細胞為構成身體組織的最重要的基本元素之一。在因應外在微環境的刺激時，細胞會藉由改變其本身的機械力學性質來適應外在微環境的變化，而細胞微流變學則是分析細胞內力學性質變化的一種技術。本研究旨建置一套粒子追蹤微流變系統、發展相關分析演算法，並將此系統運用在實際細胞的分析上。透過追蹤嵌入於細胞內螢光珠粒在細胞受到外在微環境刺激時所產生的布朗運動軌跡，此系統可計算出細胞內的黏性與彈性係數，並藉此分析細胞力學性質的變化。本文將先回顧微流變學的原理及其在力學分析上的意義，接著再描述本研究所提出的演算法流程與設計理念。在均方位移演算法驗證方面，採用理論布朗運動的模擬影像為分析標的，而系統所推得的均方位移與理論數值誤差小於2%；在彈性模數的計算方面，則採用文獻上的資料為驗證基準，結果顯示，由系統所得的彈性模數與文獻相比，平均誤差約8%而黏性模數的平均誤差約5%，其可能原因為文獻中的發散曲線數據被演算法用連續性片段曲線擬合給收斂至連續性曲線。最後，我們探究在雜訊的干擾下，我們所提的各種追蹤方法組合的效能，在雜訊比(singal-to-noise ratio, SNR)越大，珠粒影像的亮度分佈越接近常態分佈時，運用高斯混合模型做影像分割去追蹤的組合可獲得最佳的結果；而在追蹤發生形變的目標物體下，則不適合搭配相關性法去做定位。最後，採用本研究所發展的粒子追蹤微流變系統來探究腫瘤壞死因子(TNFα)對軟骨細胞的影響，並從量測結果歸納出，加了TNFα的細胞明顯比正常細胞的彈性還來得大，也就是越能抵抗變形以至於硬度較大，與文獻上結論一致。

Cells are basic constituents of tissue in the body. To adapt to the changes of extracellular microenvironment, cells will regulate their intracellular mechanical and physiological mechanisms to control their activities, and cellular microrheology is a technique to examine these intracellular mechanical properties. Advantages of particle tracking microrheology are its non-invasiveness and locality to living cells. The purpose of this study is to develop a system of video particle tracking microrheology and its corresponding algorithms to infer cellular mechanical properties. To begin with, we reviewed the theory and physical meaning of microrheology. Then, we described the procedure of our proposed algorithms and design concepts in detail. For algorithm validation, the proposed algorithms were used to track the simulated images of fluorescence particles undergoing Brownian motion with various types of noise contamination. Finally, the proposed algorithms were applied to the process of real images of fluorescence particles embedded in living cell. Results of the experiments indicated that our algorithms could reach similar conclusions as those presented in the literature.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表格目錄 VII
圖目錄 VIII
參數符號與縮寫表 XI
第一章 緒論 1
1.1 細胞力學 (Cell mechanics) 1
1.2 力學性質量測 2
1.3 文獻回顧 3
1.4 研究目的與方法 4
第二章 粒子追蹤微流變學 5
2.1 基本原理：在擴散作用下 5
2.1.1 黏滯性液體(Viscous Liquid) 6
2.1.2 彈性固體(Elastic Solid) 8
2.1.3 黏彈性物質(Viscoelastic material) 9
2.2 基本原理：粒子在擴散和載流下的作用 11
2.3 均方位移與粒子在介質中的關係 14
第三章 分析方法 15
3.1 影像概念 17
3.1.1 像素 17
3.1.2 解析度 17
3.2 影像平滑化 18
3.3 影像分割 20
3.3.1 K-means分群法 20
3.3.2 最佳化閥值，Otsu二分法 22
3.3.3 高斯混合模型 25
3.4 珠粒定位 27
3.4.1 重心(Center of Mass)法 30
3.4.1 相關性(Correlation)法 30
3.5 運動軌跡 32
第四章 系統的初步驗證 34
4.1 影像模型 34
4.2 模擬結果比較 34
4.3 剪力模數 38
4.4 分析方法比較 42
4.4.1 粒子追蹤演算法在散粒雜訊影像的效能分析 43
4.4.2 粒子追蹤演算法在椒鹽雜訊影像的效能分析 48
4.4.3 粒子追蹤演算法在銳利影像的效能分析 48
第五章 活體細胞的黏彈性測量 52
5.1 統計檢定分析 52
5.2 離群值探測 53
5.3 量測分析結果 56
第六章 結論與未來工作 61
6.1 總結 61
6.2 未來工作 62
參考文獻 63

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