Template Matching

Template Matching

Author: Walter O'Dell PhD, wodell at rochester.edu

History: 2005/06/30: First version
2005/07/05: Removes unused image window sliders and fixes window list bug

Source: Contained in Create_Template.jar, which can be opened using a ZIP utility

Installation: Download Create_Template.jar to the plugins folder, or subfolder, restart ImageJ, and there will be a new "Create Template" command in the Plugins menu or submenu.

Description: The purpose of this plugin is to perform template matching for detection of objects in an image. This plugin was created initially as a teaching tool for an image processing class. The idea of template matching is to create a model of an object of interest (the template, or kernel) and then to search over the image of interest for objects that match the template. The underlying math here computes, for each pixel in the image of interest, the normalized cross-correlation coefficient (NCCC) between the template and the underlying pixels in the image of interest. A perfect match would give an NCCC value of +1; comparison with an exact negative (inverted grayscale values) gives an NCC of -1; while a comparison with a completely unrelated template gives an NCCC of 0. The NCCC computation inherently corrects for any background image intensity variations and differences in lighting and overall contrast. It keys in only on the relative size and relative intensity contrast of the pixels under the template.

Create Disk Template button: The classic example is detection of cells. An example image is provided. Running the plugin brings up an interface window that allows one to create circular templates of any size. This is handy if your objects somewhat resemble disks, which they do for these cells. Here the cells are ~30 pixels in diameter so I would create a disk that is ~15 pixels in radius.
Inverty template button: In this example the template image should be inverted so that the template has a bright center with a darker background to match the appearance of the cells.
Crop Template from Image button If there are many like-objects to be detected then it is useful to draw an ROI around one representative object and then to use the Crop Template from Image button to apply that ROI as the template. This works amazingly well. If you want to use this Cropped ROI for other images then just save that kernel image to file using the regular ImageJ file saving menu buttons.
Load Template from File button: If there is an ROI image already saved to a file, or if you decided to create a template using an external program, then you can load that pre-existing template from file and use it as the current tempalte.
Perform Statistical Correlation: Hitting this button then invokes the computation of the NCCC based on the current template image. The Correlation image is then shown. The intensity assigned to each pixel represents the NCCC value computed with the template image centered over that pixel location. Beware that this is essentiall an O(n^3) operation so having a very large base image or template image will cause this computation to take a few minutes. For testing I often shrink my base image by a factor of 2.
set threshold and get particle analyzer results: The particle counter and analyzer functions already in ImageJ are integrated here for convenience. The threshold levels and particle sizes are those of the built-in particle analyzer and are applied to the correlation image, as evident by the program turning the corresponding pixels 'red'. The qualifying particles are then outlined with a green box on both the correlation and the original image. The results of the number of counts and their pixel locations are reported in the Results text window.
Two things of general interest to developers:

The ArrayDisplay class is really quite a useful addition. It greatly simplifies the creation and usage of image windows and display of 2 and 3 dimensional data. The coder no longer has to worry about keeping track of the ImagePlus and ImageProcessing classes and their associated windows and graphics and canvas classes, as the ArrayDisplay class makes all these transparent. It also has so much overloading of functions it can handle just about any type of 2D and 3D dataset. In my modified version of ImageJ at home I have an ArrayDisplay that has greater functionality and I have an ArrayColorDisplay to enable overlaying of two images in different colors -- useful for evaluating the quality of image regitration.
The ImageTools class is a collection of useful general math as well as image processing routines. Even some simple things like the square root function for floats does not exist in basic Java, so this has those and many others. Again I have a more complete version at home but the functions here suffice for the Create_A_Template plugin.

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Author:	Walter O'Dell PhD, wodell at rochester.edu
History:	2005/06/30: First version 2005/07/05: Removes unused image window sliders and fixes window list bug
Source:	Contained in Create_Template.jar, which can be opened using a ZIP utility
Installation:	Download Create_Template.jar to the plugins folder, or subfolder, restart ImageJ, and there will be a new "Create Template" command in the Plugins menu or submenu.
Description:	The purpose of this plugin is to perform template matching for detection of objects in an image. This plugin was created initially as a teaching tool for an image processing class. The idea of template matching is to create a model of an object of interest (the template, or kernel) and then to search over the image of interest for objects that match the template. The underlying math here computes, for each pixel in the image of interest, the normalized cross-correlation coefficient (NCCC) between the template and the underlying pixels in the image of interest. A perfect match would give an NCCC value of +1; comparison with an exact negative (inverted grayscale values) gives an NCC of -1; while a comparison with a completely unrelated template gives an NCCC of 0. The NCCC computation inherently corrects for any background image intensity variations and differences in lighting and overall contrast. It keys in only on the relative size and relative intensity contrast of the pixels under the template. Create Disk Template button: The classic example is detection of cells. An example image is provided. Running the plugin brings up an interface window that allows one to create circular templates of any size. This is handy if your objects somewhat resemble disks, which they do for these cells. Here the cells are ~30 pixels in diameter so I would create a disk that is ~15 pixels in radius. Inverty template button: In this example the template image should be inverted so that the template has a bright center with a darker background to match the appearance of the cells. Crop Template from Image button If there are many like-objects to be detected then it is useful to draw an ROI around one representative object and then to use the Crop Template from Image button to apply that ROI as the template. This works amazingly well. If you want to use this Cropped ROI for other images then just save that kernel image to file using the regular ImageJ file saving menu buttons. Load Template from File button: If there is an ROI image already saved to a file, or if you decided to create a template using an external program, then you can load that pre-existing template from file and use it as the current tempalte. Perform Statistical Correlation: Hitting this button then invokes the computation of the NCCC based on the current template image. The Correlation image is then shown. The intensity assigned to each pixel represents the NCCC value computed with the template image centered over that pixel location. Beware that this is essentiall an O(n^3) operation so having a very large base image or template image will cause this computation to take a few minutes. For testing I often shrink my base image by a factor of 2. set threshold and get particle analyzer results: The particle counter and analyzer functions already in ImageJ are integrated here for convenience. The threshold levels and particle sizes are those of the built-in particle analyzer and are applied to the correlation image, as evident by the program turning the corresponding pixels 'red'. The qualifying particles are then outlined with a green box on both the correlation and the original image. The results of the number of counts and their pixel locations are reported in the Results text window. Two things of general interest to developers: The ArrayDisplay class is really quite a useful addition. It greatly simplifies the creation and usage of image windows and display of 2 and 3 dimensional data. The coder no longer has to worry about keeping track of the ImagePlus and ImageProcessing classes and their associated windows and graphics and canvas classes, as the ArrayDisplay class makes all these transparent. It also has so much overloading of functions it can handle just about any type of 2D and 3D dataset. In my modified version of ImageJ at home I have an ArrayDisplay that has greater functionality and I have an ArrayColorDisplay to enable overlaying of two images in different colors -- useful for evaluating the quality of image regitration. The ImageTools class is a collection of useful general math as well as image processing routines. Even some simple things like the square root function for floats does not exist in basic Java, so this has those and many others. Again I have a more complete version at home but the functions here suffice for the Create_A_Template plugin.