Image Processing with ImageJ - Second Edition: Extract and analyze data from complex images with ImageJ, the world's leading image processing tool

Table of Contents

Image Processing with ImageJ Second Edition

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Getting Started with ImageJ

ImageJ distributions

The uses of ImageJ

The current state of ImageJ

ImageJ2

SciFIO and OME-XML

Bio-formats

Integrated environment for acquisition and processing

Obtaining and installing ImageJ

Installation of ImageJ

Installing on Windows

Installing on Mac OS X

Installing on Linux

The ImageJ folder structure

Plugins folder

Macros folder

Configuring a fresh ImageJ installation

Summary

2. Basic Image Processing with ImageJ

Images in ImageJ

Image types

Grayscale images

Color images

Stacks and hyperstacks

Color images and multichannel stacks

Z-stack images and volumes

Time series

Multidimensional images

Extracting image and pixel information

Loading and saving images

Loading images and sequences

Saving images

Image calibration

Viewing images in ImageJ

Viewing multichannel images

Viewing time series

Summary

3. Advanced Image Processing with ImageJ

Correcting images

Technical background

Correcting Shot noise

Correcting dark noise

Uneven illumination – background subtraction

Image normalization

Bleach correction

Stack processing

Processing Z-stacks

Stack projections

Maximum projection

Volume viewing and rendering

Processing time series

Normalizing time series data

Summary

4. Image Segmentation and Feature Extraction with ImageJ

Image segmentation

Image thresholding

Thresholding grayscale images

Thresholding color images

Morphological processing

Morphological operators

Erode and dilate

Skeletonize and watershed

Image filtering

Filtering in the frequency domain

Image filtering in the spatial domain

Feature extraction

Edge detection

Summary

5. Basic Measurements with ImageJ

Selections and regions in ImageJ

Area selections

Line selections

Point selections

Basic measurements

Area selections and measurements

Oval selections

Polygon selections

Line selections and measurements

Kymographs

Line profiles

Colocalization

Semiquantitative colocalization

Particle analysis

Preprocessing and preparations

Summary

6. Developing Macros in ImageJ

Recording macros

Recording a macro for conversion

Modifying macros

User input in macros

Opening a specific file

Saving an image to a folder

Adding choices

Performing input checking

Showing progress in macros

Processing the time series

Running macros in batch process mode

Installing macros

Summary

7. Explanation of ImageJ Constructs

Frameworks for macros and plugins

Macros and scripting languages

BeanShell scripting

ImageJ main class

Functions to process images

Functions for selections

Saving and running your scripts

Plugins for ImageJ

ImageJ main class

WindowManager

ImagePlus

ImageProcessor

RoiManager

The Roi class

The Application Programming Interface

Setting up NetBeans IDE

Gathering all components

Setting up a project

Building ImageJ

Creating a plugin

Creating documentation

ImageJ Javadoc

Plugin Javadoc

Developing plugins using Maven

Construction of the POM

Creating a Maven plugin project

Creating an ImageJ2 plugin

Pros and cons of using an IDE

Summary

8. Anatomy of ImageJ Plugins

The basic anatomy of a plugin

Legacy plugins

The PlugIn type

The PlugInFilter type

The PlugInFrame type

Implementing a legacy plugin

Combining macros and legacy plugins

SciJava plugins

The @Plugin annotation

Services

Commands

Running and debugging plugins

Compiling plugins

Compiling SciJava plugins

Debugging plugins

Examples of available plugins

Example plugins available in ImageJ and Fiji

MultipleKymograph

ColorTransformer2

MtrackJ

Coloc2

Goutte_pendante

Summary

9. Creating ImageJ Plugins for Analysis

Plugin background and goal

Basic project setup

Creating a basic PlugInFilter

Testing our current implementation

Implementing the setup method

The return type and autocomplete

Javadoc for methods

Finishing the setup method

Implementing the run method

Detecting an object

Refining the detection

Detecting multiple objects

Implementing the measurements

Adding user interaction and preferences

Settings and options dialog

Adding external libraries

Adding the dependency for Apache POI

Creating an Excel file

Sharing your plugin

Creating a site

Uploading your plugin

Summary

10. Where to Go from Here?

Basic development

Additional tools

Project management and feedback

Other resources

Summary

Index

Image Processing with ImageJ Second Edition

Image Processing with ImageJ Second Edition

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: September 2013

Second edition: November 2015

Production reference: 1241115

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78588-983-7

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Credits

Authors

Jurjen Broeke

José María Mateos Pérez

Javier Pascau

Reviewer

Jan Eglinger

Commissioning Editor

Neil Alexander

Acquisition Editor

Manish Nainani

Content Development Editor

Sumeet Sawant

Technical Editor

Parag Topre

Copy Editor

Karuna Narayanan

Project Coordinator

Shweta H Birwatkar

Proofreader

Safis Editing

Indexer

Monica Ajmera Mehta

Graphics

Disha Haria

Production Coordinator

Conidon Miranda

Cover Work

Conidon Miranda

About the Authors

Jurjen Broeke has a PhD in neuroscience from Vrije Universiteit (VU) Amsterdam and uses live-cell imaging techniques to study the fundamental processes of neuronal function. As a neuroscientist, he studies the processes involved in neural communication. Besides acquiring images, Jurjen also develops software to analyze dynamics in ImageJ, MATLAB, and R. When not enjoying the outdoors and taking pictures, he develops technical hardware and software solutions in the Department of Functional Genomics at VU.

