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

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Author(s)
Primary Supervisor
Liew, Alan
Other Supervisors
Blumenstein, Michael
Year published
2016
Metadata
Show full item recordAbstract
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 ...
View more >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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View more >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
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