Web microanalysis of big image data /
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| Author / Creator: | Bajcsy, Peter, author. |
|---|---|
| Imprint: | Cham : Springer, [2018] ©2018 |
| Description: | 1 online resource : illustrations (some color) |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11544134 |
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| 100 | 1 | |a Bajcsy, Peter, |e author. | |
| 245 | 1 | 0 | |a Web microanalysis of big image data / |c Peter Bajcsy, Joe Chalfoun, Mylene Simon. |
| 264 | 1 | |a Cham : |b Springer, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed January 31, 2018). | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a This book looks at the increasing interest in running microscopy processing algorithms on big image data by presenting the theoretical and architectural underpinnings of a web image processing pipeline (WIPP). Software-based methods and infrastructure components for processing big data microscopy experiments are presented to demonstrate how information processing of repetitive, laborious and tedious analysis can be automated with a user-friendly system. Interactions of web system components and their impact on computational scalability, provenance information gathering, interactive display, and computing are explained in a top-down presentation of technical details. Web Microanalysis of Big Image Data includes descriptions of WIPP functionalities, use cases, and components of the web software system (web server and client architecture, algorithms, and hardware-software dependencies). The book comes with test image collections and a web software system to increase the reader's understanding and to provide practical tools for conducting big image experiments. By providing educational materials and software tools at the intersection of microscopy image analyses and computational science, graduate students, postdoctoral students, and scientists will benefit from the practical experiences, as well as theoretical insights. Furthermore, the book provides software and test data, empowering students and scientists with tools to make discoveries with higher statistical significance. Once they become familiar with the web image processing components, they can extend and re-purpose the existing software to new types of analyses. Each chapter follows a top-down presentation, starting with a short introduction and a classification of related methods. Next, a description of the specific method used in accompanying software is presented. For several topics, examples of how the specific method is applied to a dataset (parameters, RAM requirements, CPU efficiency) are shown. Some tips are provided as practical suggestions to improve accuracy or computational performance. | ||
| 505 | 0 | |a Intro; Preface; Terminology; Acknowledgments; Disclaimer; Abbreviations; Contents; Chapter 1: Introduction to Big Data Microscopy Experiments; 1.1 Image Processing Pipeline; 1.2 Web Image Processing Pipeline; 1.3 Big Data Microscopy Experiments; 1.4 Motivation of Big Data Microscopy Experiments; 1.5 Range of Applications Leveraging Image Processing Pipelines; 1.6 Challenges of Big Data Microscopy Experiments; 1.7 Considerations Before and After Digital Images Are Acquired; 1.8 Enabling Reproducible Science from Big Data Microscopy Experiments; References. | |
| 505 | 8 | |a Chapter 2: Functionality of Web Image Processing Pipeline2.1 Deploying and Testing the Web Image Processing Pipeline; 2.1.1 Types of Deployment; 2.1.2 Deployment of Docker Containers; 2.1.3 Deployment Recommendations; 2.1.4 Test Data and Computational Benchmarks; 2.2 Web Image Processing Module; 2.2.1 Web Image Processing Module Processing Functionality; 2.2.2 Description of WIP Module Usage; 2.3 Web Feature Extraction Module; 2.3.1 WFE Module Processing Functionality; 2.3.2 WFE Module Usage; 2.4 Web Statistical Modeling Module; 2.4.1 WSM Module Processing Functionality. | |
| 505 | 8 | |a 2.4.2 WSM Module Usage2.5 Summary; References; Chapter 3: Example Use Cases; 3.1 Cell Count and Single Cell Detection; 3.1.1 Image Processing Workflow; 3.1.2 Create a New Image Collection; 3.1.3 Stitching of Image Tiles; 3.1.4 Intensity Scaling and Pyramid Building; 3.1.5 Image Assembling; 3.1.6 Segmentation; 3.1.7 Binary Image Labeling; 3.1.8 Feature Extraction and Single Cell Detection; 3.1.9 Discussion; 3.2 Stem Cell Colony Growth Computation; 3.2.1 Image Processing Workflow; 3.2.2 Colony Tracking and Feature Extraction; 3.2.3 Discussion; 3.3 Image Feature Variability and Its Impact. | |
| 505 | 8 | |a 3.3.1 Image Processing Workflow3.3.2 Image Feature Variability Analysis; 3.3.3 Discussion; 3.4 Summary; References; Chapter 4: Components of Web Image Processing Pipeline; 4.1 Mapping Functionality to Information Technologies; 4.2 The Basics of Client-Server Architecture; 4.2.1 The Role of Each Technology in the Client-Server Architecture; 4.3 The Basics of Web Servers and Browsers; 4.4 The Basics of Communication Protocols in Client-Server Architectures; 4.4.1 Client-Server Communication Using Hypertext Transfer Protocol; 4.4.2 Transmission Control Protocol (TCP). | |
| 505 | 8 | |a 4.4.3 Message Passing Interface4.4.4 Network File System; 4.5 Designing Interactive User Interfaces in Web Browsers; 4.5.1 Model-View-Controller Design Pattern; 4.5.2 AngularJS for Building Interactive User Interfaces; 4.6 Large Image Visualization and Processing in Web Browsers; 4.7 Representation of Large Images; 4.7.1 Large Image Visualization in Web Browsers; 4.7.2 Image Processing in Web Browsers; 4.8 Managing Images, Pyramids, and Metadata; 4.8.1 Relational Databases; 4.8.2 Non-relational Database; 4.8.3 Java Spring Framework for Web Application Development. | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 0 | |a Image processing |x Digital techniques. |0 http://id.loc.gov/authorities/subjects/sh85064447 | |
| 650 | 7 | |a COMPUTERS |x Image Processing. |2 bisacsh | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Applied mathematics. |2 bicssc | |
| 650 | 7 | |a Imaging systems & technology. |2 bicssc | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Image processing |x Digital techniques. |2 fast |0 (OCoLC)fst00967508 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Chalfoun, Joe, |e author. | |
| 700 | 1 | |a Simon, Mylene, |e author. | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319633596 |
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