Master of Science in Engineering (MSE)
Electrical and Computer Engineering
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
As an essential and powerful tool to observe living organisms, three-dimensional fluorescence microscopy is widely used in biological research and diagnosis. The 4D fluorescence microscopy data can be obtained using time-lapsed videos of 3D images. To analyze and extract useful information from the increasingly large and complex biological image dataset, efficient and effective computational tools are in need but still lagging behind. In analyzing biological data, two major challenges are faced. First, time-lapsed fluorescence microscopic images typically have a low SNR. Second, biological objects often change their morphology and internal structure frequently. As such, conventional image processing methods may not be suitable for analyzing fluorescence microscopic images. The dataset we used is the 4D microscopic images of developing drosophila (fruit fly) embryos, in which three types of proteins (Myosin, Ajuba and E-cad) play important roles. Located in the center part of the cells, Myosin contraction initiates the physical tension. E-cad is the core component of intercellular adhesion complexes located on cell membranes. Ajuba is the tensor sensor which is recruited by Myosin to adhere junctions. In our previous work, we proposed a multi-object tracking method with portion matching to track those protein clusters. The goal of this research is to quantitatively analyze the correlation between these proteins based on the tracking results. In this thesis, the following tasks have been conducted. A.Sort out effective objects according to their fusion and split actions and visualize their development process clearly. B.Quantify the amount and intensity of another type of object in adjacent to the first type of object. C.Analyze the correlation between two types of microscopic objects.
Computer Vision; Correlation Analysis; Microscopic Objects; Quantification; Sorting; Visualization
Computer Engineering | Electrical and Computer Engineering
University of Nevada, Las Vegas
Dao, Yuan, "Analysis of Microscopic Objects Using Computer Vision Methods" (2021). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4238.
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