This volume of Advances Anatomy Embryology and Cell Biology focuses on the emerging field of bio-image informatics, presenting novel and exciting ways of handling and interpreting large image data sets. A collection of focused reviews written by key players in the field highlights the major directions and provides an excellent reference work for both young and experienced researchers.
Microscope Image Processing, Second Edition, introduces the basic fundamentals of image formation in microscopy including the importance of image digitization and display, which are key to quality visualization. Image processing and analysis are discussed in detail to provide readers with the tools necessary to improve the visual quality of images, and to extract quantitative information. Basic techniques such as image enhancement, filtering, segmentation, object measurement, and pattern recognition cover concepts integral to image processing. In addition, chapters on specific modern microscopy techniques such as fluorescence imaging, multispectral imaging, three-dimensional imaging and time-lapse imaging, introduce these key areas with emphasis on the differences among the various techniques.The new edition discusses recent developments in microscopy such as light sheet microscopy, digital microscopy, whole slide imaging, and the use of deep learning techniques for image segmentation and analysis with big data image informatics and management.Microscope Image Processing, Second Edition, is suitable for engineers, scientists, clinicians, post-graduate fellows and graduate students working in bioengineering, biomedical engineering, biology, medicine, chemistry, pharmacology and related fields, who use microscopes in their work and would like to understand the methodologies and capabilities of the latest digital image processing techniques or desire to develop their own image processing algorithms and software for specific applications. - Presents a unique practical perspective of state-of-the-art microscope image processing and the development of specialized algorithms - Each chapter includes in-depth analysis of methods coupled with the results of specific real-world experiments - Co-edited by Kenneth R. Castleman, world-renowned pioneer in digital image processing and author of two seminal textbooks on the subject
This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake.
A comprehensive guide to the art and science of bioimaging data acquisition, processing and analysis Standard and Super-Resolution Bioimaging Data Analysis gets newcomers to bioimage data analysis quickly up to speed on the mathematics, statistics, computing hardware and acquisition technologies required to correctly process and document data. The past quarter century has seen remarkable progress in the field of light microscopy for biomedical science, with new imaging technologies coming on the market at an almost annual basis. Most of the data generated by these systems is image-based, and there is a significant increase in the content and throughput of these imaging systems. This, in turn, has resulted in a shift in the literature on biomedical research from descriptive to highly-quantitative. Standard and Super-Resolution Bioimaging Data Analysis satisfies the demand among students and research scientists for introductory guides to the tools for parsing and processing image data. Extremely well illustrated and including numerous examples, it clearly and accessibly explains what image data is and how to process and document it, as well as the current resources and standards in the field. A comprehensive guide to the tools for parsing and processing image data and the resources and industry standards for the biological and biomedical sciences Takes a practical approach to image analysis to assist scientists in ensuring scientific data are robust and reliable Covers fundamental principles in such a way as to give beginners a sound scientific base upon which to build Ideally suited for advanced students having only limited knowledge of the mathematics, statistics and computing required for image data analysis An entry-level text written for students and practitioners in the bioscience community, Standard and Super-Resolution Bioimaging Data Analysis de-mythologises the vast array of image analysis modalities which have come online over the past decade while schooling beginners in bioimaging principles, mathematics, technologies and standards.
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.
This book constitutes the refereed proceedings of the 14th International Conference on Brain Informatics, BI 2021, held in September 2021. The conference was held virtually due to the COVID-19 pandemic. The 49 full and 2 short papers together with 18 abstract papers were carefully reviewed and selected from 90 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.
This unique compendium provides state-of-the-art computational methodology and applications in bioimage informatics. It covers cutting-edge technology developments in biological image analysis, where images come from new modalities and are often large scale, high throughput and high dimensional. The book reflects advances in intelligent algorithms for tasks such as biological image segmentation, reconstruction, and object tracking.Contributed by world renowned researchers, this useful reference text presents case studies that can potentially help readers find approaches and resources to address their imminent scientific problems.
This volume of Methods in Enzymology is the first of three parts looking at current methodology for the imaging and spectroscopic analysis of live cells. The chapters provide hints and tricks not available in primary research publications. It is an invaluable resource for academics, researchers and students alike. - Expert authors who are leaders in the field - Extensively referenced and useful figures and tables - Provides hints and tricks to facilitate reproduction of methods
This book emphasizes the latest developments and achievements in artificial intelligence and related technologies, focusing on the applications of artificial intelligence and medical diagnosis. The book describes the theory, applications, concept visualization, and critical surveys covering most aspects of AI for medical informatics.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.
