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

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 ...