Pattern recognition in high-throughput zebrafish imaging

Leiden Repository

Pattern recognition in high-throughput zebrafish imaging

Title: Pattern recognition in high-throughput zebrafish imaging
Author: Nezhinsky, Alexander Evgenievich
Publisher: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
Issue Date: 2013-11-21
Keywords: Image analysis
Pattern recognition
High Throughput
Template matching
Zebrafish
Segmentation
Abstract: High Throughput (HT) methods are high volume experimental approaches that are common in the fields of the life-sciences. The instrumentation for these methods differs per application. We will focus on the HT methods that are concerned with imaging. The aim of this thesis is to find robust methods for object extraction and analysis. We focus on the Computer Science aspects of such analysis, namely pattern recognition. Pattern Recognition can be seen in the context of object recognition and data mining. Both aspects will be described in this thesis. We present a framework for segmenting and recognizing the objects of interest based on Template Matching. This approach was designed for an application in the HT screening of zebrafish embryos. All proposed methods are fully automated. We further elaborate on the segmentation algorithms to apply these in software that can be used in a HT context to derive measurements. Then we apply the software on a real life problem involving zebrafish infected with Mycobacterium marinum.
Description: Promotor: J.N. Kok, Co-promotor: F.J. Verbeek
With summaries in Dutch and Russian
Faculty: Faculteit der Wiskunde en Natuurwetenschappen
Citation: Nezhinsky, A.E., 2013, Doctoral thesis, Leiden University
ISBN: 9789461919663
Sponsor: Smartmix
Handle: http://hdl.handle.net/1887/22286
 

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application/pdf Chapter 1 Introduction 154.7Kb View/Open
application/pdf Chapter 2 484.8Kb View/Open
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application/pdf Bibliography 110.7Kb View/Open
application/pdf Summary in Dutch 99.00Kb View/Open
application/pdf Summary in Russian 100.6Kb View/Open
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