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Web Microanalysis of Big Image Data, Peter Bajcsy; Joe Chalfoun; Mylene Simon


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Автор: Peter Bajcsy; Joe Chalfoun; Mylene Simon
Название:  Web Microanalysis of Big Image Data
ISBN: 9783319875330
Издательство: Springer
Классификация:




ISBN-10: 3319875337
Обложка/Формат: Soft cover
Страницы: 197
Вес: 0.34 кг.
Дата издания: 2018
Язык: English
Издание: Softcover reprint of
Иллюстрации: 93 illustrations, color; 10 illustrations, black and white; xx, 197 p. 103 illus., 93 illus. in color.
Размер: 234 x 156 x 12
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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 readers 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.
Дополнительное описание: 1 Introduction.- 2 Using Web Image Processing Pipeline for Big Data Microscopy Experiments.- 3 Example Use Cases.- 4 Building Web Image Processing Pipeline for Big Images.- 5 Image Processing Algorithms.- 6 Interoperability Between Software and Hardware.-


Web Microanalysis of Big Image Data

Автор: Peter Bajcsy; Joe Chalfoun; Mylene Simon
Название: Web Microanalysis of Big Image Data
ISBN: 3319633597 ISBN-13(EAN): 9783319633596
Издательство: Springer
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Цена: 93160.00 T
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Описание:

Table of Contents. 1

Preface

1 Introduction. 1

1.1 What is image processing pipeline?. 1

1.2 What does web image processing pipeline consist of?. 3

1.3 What are big data microscopy experiments?. 4

1.4 Why are scientists interested in big data microscopy experiments?. 6

1.5 What is the range of applications leveraging image processing pipelines?. 9

1.6 Challenges of big data microscopy experiments. 10

1.7 Tradeoffs before and after digital images are acquired. 12

1.8 Enabling reproducible science from big data microscopy experiments. 14

2 Using Web Image Processing Pipeline for Big Data Microscopy Experiments. 1

2.1 Deploying and Testing the Web Image Processing Pipeline. 2

2.1.1 Types of deployment 4

2.1.2 Deployment of Docker Containers. 6

2.1.3 Deployment recommendations. 7

2.1.4 Test data and computational benchmarks. 8

2.2 Web Image Processing. 10

2.2.1 WIP processing functionality. 10

2.2.2 Examples of WIP usage. 12

2.3 Web Feature Extraction. 15

2.3.1 WFE processing functionality. 17

2.3.2 WFE usage. 19

2.4 Web Statistical Modeling. 21

2.4.1 WSM processing functionality. 23

2.4.2 WSM use case. 24

2.5 Summary. 25

3 Example Use Cases 1

3.1 Cell count and single cell detection. 1

3.1.1 Image processing pipeline. 2

3.1.2 Create a new image collection. 3

3.1.3 Stitching of image tiles. 4

3.1.4 Intensity scaling and pyramid building. 5

3.1.5 Image assembling. 6

3.1.6 Segmentation. 7

3.1.7 Binary image labeling. 8

3.1.8 Feature extraction and single cell detection. 8

3.1.9 Discussion. 9

3.2 Stem cell colony growth computation. 10

3.2.1 Image processing pipeline. 11

3.2.2 Colony tracking and feature extraction

3.2.3 Discussion. 13

3.3 Summary. 15

4 Building Web Image Processing Pipeline for Big Images. 1

4.1 Mapping functionality to information technologies. 1

4.2 The role of each technology in the client-server architecture. 5

4.3 &nbs


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