文件名称：Web Microanalysis of Big Image Data-Springer(2018).pdf
文件大小：8.77MB
文件格式：PDF
更新时间：2021-02-14 09:02:12
Micro analysis Big Image Data
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.