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Image Processing with ImageJ
Image Processing with ImageJ

Image Processing with ImageJ: Get familiar with one of the world's most highly regarded Digital Image processors, ImageJ. This tutorial takes you through every aspect of viewing, processing, and analysing 2D, 3D, and 4D images, clearly and comprehensively.

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Image Processing with ImageJ

Chapter 2. Basic Image Processing with ImageJ

If you followed the instructions found in the preceding chapter, you will have a working ImageJ installation on your system. This chapter includes:

  • Open and save images (from disk and from the ImageJ sample repository)

  • Zoom on the image and obtain pixel values

  • Color, multichannel, and stack images

  • Adjusting your image brightness, contrast, and image size

  • Thresholding

Image reading/writing


In this section, we learn how to open and write different images.

Opening images with a certain format

First of all, we will need an image to open. For this first exercise, we are going to use one of the several public domain images showing the "happy face" on Mars. Go to http://www.msss.com/msss_images/2008/01/31/index.html and download the image you see (happy_face.jpg, image credit to NASA / JPL / Malin Space Science Systems). Store it on your hard disk in a place that is easy to access, as you shall need it right away.

Now run ImageJ, execute File | Open..., and select the file you just downloaded. ImageJ will load the image contents into a new window and you should see something like this:

You just opened your first image using ImageJ. Smile!

We have scaled the image to fit in the page (note the (50%) on the title bar; more on this later), so your window will be bigger. In any case, you can check some common elements to all images opened in ImageJ.

  • Image title: In this...

Zooming on the image and pixel values


Let's stick with the image we just opened for a while, as it will be a good template for the next exercise. If we want to zoom in or out of a given image, we can run the Image | Zoom | In [+] and Image | Zoom | Out [-] menu options. It is much easier to use the shortcuts, (+) and (-). The current zoom level is added to the image title bar if it is different from 100 percent (recheck the figure shown when we opened happy_face.jpg, and you will find the zoom level set to 50%). Now we can try zooming in and out of the tuberculosis_sample.tif file; the position of the mouse pointer on the image helps guiding the zoom in operation, since it is used to center the scaling. When the zoom level is too big, the main window stops getting bigger and the image is cropped. Two purple squares help the user keep track of the location inside the image. When this zoom level has been reached, you can keep the space bar pressed and drag the image around.

Zoomed image displayed...

Color and multichannel images


Color images can be manipulated in different ways in ImageJ. The most basic option would be to use a pseudo color image, which is an 8-bit image in which every intensity value has a corresponding RGB color, resulting in 256 colors. This color mode is called pseudo color or false color because the color correspondence can be manually changed, depending on the color palette (or the Lookup Table) that we select. The GIF image format stores 8-bit images with the color palette included, so when we open a file in this format it will be displayed in color, although values for every pixel will be limited to 256 possibilities. Open the tuberculosis_sample.gif file again using the File | Open menu option. Now navigate to Image | Lookup Tables. In this menu, many LUTs are offered to be selected. Choose the one named Fire and the result will be:

16-bit image with a Lookup Table applied (Fire LUT) in order to display it with pseudo color

Tip

We can apply an 8-bit LUT to a 16...

3D and 4D images – stacks and hyperstacks


Digital images are not only bidimensional (2D) or multichannel (2D with several channels). We may have several 2D images with spatial, temporal, or any kind of relationship between them. These kinds of images are managed by ImageJ as stacks and hyperstacks. Stacks have three spatial dimensions: x, y, and z. Hyperstacks have four dimensions (x, y, z, and time) and both may also have several channels. As you may be thinking, these two kinds of images are really powerful, since almost any kind of single experiment that provides image results which are somehow related can be handled with them. Now, every image position is not a pixel but a voxel (it has volume), and has three or more dimensions. There is no need that all the dimensions are different from one, and consequently stacks are a subgroup of hyperstacks.

Let's open one sample stack to start familiarizing with this concept. Open the sample image described as T1 head (file t1-head.zip). This stack...

Image adjust tools


In this section, we will explore different tools that can be used for basic image processing.

Image histogram and window/level parameters

In this section, we will first understand one basic descriptor of the image intensity content, the image histogram. It will allow us to better understand what the intensity processing tools are doing. The image histogram is a graphical representation of the intensity values present in the image that plots the number of pixels for each intensity value. It shows intensity distribution over the image at a glance, and will help a lot when adjusting the way these intensities are displayed or modified.

Histograms, like most of the concepts in this book, are better understood with examples. Open two test images: happy_face.jpg and tuberculosis_sample.tif. If you look at these images side-by-side, you will notice that the intensity distributions are quite different, that is, happy_face presents a similar intermediate intensity along the image ...

Summary


In this chapter, you have seen how to perform basic image operations using ImageJ on a variety of image formats with special emphasis on adjusting different image parameters such as window/level and size. Now that you are familiarized with these simple operations, you are ready to go on to the next chapter, where we will show you how to perform more complex operations, such as local measurements or filtering.

