IMAGEJ THRESHOLD TO SELECTION UPDATE
IMAGEJ THRESHOLD TO SELECTION CODE

setOption('black background', true) // set 'Black Background' in 'Binary Options' Only when explicitly/implicitly setting a threshold, we manage to make it predictably select the foreground (area 9200px). Depending on ImageJ’s state, the “ Create Selection” command sometimes TM selects the black squares (pixel values “0”, area 800px) instead of the white foreground. It is first resetting the background option to black, then creating a 100x100px white image and two black 20x20px squares.

IMAGEJ THRESHOLD TO SELECTION CODE

This makes it somewhat difficult (or at least counter-intuitive) to have the code behave consistently. We managed to trace our problems down to the changing behavior of the run('Create Selection') command.Īccording to the documentation and/or the User Guide we are expecting the command to respect the “ Black Background” settings in Process > Binary > Options… - unfortunately in our experience that’s not true but the result rather depends on the settings of other functions used before (most notably thresholding). IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2), pp.113–125.We ( and myself) are struggling hard to make one of our macros work in a consistent manner. An unbiased detector of curvilinear structures. We think the best way is to cite the formal method and the used implementation: Self-assembly of hierarchically ordered structures in DNA nanotube systems. Glaser, M., Schnauß, J., Tschirner, T., Schmidt, S., Moebius-Winkler, M., Käs, J.

This is a list of publications where the plugin was used: If you use ImageJ just copy the RidgeDetection.jar file in your plugins folder and copy the apache-commons-lang 3 jar file into the plugins/jars folder.

IMAGEJ THRESHOLD TO SELECTION UPDATE

Simply turn on the Biomedgroup update site, which includes the ridge detection plugin. Line 134 is selected to illustrate the selection of a complete line despite numerous intersections. Successful ridge detection with slope-based overlap detection enabled. In this case due to a superfluous junction point at the bottom of one arm, leading to a misdiagnosis of what lines are overlapping. In this image we see poor overlap detection. These are 'real' junction points that will allow reasonable overlap detection via line slope. This detection with a sigma of 1.6 produces a set of lines and junctions not suited to slope-based overlap detection. If you use this method of overlap resolution, it is recommended that you first tune the Ridge Detection parameters with Preview enabled to get a minimal starting set of junction points - so that each ridge matches reality as best as possible. This is best suited to datasets with brief periods of overlap! If two ridges have a significant portion of overlap, the accuracy of this method will rapidly diminish. This method makes the assumption that when two ridges overlap, it is more likely that they will continue on their path than make turns. Any point of potential intersection will be treated as an end point for the ridges involved.

The default behavior: no assumption of overlap is made. The accuracy of this method will depend on the structure of your data. You can select a method to attempt automatic overlap resolution. Verbose mode: If this option is selected, status information will be printed to the log while running.ĭisplay results: If this option is selected, all contours and junctions are filled into a results table.Īdd to Manager: All lines and junctions points will be added to the roi manager. Show IDs: The ID of each line will be shown. Show junction points: If this option is selected the junctions points will be displayed. Correct position: Correct the line position if it has different contrast on each side of it.Įstimate width: If this option is selected the width of the line is estimated.

Imagej threshold to selection