Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/15581
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dc.titleEndosome detection in cell images
dc.contributor.authorGAO JIONG
dc.date.accessioned2010-04-08T10:55:06Z
dc.date.available2010-04-08T10:55:06Z
dc.date.issued2006-11-05
dc.identifier.citationGAO JIONG (2006-11-05). Endosome detection in cell images. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/15581
dc.description.abstractDetecting 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.
dc.language.isoen
dc.subjectCell segmentation, Endosome, Canny, Training, Active contour, Metrics
dc.typeThesis
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.supervisorLEE MONG LI, JANICE
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Master's Theses (Open)

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