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PeterBajcsy· JoeChalfoun
MyleneSimon
Web
Microanalysis
of Big Image
Data
Web Microanalysis of Big Image Data
PeterBajcsy • JoeChalfoun • MyleneSimon
Web Microanalysis of Big
Image Data
ISBN 978-3-319-63359-6 ISBN 978-3-319-63360-2 (eBook)
https://doi.org/10.1007/978-3-319-63360-2
Library of Congress Control Number: 2017948649
© Springer International Publishing AG 2018
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
the material is concerned, specically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microlms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology
now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specic statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the
editors give a warranty, express or implied, with respect to the material contained herein or for any errors
or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims
in published maps and institutional afliations.
Printed on acid-free paper
This Springer imprint is published by Springer Nature
The registered company is Springer International Publishing AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
PeterBajcsy
National Institute of Standards
and Technology
Gaithersburg, MD, USA
MyleneSimon
National Institute of Standards
and Technology
Gaithersburg, MD, USA
JoeChalfoun
National Institute of Standards
and Technology
Gaithersburg, MD, USA
v
Preface
We motivate big data microscopy experiments and then introduce the theoretical
and architectural underpinnings of our Web Image Processing Pipeline (WIPP) sys-
tem for analyzing images collected during big microscopy experiments. This book
comes with both the WIPP tool and test image collections, in order to increase the
reader’s understanding and gain experience with practical tools for analyzing big
image experiments. We will describe (a) WIPP functionalities, (b) use cases, and (c)
components of the web software system (web server and client architecture, algo-
rithms, and hardware-software dependencies). Our descriptions of technical details
will follow a top-down presentation and will explain the interactions of the web
system components and their impact on computational scalability, provenance
information gathering, interactive display, and computing.
Our purpose is to encourage graduate students, postdoctoral students, and scien-
tists to perform big data microscopy experiments. We will attempt to achieve this by
providing educational materials, software tools, and test data at the intersection of
research areas known as microscopy image analyses and computational science.
Furthermore, by providing the WIPP software and test data, students and scientists
are empowered with tools to make discoveries with much higher statistical signi-
cance than before. Once they become familiar with the web image processing com-
ponents, they can extend and re-purpose the existing software for new types of
analyses.
While there have been a multitude of books about microscopy image processing,
there is increasing interest in running these processing algorithms on big micros-
copy image data. However, when analyzing big data microscopy experiments, sci-
entists are restricted by the image processing methods designed for desktop
computers, the time it takes to complete desktop intensive processing, and the com-
plexity of the required big data computational infrastructure. We hope that our read-
ers will nd this book to be a useful resource when learning about solutions that can
overcome these restrictions.
We ordered the chapters so that readers are rst introduced to the problem of big
data microscopy experiments (Chap. 1), can install open-source software and
become familiar with the capabilities of the web image processing pipeline
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