Book Image

Image Processing with ImageJ

Book Image

Image Processing with ImageJ

Overview of this book

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.
Table of Contents (13 chapters)
Image Processing with ImageJ
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

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...