . . . . . . . . . . . . . 38
4.3.3 使用更多特徵的實驗結果 . . . . . . . . . . . . . . . . . . . . . 41
4.3.4 使用 F-score 特徵選取的實驗結果 . . . . . . . . . . . . . . . . 46
4.3.5 使用兩階段的 PCA+LDA 特徵降維的實驗結果 . . . . . . . . . 51
4.3.6 使用資訊融合的實驗結果 . . . . . . . . . . . . . . . . . . . . . 54
4.3.7 合併使用所有特徵的實驗結果 . . . . . . . . . . . . . . . . . . . 57
4.4 實驗總結與討論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
第五章 結論和未來發展 65
第六章 附錄 67
6.1 附錄 A:其餘組別之實驗結果 . . . . . . . . . . . . . . . . . . . . . . . 67
參考文獻 74
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