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研究生:陳玟儒
研究生(外文):Wen-Ru Chen
論文名稱:一種利用雙攝影機及追蹤投影機重建紅外線立體血管影像的方法
論文名稱(外文):A Method of Reconstructing Vascular Infrared Stereo Image by Using Dual Camera and Projector Tracking
指導教授:黃德成黃德成引用關係
指導教授(外文):Der-Chen Huang
口試委員:謝韶徽蕭敏學
口試委員(外文):Shao-Hui ShiehMiin-Shyue Shiau
口試日期:2016-07-29
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊科學與工程學系
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:2016
畢業學年度:104
語文別:中文
論文頁數:58
中文關鍵詞:血管投影三維座標重建深度影像擷取
外文關鍵詞:vein reprojection3D reconstructionstereo disparity
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靜脈穿刺是現今常見的侵入式醫療方式之一,然而依據病患體質不一,靜脈位置的找尋時常造成醫護人員的困擾,也經常發生重複施打多次仍找不到正確血管位置的狀況,這樣的問題對需要緊急治療的傷患尤為不利;若是能夠藉由科技的輔助,透過投影等方法提示醫護人員血管的所在位置,可以很大程度的加速救護工作的進行。

本論文利用血管對特定波長紅外光的吸收程度與皮膚組織不同的原理,利用雙攝影機擷取血管影像,透過計算出血管的視差圖重建其三維座標,並使用投影機將血管投射在皮膚表層上;期望透過這樣的血管示現方式,準確的指出較深層不易以肉眼察覺之血管位置,進而達到醫療輔助之目的。


Venipuncture is a common procedure in invasive medical. However, there are so many differences among each individual that may cause the variation of veins’ position. Mistakes are easily taken when healthcare workers try to inject some necessary medical cures. As a result, how to precisely locate veins on patients’ body becomes an important and difficult medical issue. We started to think about an alternative solution from technical views since we realize the problem.

In this research, we utilize the feature of hemoglobin that has higher absorption rate for infrared light source in specific wavelength. By using dual charge-coupled device (CCD) cameras to receive the reflected infrared from the target, this paper proposed a structure which reconstructs the three-dimensional coordinates of veins, and with the supports of projector we finally reproject the invisible veins onto tested personnel''s skin to assist vascular targeting for medical usage.


致謝辭 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
1 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 論文架構 2
2 相關研究 3
2.1 近紅外光照射血管之原理 3
2.2 影像處理 4
2.2.1 平均濾波(Mean Filtering) 4
2.2.2 直方圖均化(Histogram Equalization) 5
2.3 攝影機校正(Camera Calibration) 6
2.3.1 攝影機內部參數(Camera Intrinsic) 6
2.3.2 失真參數(Distortion Properties) 9
2.3.3 攝影機外部參數(Camera Extrinsic) 12
2.3.4 同質性座標(Homogeneous Coordinate) 14
2.3.5 攝影機校正與對應性矩陣 14
2.3.6 校正板 16
2.4 雙攝影機校正 18
2.4.1 立體校正(Stereo Calibration) 18
2.4.2 極線幾何(Epipolar Geometry) 19
2.4.3 立體對齊校正(Stereo Rectification) 22
2.5 投影機與攝影機校正(Projector-Camera Calibration) 24
2.6 視差圖擷取方法 26
2.6.1 計算像素相異程度分數表(Cost Volume) 26
2.6.1.1 SAD(Sum of absolute differences) 27
2.6.1.2 SSD(Sum of squared differences) 27
2.6.2 平滑度限制(Smoothness constraint)與濾波(Filtering) 27
2.6.2.1 樹狀濾波(Tree Filtering) 28
3 研究方法 31
3.1 照射光源波長 32
3.2 血管影像的前處理 34
3.3 計算視差圖 36
3.4 血管像素的三維座標還原 39
3.5 計算投影影像及追蹤投影 41
4 實驗結果 42
4.1 實驗之軟硬體環境 42
4.1.1 電荷耦合元件攝相機 43
4.1.2 投影機 43
4.1.3 940nm左右光源 44
4.2 血管光源實驗 44
4.3 攝影機校正 46
4.4 投影機與攝影機校正(Projector and Camera Calibration) 49
4.5 擷取血管影像及追蹤投影 52
5 結論及未來展望 56
參考文獻 57



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[6]Wikipedia, "Epipolar Geometry," https://en.wikipedia.org/wiki/Epipolar_geometry
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[10]M. Chowdhury, M. A. Bhuiyan, "Fast Window based Stereo Matching for 3D Scene Reconstruction," The International Arab Journal of Information Technology, vol. 10, no. 3, pp. 209-214, 2013.
[11]Q. Yang, "A Non-Local Cost Aggregation Method for Stereo Matching," Computer Vision and Pattern Recognition, pp.1402–1409, 2012.
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