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|Title:||Endosome detection in cell images||Authors:||GAO JIONG||Keywords:||Cell segmentation, Endosome, Canny, Training, Active contour, Metrics||Issue Date:||5-Nov-2006||Citation:||GAO JIONG (2006-11-05). Endosome detection in cell images. ScholarBank@NUS Repository.||Abstract:||Detecting the movement of endosomes after the pharmacological treatment to cells is an interesting topic in pharmacology research. This study seeks to provide a comprehensive and objective characterization of the changes with respect to the intensity of cell cytoplasm and number of endosomes within a cell. Previous works have demonstrated that some automated methods can detect certain types of cells in fluorescence microscope images with high accuracy. However, cells in microscope images are tend to overlap with blur edges and noises. The existing methods are not effective enough to detect the endosomes and cell outlines for our cell images. Thus in this thesis, we defined a set of metrics to measure the endosomes in cells. Then we propose a method based on edge detection, machine learning and active contour modeling to detect the endosomes in the cells and locate those detected endosomes by cells. Based on our method, we implement a tool which can assist biologists to compute the metrics of each cell easily and quickly.||URI:||http://scholarbank.nus.edu.sg/handle/10635/15581|
|Appears in Collections:||Master's Theses (Open)|
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