醫學專家與病理專家藉由細胞實驗來觀察外界因素對人體的影響。一種稱為Phase Contrast的成像技術，改進一般Bright Contrast成像導致細胞幾乎看不見的缺點，已被廣泛地使用在長時間觀察活細胞的研究上。
在本論文中，我們藉由影像處理技術抓取涵蓋細胞的初始輪廓，然後以Fast Geodesic Active Contour（FGAC）來分割並取得細胞輪廓資訊。經實驗證實即使初始輪廓包含兩個以上的獨立細胞，FGAC仍能正確的分割出完整的細胞個體。在細胞序列影像的分割上，我們採取Snake與FGAC交替使用的方式來節省時間，同時維持不錯的效果。在細胞追蹤上，我們證明即使細胞在活動過程中有細胞分裂的情形產生，我們的方法仍能完全確立細胞在一影像序列中的前後對應關係。此外，我們分析細胞輪廓隨時間不同而形變的應變， 藉由觀察應變量曲線與細胞生命週期相呼應，以斷定細胞的行為、狀態。在視覺化的效果上，我們將細胞輪廓資訊與時間相結合成三維資訊，並將細胞的運動變化情形呈現在三維空間中，使得觀察細胞形變、運動過程更方便，這些都是我們以電腦視覺技術應用於細胞顯微影像的具體成果。

Medical experts and pathologists experiment on cells to observe the effects upon cells caused by variations of outside factors. A new imaging technology called “Phase Contrast”, which improves the drawbacks of invisible cells under the conventional microscopy, has been employed in the researches of observing living cells for a long time.
In this thesis, we use image-processing technique to get the initial contour containing cells. Then we do the segmentation with the Fast Geodesic Active Contour (FGAC) method to extract the contours of cells. Even when the initial contour covers more than two cells, the FGAC still can correctly segment all the cells. In the segmentation of cellular image sequence, we use Snake and FGAC alternatively to save computation time and retain the good results. In tracking cells, we demonstrate that our method can trace and maintain correct correspondences between cells in a sequence of cell motion image, even cytokinesis occurred in the activities of cells. Besides, we use analyze “strain” of cell contours deforming with time, and ascertain the behaviors and states of cells by studying the curve of magnitude of strain corresponding to a life cycle of cell. In motion visualization, we combine the information of cell contours with time to get the (2D+T) data set, and display the variation of cell motion in the three-dimensional space. It is convenient to observe the complete process of the deformation and motion of a certain cell, or to observe the global motions of all cells in an image sequence. These are some practical achievements in this thesis for applying computer vision technique to cellular microscopic image analyses.

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