select window threshold imagej macro language

Historically, there has been a problem with my preferred approach to use the ROI Manager output from Analyze ‣ Analyze Particles…. It can output summary results and add ROIs for each region to the ROI Manager.

Its various options also make it possible to ignore regions that are particularly small or large, straight or round (using a Circularity metric). When you want to measure multiple objects quickly, Analyze ‣ Analyze Particles… is arguably the most automated and versatile option. I might not necessarily use the single ROI for measurements in the end, but it can be very good for orientation. I most often use it in combination with Edit ‣ Selection ‣ Restore Selection to visualize what has been detected on top of another image.

I like the fact that Edit ‣ Selection ‣ Create Selection provides a very quick way to convert a thresholded region to a single ROI. That way I can visualize what any of these commands will treat as foreground: with the default thresholding display, the foreground is highlighted in red. In fact, because it’s not 100% clear whether black or white pixels represent the foreground in an ImageJ binary image, I tend to set a threshold even on an image that is already binary. Therefore you may not need to press Apply in the Threshold dialog at all. The Wand tool, Edit ‣ Selection ‣ Create Selection & Analyze ‣ Analyze Particles… can also be used when a threshold is being previewed on an image, but it has not yet been converted to binary. In 2D, there are several options to generate ROIs from a thresholded image, each with slightly different applications:Ĭlick on an object with the Wand tool – this is good for interactively choosing one connected region.Įdit ‣ Selection ‣ Create Selection – this makes a single ROI containing all the foreground pixels.ĭisconnected regions can be separated by adding the ROI to the ROI Manager and choosing More > Split.Īnalyze ‣ Analyze Particles… – this detects and measures all the foreground regions as individual objects, optionally filtering out objects based on shape or area.Ĭreating ROIs without applying a threshold ROIs are a good way to represent objects in ImageJ, because they are easy to measure. Once we have a binary image, the next step is to identify objects for further exploration. The various automated thresholds are described at, often with references to the original published papers upon which they are based. Image ‣ Adjust ‣ Auto Threshold… helps with this, by providing an option to try all of the methods.Īpplying this to ImageJ’s famous blobs.gif reveals that not all methods work equally well: The Threshold dialog is good for interactively exploring different automated thresholding methods, but it can be hard to systematically compare them. Therefore if you find that any processing of binary images gives odd results, be sure to check the binary options and LUT status. Nevertheless, you should be aware that this convention is not adopted universally.įurthermore, if you choose Invert LUT then the colors are flipped anyway – so yet more confusion arises. The interesting things we have detected), and so I will assume that the Black background option has been checked. Personally, I prefer for white to represent the foreground (i.e. To complicate matters, ImageJ also permits either of these to represent the foreground, with the choice hidden away under Process ‣ Binary ‣ Options…, and 0 taken to be ‘black’ and 255 ‘white’. This replaces the original, so it may be wise to duplicate the image first.Īlthough only one 1 bit is really needed for each pixel in a binary image, the implementation in ImageJ currently uses 8 bits – and so the actual pixel values allowed are 0 and 255. There is also a drop-down menu allowing you to select from a list of automated thresholding methods.ĭuring preview, the pixels that are considered foreground are shown in red by default (it’s possible to change this, but I never do).Īfter choosing suitable thresholds, pressing Apply produces a binary image.

These options are controlled using a combination of the threshold sliders and the Dark background checkbox. This opens a Threshold dialog that allows you to identify pixels above a threshold, below a threshold, or falling between two thresholds. The main thresholding command in ImageJ is Image ‣ Adjust ‣ Threshold…, with the shortcut Shift+ T. We will also confront some of the associated complications. Here, we will explore some ImageJ’s methods to apply thresholds to images, generating binary images, labeled images and ROIs. append ( './././' ) from helpers import * from matplotlib import pyplot as plt from myst_nb import glue import numpy as np from scipy import ndimage Introduction #

Select window threshold imagej macro language