A microscope can capture a defined area of a sample. This area is called Field-of-View (FOV) and depends on the optical configuration and microscope acquisition device. This is a limiting feature of microscopy. To be able to observe with a higher resolution the total visualized area is reduced. This can be an issue when trying to visualize feature that are bigger than the FOV.

One way to deal with this issue is to acquire multiple images and stitch them together after acquisition. Instead of acquiring adjacent FOV it is best to have partially overlapping regions. These regions will help to stitch images together.

While many softwares provide proprietary stitching solution we will focus here on the free and versatile plugin for ImageJ named Grid Collection Stitching.

Developed by Stephan Preibisch, this plugin is also part of FIJI distribution of ImageJ.

Stitching process

Stitching usually occurs in 3 steps:

1. The first is the "layout" which finds the adjacent images for each given image. This step approximatively place the images in relation to each other.
2. The second step finely transform (rotation, translation) one image to the adjacent ones. It matches detected features in one image to the same feature in the adjacent image
3. The last step blends the images so the results appears smooth

Layout

Three pieces of information can be used to define the layout.

1. Images metadata

Modern microscopes use motorized stages to move the sample in X and Y. These coordinates can be stored in the image metadata and used during stitching. Knowing the approximate position of each tile greatly help stitching as you just have to compute the fine matching between the different images.

2. Tiles configuration and acquisition order

if you have 25 images (Image 1, Image 2,....) and know that it comes from a 5 x 5 acquisition from the top left to the bottom righ, by row from left to right; then you can quickly place your images to their approximate positions. Sometimes the tile configuration is directly saved into the file names (Image X1Y1, Image X2Y1 etc.), this can also be used to define the approximate tile layout

3. Images themselves

The data in the image can also be used to define the layout. It requires computing power as it usually parse all possible pairwise combination and compute a correlation coefficient. It then matches images with highest correlation.

Transformation

Once the layout is defined, the images need to be finely adjusted one to another. Because microscopes are not perfect some translation and rotation can be used to finely match identified features in adjacent images. To do this images are usually acquired with a 10 to 20% overlapping region. This region will be used to finely match adjacent images.

Blending

A blending can be applied to the overlapping region to ensure a smooth tiled result.

Protocol

• Open up FIJI
• Open the Grid/Collection stitching plugin Menu Plugins>Stitching>Grid/Collection stitching

Your files are saved under Tile_x001_y001.tif, you know the grid size and the percentage overlap

• Type: Filename defined position
• Order: Defined by filename
• Click OK
• Indicate the grid size (for example 5x5 if you have 25 images)
• Under directory click Browse
• Select the folder containing your files
• Click Choose
• Under file names for tiles Tile_x{xxx}_y{yyy}.tif

Several options are available I recommend using the following:

• Add tiles as ROIs (to check tiling quality
• Compute Overlap
• Display fusion

Your files are saved under Tile_001.tif, you know the grid size, the acquisition order and the percentage overlap

• Type: Grid: Column by column
• Order: Down & Right
• Click OK
• Indicate the grid size (for example 5x5 if you have 25 images)
• Under directory click Browse
• Select the folder containing your files
• Click Choose
• Under file names for tiles Tile_{iii}.tif

Several options are available I recommend using the following:

• Add tiles as ROIs (to check tiling quality
• Compute Overlap
• Display fusion
• Subixel accuracy

Your are capturing images with a manual stage. If you read this before the acquisition I would suggest to acquire your tiles using a given size and scheme (for example 3x3 snake horizontal right). This will allow to use the process above (except Type Grid: Snake by rows). Make sure to have some overlap between images to be able to finely place them. Since you are probably reading this after your acquisition you would have file saved as Tile_001.tif... but you do not know the grid size or the acquisition order nor the percentage overlap

• Type: Unknown position
• Order: All files in Directory
• Click OK
• Under directory click Browse
• Select the folder containing your files
• Check Confirm files

Several options are available I recommend using the following:

• Add tiles as ROIs (to check tiling quality
• Ignore Z stage position
• Subpixel accuracy
• Display fusion
• Computation parameters: Save computation time

If you choose Type: Grid the plugin except sequential continuous files i i+1 etc...

If you choose Positions from File the plugin expect one single file multiseries file. To Combine several files into one single T series, open the images individually or all together Then combine them as a stack

Image>Stacks>Images to Stack

Check use tiltes as labels

then convert the Stack to a T serie

Image>Hyperstacks>Stacks to Hyperstacks

Slices (z)=1 Frames (t)=number that was in z and you have replaced by 1

In my experience proprietary softwares do not encode X and Y values in the metadata properly so this method is not often used

Type: Positions from file
Use this is you want to use the image metadata to define the tile positions. This only works if you have one single input file with all the tiles inside. In my experience this works when the file issaved under the acquisition software proprietary format.
You can also use this type of stitching if you have an additional text files defining the position of each image. You can also create this file yourself 

# Define the number of dimensions we are working on
dim = 3
# Define the image coordinates (in pixels)
img_01.tif; ; (0.0, 0.0, 0.0)
img_05.tif; ; (409.0, 0.0, 0.0)
img_10.tif; ; (0.0, 409.0, 0.0)
img_15.tif; ; (409.0, 409.0, 0.0)
img_20.tif; ; (0.0, 818.0, 0.0)
img_25.tif; ; (409.0, 818.0, 0.0)

More information

https://imagej.net/plugins/image-stitching