If the program is jumping too easily between particles, try reducing the link range. ![]() You can use the display frame slider to visualize the tracking as it occurs. ![]() I rarely use this option for crowded environments even though it may track more accurately for less crowded environments. Tracking parameters that worked well for me were a min separation of 18 pixels, a threshold fraction of 0.3, and all other parameters at their default including track center of mass. We will threshold relative to the maximum intensity statistic. Next, run the track max not mask jru v2 plugin. Then to denoise the image somewhat, we will use the Gaussian Blur utility to blur with a radius (standard deviation) of 1 pixel. Then we will use the ImageJ Subtract Background utility with a rolling ball radius of 10 pixels. ![]() To begin with, duplicate the first 25 frames of the red channel and convert it to grayscale with Image>Lookup Tables>Grays. These blobs are relatively sparse, so we will track them in 2D on the max projection. The red channel contains multiple blobs that move in time. I would recommend recording a macro with a small scale image and then running that macro on larger scale images to avoid crashes.Ī reasonable demonstration data set for the plugin is the mitosis sample video built into ImageJ (File>Open Samples>Mitosis). The negative impact of this is that it is very crash prone for large scale images. This means that for a small image, you can adjust the parameters to get the best maximum identification in an interactive way. In addition, if the objects to be tracked have significant internal heterogeneity, it is a good idea to blur them (possibly in 3D) with Process>Filters>Gaussian Blur to reduce them as much as possible to single maxima. Prior to running these plugins, the user should duplicate out the desired image channel and appropriately subtract the background (using roi average subtract jru v1 or Process>Subtract Background). If a particle has disappeared in the next frame, it should reappear before Max Link Delay number of frames or it will be considered a new particle. For each point, the closest neighboring point in the subsequent frame is identified as the same object in the next frame as long as it is below the distance (Link Range) cutoff. The threshold is determined based on the fraction of the maximum intensity in the image.Īfter maxima are found in this way, they are tracked in a relatively simple fashion. Then the maximum value is found again and masked until there are no pixels above the threshold value. All pixels within a specified radius around that maximum are deleted ("masked"). Note that the 3D implementation is identical to the 2D implementation but in three dimensions.įirstly, the algorithm finds the maximum intensity pixel in the entire image. Nevertheless, I prefer this implementation when possible because it outperforms more sophisticated implementations by finding dim neighboring blobs when they do not represent true maxima. The "Find Maxima" tool built into ImageJ outperforms this algorithm in terms of computational performance and the restriction to true maxima. In that respect, they are certainly not the most computationally efficient way to track maxima in an image, nor do they strictly find only true maxima in an image. These plugins are not novel in the algorithmical sense-they are based on a very simple premise. These plugins are called track max not mask fast jru v1 and track max not mask 3D fast jru v1. This speeds up analysis significantly but may result in slightly different object positioning at the tile boundaries especially from dense images. They work in much the same way, but perform the object identification (the track max not mask portion) in a tiled fashion, combining the points from the tiles to create the whole data set after the fact. Update: I have recently implemented fast versions of the above plugins. Under these conditions the plugins outperform traditional segmentation algorithms because they rely on uniformity of size among structures-something that is difficult to implement in purely threshold based segmentations. Here I will describe a set of particle tracking plugins (track max not mask jru v2 and track max not mask 3D jru v1) that we also utilize for segmentation of crowded environments. Particle Tracking Plugins ("track max not mask")
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