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  • Utilization of Spatial Information for Segmentation of Cell Nuclei in Fluorescence Microscopy Image

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    Gharipour_2016_01Thesis.pdf (1.555Mb)
    Author(s)
    Gharipour, Amin
    Primary Supervisor
    Liew, Alan
    Other Supervisors
    Blumenstein, Michael
    Year published
    2016
    Metadata
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    Abstract
    Numerous aspects of analyzing and quantifying fluorescence microscopy images rely on quantitative cell nucleus image analysis. Specifically, the basis for all automated cell image analysis in high-throughput applications is image segmentation. Semi-automatic and manual segmentation methods are tedious, require intensive labor, and suffer from inter-and intra-person variability. Therefore, automatic methods with the ability to deal with different cell types and image artifacts are required. The goal of this thesis is motivated by the fact that in cell nuclei image segmentation, the spatial relationship of the pixels can be ...
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    Numerous aspects of analyzing and quantifying fluorescence microscopy images rely on quantitative cell nucleus image analysis. Specifically, the basis for all automated cell image analysis in high-throughput applications is image segmentation. Semi-automatic and manual segmentation methods are tedious, require intensive labor, and suffer from inter-and intra-person variability. Therefore, automatic methods with the ability to deal with different cell types and image artifacts are required. The goal of this thesis is motivated by the fact that in cell nuclei image segmentation, the spatial relationship of the pixels can be utilized as an important characteristic that improves the performance of segmentation methods. Therefore, this thesis aims to use spatial information in cell nuclei image segmentation and proposes several implicit models under different assumptions. First, we assume that the local image data are Gaussian and propose an implicit model based on the Bayesian classification risk and anisotropic weighting scheme for fluorescence microscopy image segmentation. The proposed algorithm obtains the lowest MAD measure for all four experiments. The MAD values are approximately 18%, 7%, and 12% better than the state-of-the art methods we compared with for U20S cells, NIH3T3 cells, and Synthetic cells, respectively.
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    Thesis Type
    Thesis (PhD Doctorate)
    Degree Program
    Doctor of Philosophy (PhD)
    School
    School of Information and Communication Technology
    DOI
    https://doi.org/10.25904/1912/586
    Copyright Statement
    The author owns the copyright in this thesis, unless stated otherwise.
    Item Access Status
    Public
    Subject
    Fluorescence microscopy
    Cell nuclei
    Bayesian classification
    Jaccard coefficient
    Publication URI
    http://hdl.handle.net/10072/365266
    Collection
    • Theses - Higher Degree by Research

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