Book Image

Image Processing with ImageJ - Second Edition

Book Image

Image Processing with ImageJ - Second Edition

Overview of this book

Advances in image processing have been vital for the scientific and technological communities, making it possible to analyze images in greater detail than ever before. But as images become larger and more complex, advanced processing techniques are required. ImageJ is built for the modern challenges of image processing – it’s one of the key tools in its development, letting you automate basic tasks so you can focus on sophisticated, in depth analysis. This book demonstrates how to put ImageJ into practice. It outlines its key features and demonstrates how to create your own image processing applications using macros and ImageJ plugins. Once you’ve got to grips with the basics of ImageJ, you’ll then discover how to build a number of different image processing solutions. From simple tasks to advanced and automated image processing, you’ll gain confidence with this innovative and powerful tool – however and whatever you are using it for.
Table of Contents (17 chapters)
Image Processing with ImageJ Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
2
Basic Image Processing with ImageJ
Index

Image segmentation


For many steps in image analysis, it is important to split the image into two separate (non-overlapping) components. These components are usually labeled as background and foreground. Generally speaking, the background is the part of the image we are not directly interested in when we analyze the image. We normally restrict our analysis to parts of the image that are deemed as the foreground. This splitting into two components is called segmentation and is primarily based on pixel intensity. This is important if you wish to count and measure a number of unique objects of a specific type or measure the intensity of a single complex object while excluding the background from the measurement.

Image thresholding

To achieve the split of an image into background and foreground, we will set a threshold value. Values below this threshold will be classified as one group, while pixels with higher or equal values will be classified as another group. In general, the background in fluorescent...