José María Mateos Pérez is a Spanish postdoctoral fellow at the Montreal Neurological Institute (http://www.mcgill.ca/neuro/), where his main research lines deal with neurodevelopment and machine learning applied to clinical prediction. He has also been an experienced ImageJ user and has developed several macros and plugins. One of them, jClustering, has been published in PLOS ONE, a peer-reviewed journal. When he has enough time to procrastinate, he also likes to develop data analysis tools in Python and R.

Javier Pascau received his PhD from Polytechnic University in Madrid in 2006 and is currently a visiting professor at Carlos III University, Madrid. He has been a part of Biomedical Imaging and Instrumentation Group, a research laboratory with a multidisciplinary team of engineers, physicists, biologists, and physicians located both in the university as well as Hospital General Universitario Gregorio Marañón (biig.uc3m.es). Javier's research and teaching cover areas such as medical image processing, analysis, quantification, and multimodal registration, both in preclinical and clinical environments. He has been involved in the development of small animal PET and CT devices. In the last few years, Javier has led several projects on intraoperative radiation therapy and image-guided surgery. He has authored more than 40 papers published in peer-reviewed journals over the last 15 years.

I want to thank all my colleagues at the university and the hospital, since my knowledge on image processing is the result of multiple interactions in this multidisciplinary environment.

About the Reviewer

Jan Eglinger works as an image processing specialist at Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Jan received a master's degree in biotechnology from ESBS in Strasbourg, France, and a PhD in cell biology from MPI-CBG in Dresden, Germany. He has been contributing to Fiji and ImageJ development since 2010.

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Preface

Advances in image processing are vital for the science and technology communities. However, as images become larger and more complex, even more advanced processing techniques are required. Automation becomes necessary too so that you can perform simple tasks easily and focus on more sophisticated issues. ImageJ is here to help—as one of the key powerful tools in the development of image processing, it lets you extract even more useful data from your images.

What this book covers

Chapter 1, Getting Started with ImageJ, takes a look at the origin and use of ImageJ and discusses how to download and install it on different platforms. We will also take a look at the basic folder structure of ImageJ installation and configure it to be used.

Chapter 2, Basic Image Processing with ImageJ, discusses the different image types that are supported by ImageJ. You will also learn how to load images from a disk or URL. We will take a look at the anatomy of an image window in ImageJ and the information that can be viewed. It will also deal with image scaling, calibration, lookup tables, adjusting image size, and adjusting channels.

Chapter 3, Advanced Image Processing with ImageJ, investigates the processing of different types of images. We will take a look at different sources of noise that can corrupt images and degrade their quality. You will also learn how to apply different corrections to images to fix these problems.

Chapter 4, Image Segmentation and Feature Extraction with ImageJ, looks at the ways to separate an image into a foreground and background. We will consider different methods to set the threshold in grayscale and color images.

Chapter 5, Basic Measurements with ImageJ, considers some methods to measure the parameters within images and time series. We will apply some of the techniques discussed in previous chapters to extract data from our images. You will also learn how to visualize dynamic data in a single image (kymographs).

Chapter 6, Developing Macros in ImageJ, discusses how to create a macro using a recorder to discover the commands and functions that we can apply. Next, we will take a look at processing a folder full of images and saving the resulting images to the hard disk. Finally, we will look at the Batch Process mode, which allows ImageJ to process a folder in a similar way.

Chapter 7, Explanation of ImageJ Constructs, looks at the framework of macros and plugins that are available in ImageJ. We will discuss some of the constructs that the ImageJ API exposes for use in scripting and plugins. Finally, we will describe how to set up an IDE to develop ImageJ and plugins using it as a standalone or Maven-based project.

Chapter 8, Anatomy of ImageJ Plugins, takes a look at the anatomy of plugins for ImageJ1.x and ImageJ2. We will also take a look at some of the specific constructs that are used in plugins for both frameworks. This chapter examines how to compile, run, and debug plugins using the IDE or tools provided by ImageJ.

Chapter 9, Creating ImageJ Plugins for Analysis, develops a plugin from scratch using the Maven system and NetBeans IDE. We will discuss how to add a basic user interface to our plugin, allowing the user to change some of the parameters that influence the way the plugin functions. We will also add an external library to provide additional functionality that was not present in ImageJ.

Chapter 10, Where to Go from Here, sums up the topics that are discussed in previous chapters and provides further resources that are available for you to continue developing your own plugins. The chapter also looks at some of the more advanced techniques that are available for developers.

