Image segmentation

An example macro introduced in the documentation page of the ImageJ plugin Trainable Weka Segmentation (in Fiji, it's bundled). A segmentation protocol based on machine learning. Full macro is available in the "Download" Link. 

This plugin can be trained to learn from the user input and perform later the same task in unknown (test) data. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka. Segmentation: it provides a labeled result based on the training of a chosen classifier. Trainable Weka Segmentation Complete macro example is at the end of the page.

This article Baslat et al. presents a method to compute Lymphatic Vessel Density on an image of the whole slide (a workflow documented as text).

Vessels are obtained with a Maximum Entropy Thresholding applied on the excess Red channel (2 times the red values minus blue+green value). Stroma tissue is obtained with a Moment Preserving Thresholding on the blue channel.

CellX is an open-source software package of workflow template for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images with distinguishable cell boundary.

Installation and step-by-step usage details are described in Mayer et al (2013). 

After users provide a few annotations of cell sizes and cell boundary profiles, it tries to match boundary profile pattern on cells thus provide segmentation and further tracking. It works the best on cells without extreme shapes and with a rather homogeneous boundary pattern. It may not work well on images with cells of sizes only a few pixels. Its output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.

This macro recognizes wells in a picture from a multi-well plate (it works also on a picture of a single well). It is used to segment a picture to determine the number of "Colony Forming Units" in each individual well of a plate.

The steps are the following:

1. Makes a 8-bit B&W picture, inverts it (=> borders will look white instead of black), resizes it (optional, this is to speed-up convolution thereafter) and find edges.
2. Convolves the obtained picture with a kernel corresponding to a thick white circle of the size of the wells. The resulting image has big "blobs" or "particles" corresponding roughly to the centers of the well.
3. The image is thresholded to remove particles not corresponding to strong hits and "Analyze particle" is run.
4. The measured parameter is the center of mass of the particles which gives the center of the well. These are saved in an array.
5. Circles are drawn and added to the ROI manager. The centers of the circles are the identified centers of mass of the particles and their radius is the expected radius of the wells in the original image.

This method allows to compute a threshold that preserves the moments of an image. In ImageJ/Fiji, you can access it in Image->Ajust->Threshold and choose Moments in the list. In Aphelion, the tool is in Segmentation->Threshold->AphImgMomentThreshold The original paper is 2449