Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Image Processing with ImageJ - Second Edition
  • Table Of Contents Toc
  • Feedback & Rating feedback
Image Processing with ImageJ - Second Edition

Image Processing with ImageJ - Second Edition

By : Jurjen Broeke, Pascau
3.4 (5)
close
close
Image Processing with ImageJ - Second Edition

Image Processing with ImageJ - Second Edition

3.4 (5)
By: Jurjen Broeke, Pascau

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 (12 chapters)
close
close
2
2. Basic Image Processing with ImageJ
11
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...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY