臺灣博碩士論文加值系統

微創手術只需要幾個小切口就能執行手術，它的優點包含傷口小、出血少和恢復快。然而，外科醫生只能透過內視鏡的畫面和手術器械進行手術，這會提高實行微創手術的複雜度以及困難性。因此，在實際開刀前醫生必須接受多種相關的訓練來提升他們的手術技巧。現存訓練系統大多有著零散難以組裝與體積龐大難以移動的缺點，所以本篇論文的目標在於開發一套基於智慧型裝置的可攜式混合型的微創手術訓練系統模組。系統中主要包含了三個部分：定位模組、實體訓練模組與智慧型裝置。本論文中已經成功實現本系統，方便攜帶而且容易應用於任何現存的手術訓練箱中，並且可以提供實體訓練模組以及虛擬實境兩種訓練方式。定位模組使用影像偵測兩個被動式標記(Marker)的位置，其定位的三軸誤差(X, Y, Z)分別為0.21±0.17 cm、0.10±0.10 cm和0.29±0.22 cm。不僅如此，定位模組還能在畫面更新率22 FPS (Frames per Second)下即時計算出座標，可以準確且流暢地應用於微創手術訓練之中。而經過7位專業醫師的問卷調查後可以證實本系統是具有可攜性以及訓練成效的。

The advantages of MIS include smaller incisions, less bleeding and shorter recovery time. However, surgeons can only perform laparoscopic surgery through the video from a laparoscope and surgical instruments. This may increase the complication and difficulties while fulfilling MIS. Therefore, before conducting the realistic operation, surgeons are required to receive multiple related trainings to improve their surgical skills. Current training systems are scattered and large. Herein, the objective of this thesis is to develop a smart device based, portable, and hybrid MIS training system. This proposed system consists of three main elements: a locator, a physical training module and a smart device. In this thesis, all parts of the system have been implemented to be carriable and simple to apply on any current surgical training box. In addition, the system provides physical and VR training processes based on smart device. The locator in our system can detect the positions of two markers through image processing. The errors of the provided locating method can reach to 0.21±0.17 cm in X-axis, 0.10±0.10 cm in Y-axis, and 0.29±0.22 cm in Z-axis. Besides, the coordinates can be calculated at the frame rate of 22 frames per second. Therefore, our system can work accurately and smoothly on MIS training, and the questionnaire proves that our system is portable and training-effective.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章、 緒論 1
1.1 研究動機 1
1.2 文獻探討 2
1.3 研究目的 3
1.4 論文架構 4
第二章、 背景介紹 5
2.1 微創手術訓練 5
2.1.1 實體訓練箱 5
2.1.2 動物實驗 6
2.1.3 虛擬實境訓練系統 6
2.2 影像形態學 7
第三章、 系統設計與方法 10
3.1 系統架構 10
3.2 訓練介面程式 11
3.2.1 實體訓練模組介面 11
3.2.2 虛擬實境訓練介面 14
3.3 實體訓練模組 15
3.4 定位模組 17
3.4.1 Bayer Filter Pattern/RGB轉換 18
3.4.2 Line Buffer 20
3.4.3 顏色校正 20
3.4.4 RGB/HSV轉換 22
3.4.5 顏色偵測與二值化 24
3.4.6 影像形態學濾波器 25
3.4.7 二維座標計算 27
3.4.8 軟硬體協同設計 28
3.5 實驗設計 35
3.5.1 定位準確度實驗 35
3.5.2 問卷評估實驗 37
第四章、 結果與討論 39
4.1 系統規格 39
4.1.1 實體訓練模組 41
4.1.2 定位模組 42
4.2 定位模組驗證 43
4.2.1 定位準確度實驗結果 43
4.2.2 定位系統比較 45
4.3 問卷評估實驗結果 46
4.4 準確度誤差分析 46
4.4.1 二值化誤差 47
4.4.2 系統架構限制 47
4.4.3 鏡頭扭曲 47
第五章、 結論與未來展望 49
第六章、 參考文獻 50
附錄一 、問卷 53
附錄二 、藍牙模組介紹 55
附錄三 、Nexus 9規格 56

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