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This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.
The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
List of contents
Workflows and Components of Bioimage Analysis.- Measurements of Intensity Dynamics at the Periphery of the Nucleus.- 3D Quantitative Colocalisation Analysis.- The NEMO Dots Assembly: Single-Particle Tracking and Analysis.- Introduction to MATLAB: Image Analysis & Brownian Motion.- Resolving the process of Clathrin Mediated Endocytosis Using Correlative Light & Electron Microscopy (CLEM).
About the author
Dr. Kota Miura is a Bioimage Data Analyst and works with various research groups at the EMBL in Heidelberg, Germany. He is also Vice Chair of NEUBIAS (the Network of European Bioimage Analysts).
Natasa Sladoje is an Associate Professor at the Centre for Image Analysis, Department of Information Technology at Uppsala University in Sweden and a Professor at the Faculty of Technical Sciences, University of Novi Sad, in Serbia. She is also an Associate Research Professor at the Mathematical Institute of the Serbian Academy of Sciences and Arts.
Summary
This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.
The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