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Key benefits

  • Learn how to process digital images using ImageJ and deal with a variety of formats and dimensions, including 4D images
  • Understand what histograms, region of interest, or filtering means and how to analyze images easily with these tools
  • Packed with practical examples and real images, with step-by-step instructions and sample code

Description

Digital image processing is an increasingly important field across a vast array of scientific disciplines. ImageJ's long history and ever-growing user base makes it a perfect candidate for solving daily tasks involving all kinds of image analysis processes. Image Processing with ImageJ is a practical book that will guide you from the most basic analysis techniques to the fine details of implementing new functionalities through the ImageJ plugin system, all of it through the use of examples and practical cases. ImageJ is an excellent public domain imaging analysis platform that can be very easily used for almost all your image processing needs. Image Processing with ImageJ will start by showing you how to open a number of different images, become familiar with the different options, and perform simple analysis operations using the provided image samples. You will also learn how to make modifications through ImageJ filters and how to make local measurements using the selections system. You will also find the instructions necessary to record all the steps you perform so they can be saved and re-run on the same image to ensure analysis reproducibility. Finally, you will get to know some different ImageJ plugins and will learn how to implement your own.

Who is this book for?

This book is targeted at scientists, engineers, technicians, and managers, and anyone who wishes to master ImageJ for image viewing, processing, and analysis. If you are a developer, you will be able to code your own routines after you have finished reading this book. No prior knowledge of ImageJ is expected.

What you will learn

  • Install and configure ImageJ and its plugins
  • Open different image formats
  • Move around the image, zoom in and out, and obtain pixel values
  • Master the different selection tools
  • Plot the image profile and understand the histograms
  • Handle 3D and 4D images
  • Use the macro recording system
  • Create your own macros beyond the recorder
  • Build your own plugins using the ImageJ editor
  • Integrate ImageJ with the Eclipse IDE to improve the plugin development process
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Publication date : Sep 23, 2013
Length: 140 pages
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Language : English
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Tools :

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Table of Contents

6 Chapters
Getting Started with ImageJ Chevron down icon Chevron up icon
Basic Image Processing with ImageJ Chevron down icon Chevron up icon
Advanced Image Processing with ImageJ Chevron down icon Chevron up icon
ImageJ Macros Chevron down icon Chevron up icon
ImageJ Plugins for Users Chevron down icon Chevron up icon
ImageJ Plugins for Developers Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(7 Ratings)
5 star 14.3%
4 star 42.9%
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2 star 42.9%
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Michael W. Konrad Jun 04, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
ImageJ is a Goodsend for a scientist who need to analyze images. This book is a clear and comprehensive introduction to ImageJ and all the plugins that you can down load for it. Worth 10X the price.
Amazon Verified review Amazon
rock Dec 21, 2013
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Image processing is a very important task in many fields of science. This book is aimed to show how is possible use at best an open-source and wonderful software as ImageJ; in comparison with other software its strenght is that it keep clear every step done during image processing (in order to avoid error in data analysis) and let perform very advanced taks.Many times new user of a software are not comfortable in learn completely its use or he just needs to perform specific tasks, or simply knows enough well the software, but lacks of some important fundamentals.This book accomplishes in full explaining the wonderful capabilities of ImageJ: it starts from a very low level, introducing the usersto main uses of that software for image processing, proceding with teaching the use of macro language, in order to automatize image analysis tasks; at the end a whole chapter is dedicated to plugins development. Reading the text, one can also simply check the level of his knowledge, but in every chapter is possible find some useful trick.I enjoyed the reading of this book, because I could improve my knowledge in ImageJ. I also appreciated the completeness of text; for this I'm confident to suggest it to all users interested in learn or improve their image processing performances with ImageJ.
Amazon Verified review Amazon
年寄りの冷や水 Jan 02, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
この本に従ってマクロの使い方などを習得することができました。
Amazon Verified review Amazon
C. Labno Dec 06, 2013
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
ImageJ is a powerful program with a steep learning curve. I've been teaching people how to process images with ImageJ for years, and I'm always on the lookout for new resources to make the process easier for myself and my colleagues. This book is a great resource for anyone learning how to use almost any aspect of ImageJ, from absolute beginners just installing the program to more advanced users writing their own plugins and macros.The explanations are clear and concise, but the interactive nature of the tutorials is -- in my view -- the best part of the book's style. The authors let users know at the beginning of the book where to download sample images and encourage users to follow along with the lessons using their own copy of ImageJ. My only complaint so far is that there is little focus on color (RGB) images. The authors do quite a bit of work with multichannel data, but there is little to do with handling RGB images (things like adding a color lookup table to a grayscale image, using the Channels Tool with hyperstacks, etc).I received a digital preview of this book in exchange for my honest review.
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Robin Cole Feb 18, 2016
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A very basic introduction, pretty much what you will find on the introductory website.
Amazon Verified review Amazon
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