What you need for this book

You'll need the following software for the book:

ImageJ 1.4x or Fiji

NetBeans 8.0.2+

Who this book is for

This book is created for engineers, scientists, and developers eager to tackle image processing with one of the leading tools in the field for image processing and analysis. No prior knowledge of ImageJ is needed. Familiarity with Java programming will be needed for readers to code their own routines using ImageJ.

Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: The two most important folders are the macros and plugins folders.

A block of code is set as follows:

varmyTools = newMenu(My awesome tools,

newArray(Macro_1, Macro_2, -, Macro_3));

macroMy awesome tools - C037T0b11MT7b09aTcb09t {

  cmd = getArgument();

  if(cmd== Macro_1)

  runMacro(/PATH/TO/Macro_1_tool);

  else if(cmd == Macro_2)

  runMacro(/PATH/TO/some_other_tool);

}

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: We can now perform the particle analysis by selecting Analyze | Analyze Particles… from the menu.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Downloading the example code

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Errata

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Questions

You can contact us at <[email protected]> if you are having a problem with any aspect of the book, and we will do our best to address it.

Chapter 1. Getting Started with ImageJ

Welcome to the second edition of Image Processing with ImageJ. ImageJ is a versatile and open source software package designed for scientific image processing and analysis. It is written in the Java programming language, allowing for a uniform cross-platform experience. It is based on the NIH Image software package on the Macintosh platform, developed in 1987 by Wayne Rasband. Rasband, who is still an active contributor of ImageJ, published the first ImageJ distribution in 1997. It was developed as a project to provide a solution to a problem. In 2012, ImageJ celebrated its twenty-fifth birthday with a publication in the journal Nature Methods.

ImageJ distributions

Currently, there are different distributions that are based on or are extensions of the original ImageJ. The basic ImageJ package is available on the ImageJ website at the National Institute of Health (http://imagej.nih.gov/ij/download.html). The current version of the package is version 1.50b, and the website is updated monthly. This is the core distribution of ImageJ, which contains the main interface and all the basic tools to load, view, process, and export images and data. Other distributions contain this core package and most of its features, but you need to add additional features and plugins to create an optimized interface for specific fields. Some of these other distributions are still easily recognizable as ImageJ, while others offer a completely different interface.

For different scientific fields, different distributions were developed based on the core of ImageJ. One of the major distributions for the life sciences is called Fiji (Fiji Is Just ImageJ), which can be found on the Fiji website (http://fiji.sc/Fiji). The basis of Fiji is ImageJ, but it comes with a large complement of preinstalled features (macros and plugins) that are commonly used for image processing in the life sciences. It is focused on fluorescence microscopy, with built-in tools for segmentation, visualization, and co-localization. It also contains plugins for image registration, particle tracking, and super-resolution processing and reconstruction. It also has an extensive library of image formats that can be opened. This library includes proprietary image formats from all the major acquisition software packages via the Bio-Formats plugin, as described in the upcoming section. The advantages of this distribution are the large number of supplied plugins that come with it as well as a very user-friendly script editor. It also has an extensive update mechanism for both ImageJ as well as some plugins.

For the field of astronomy, a different distribution of ImageJ was developed, named AstroImageJ (http://www.astro.louisville.edu/software/astroimagej/). This distribution takes the core implementation of ImageJ and supplements it with specific plugins and macros developed for analysis in the field of astronomy. It is not directly compatible with ImageJ. The core of ImageJ was slightly modified for this distribution.

An example of a distribution derived from ImageJ but with a different user interface is Icy (http://icy.bioimageanalysis.org/). The Icy distribution has integrated ImageJ, and many plugins are compatible. However, not every plugin developed for ImageJ will work within Icy and vice versa. In the Icy distribution, there is a strong emphasis on cellular and spot detection and tracking. There is also a strong emphasis on plugin development. Plugins that are developed for the Icy platform will have documentation and automated updating implemented by design. There are also possibilities for users to directly provide feedback to the developers from within the interface, which is a feature not present within other distributions based on ImageJ. A disadvantage may be that it requires several external libraries to be installed, most importantly VTK, which can cause issues on Linux systems.

Another distribution that uses ImageJ not only for the processing of data but also aids in the acquisition of data is called μManager, which can be found at https://www.micro-manager.org/. It is loaded from within ImageJ as a plugin, but provides a unique interface geared towards image acquisition and hardware control. Camera and microscope drivers allow the control of supported hardware used in image acquisition, which can then be fed directly to ImageJ for processing and analysis. An example of the use of μManager is in the Open SPIM project, where it is used to control a DIY light sheet microscope, acquire images, and process them.

The uses of ImageJ

ImageJ is a great tool to process images and perform analysis. It is used in many scientific peer-reviewed publications, with over 1000 articles in diverse fields such as life sciences, astronomy, and physics. In life sciences, it is used to quantify medical images to aid in the detection of pathological markers. It is also used to process and quantify data from single-cell or single-molecule experiments using super-resolution