Image analysis and platform development for automated phenotyping in cytomics

Leiden Repository

Image analysis and platform development for automated phenotyping in cytomics

Title: Image analysis and platform development for automated phenotyping in cytomics
Author: Yan, Kuan
Publisher: Department of Imaging and Bioinformatics, Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
Issue Date: 2013-11-27
Keywords: Image analysis
High-content screen
Microscopy imaging
High-throughput screen
Machine learning
Abstract: This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general application domain, the efficiency of HT/HC image analysis is highly subjected to image quantity and quality. Accordingly, this thesis will address two major procedures, namely image segmentation and object tracking, in the image analysis step of HT/HC screen study. Moreover, this thesis focuses on expending generic computer science and machine learning theorems into the design of dedicated algorithms for HT/HC image analysis. Additionally, this thesis exemplifies a practical implementation of image analysis and data analysis workflow via empirical case studies with different image modalities and experiment settings. However, the data analysis theorem will be generally illustrated without further expansions. Finally, the thesis will briefly address supplementary infrastructures for end-user interaction and data visualization.
Description: Promotores: J.N. Kok, B. van de Water, Co-Promotor: F.J. Verbeek
With Summary in Dutch
Faculty: Faculteit der Wiskunde en Natuurwetenschappen
Citation: Yan, K., 2013, Doctoral Thesis, Leiden University
ISBN: 97890988917622
Sponsor: Netherlands Bioinformatics Centre

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